I spoke with Mulesoft CTO Matt McLarty about a new report that details 7 trends shaping digital transformation, from sustainability to cybersecurity.

Among the topics we discussed: 

1. Let’s talk about the report on Digital Transformation Trends in 2023. First, what’s your biggest takeaway?

2. Let’s look at some key points from the report:

• The growing prioritization of sustainability will drive the final say on IT investments and spending.
• Cybersecurity defenses will rely more on lightweight platforms to protect distributed infrastructures as more companies move to the edge.
• Real-time analytics will be used to automate decision making, thus accelerating time-to-value and eliminating wasted opportunities.

3. How is Mulesoft addressing the application integration needs of its clients?

4. The future of digital transformation? I know we’ll never be “done,” but what do you foresee as the next few chapters?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

The post Mulesoft CTO Matt McLarty on Digital Transformation appeared first on eWEEK.

C3 AI and DataRobot are two of the leading AI cloud platforms. As such, this is a close comparison. Each has an extensive set of artificial intelligence features. Which is best for your company? Let’s review the key features and compare them.

So what is an AI cloud platform? AI cloud platforms provide artificial intelligence (AI) and machine learning (ML) capabilities that are structured to simplify the process of analyzing data, finding patterns, and build models. In essence, they support anything that a data scientist wants to achieve with data analytics. The platforms also seek to broaden the scope of this technology to make it available to business analysts, software developments, enterprise architects and IT operations teams.

Forrester Research lists the key capabilities in this sector:

• Offering a broad set of tools for both data science and extended AI teams.
• Having industry-specific solution accelerators.
• Taking an extensible and interoperable approach to both tools and technologies.

Also see: What is Artificial Intelligence 

C3 AI vs. DataRobot: Key Features

The C3 AI Application Platform uses a model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise-scale AI applications. It includes C3 AI Studio, a library of deep-code tools and a low-code environment for developing, deploying, and operating AI applications.

The idea behind it is to provide a cohesive development experience for data ingestion, data modeling, machine learning feature engineering, and model lifecycle management. It incorporates metadata-driven UI development tooling. Users can explore, clean, contextualize, and label data, as well as visualize data pipelines and develop features through an integrated code experience. This is said to reduce the effort required to move data pipelines into production

C3 AI has thrown its hat into the Google Cloud ring in a big way. Most of its announcements this year are about their partnership to accelerate joint selling and co-development efforts with Google Cloud. C3 AI’s entire portfolio of enterprise AI applications is available on Google Cloud.

They are working together on a go-to-market strategy to expand pilot programs with Fortune 2000 companies, as well as jointly develop AI-driven applications using Google Cloud’s Vertex AI, ML capabilities, and data analytics services. All of C3 AI’s 42 enterprise AI applications have been optimized for Google Cloud and are available on the Google Cloud marketplace.

DataRobot AI Cloud is a machine learning platform for automating, assuring, and accelerating predictive analytics. It helps data scientists and analysts to rapidly build and deploy accurate predictive models in a fraction of the time required by other solutions. It can visually track prediction progress with charts and manage workloads and delays. This enables users to view which predictions are delayed, why they are delayed, and the time frame.

Another key DataRobot feature enables users to compare data drift across multiple features (or groups of features) as well multiple time periods. This can be done for both training and scoring data. Available in the cloud, there is also a managed version known as DataRobot Dedicated Managed AI Cloud. This dedicated hosted version of AI Cloud is managed by DataRobot experts to support AI and machine learning projects with the advantage of public cloud services. This reduces cost and time-to-value in deploying, upgrading and managing the AI infrastructure.

Who wins? C3 AI provides much of the features that are available from DataRobot. In addition, it has natural language processing (NLP) and stronger statistical and mathematical tools. C3 AI wins as a slightly broader AI cloud platform.

Also see: Top AI Software 

C3 AI vs. DataRobot: Usability and Support

C3 AI provides plenty of autocomplete and recommended suggestions to help users create models and built AI apps. Users can instantly check for errors on data model construction and function implementation with each file save. Further usability features include being able to hover over any keyword, data model element, or function to receive in-context documentation.

Users can click into references and implementation files with the code to traverse across an application and its dependencies. Additionally, they can generate test files specific to data model elements and APIs or individually run test files or group multiple tests together and run them in sequence or in parallel.

DataRobot AI Cloud, too, comes with plenty of automation bells and whistles to take the time out of mundane data science and model creation operations. It can run on any combination of public clouds, data centers, or at the edge. Users like its governance and data protection capabilities.

DataRobot AI Cloud’s single-platform approach serves data scientists, analytics experts, IT, and the business with a single view of all data from any source, any type. Users comment that it’s simple to use and adapt to your own needs. It is also said to be quick, easy to use, and effective at building models of most types.

Automation eliminates much of the coding work. DataRobot automates model setup, testing, and administration. Models can be built from any data source including tables and plain text, as well as graphic and geographical data. But there are a few negative comments, too. Sometimes the tools to decipher results are not the most user friendly, and there can be a lack of flexibility. Some say it favors modeling quantity over quality in some scenarios. And others complain that there are so many options that it can be difficult to know what to choose. One user said he wanted it to be easier to upload and integrate massive datasets.

Overall, though C3 AI offers more comprehensive support such as 24/7 live support and plenty of online options. Support from DataRobot is limited by comparison, primarily via online resources. Similarly, C3 Ai offers a wider range of documentation and training than its rival. DataRobot doesn’t yet offer live online and in person training.

In terms of usability and support, C3 AI wins.

Also see: Top Business Intelligence Software 

C3 AI vs. DataRobot: Data Scientists and Developers

Both platforms are used heavily by data scientists and developers. C3 AI Studio enables developers and data scientists to focus on solving complex business problems by providing an integrated environment that abstracts routine and complex application development tasks.

It provides technical users with core code-based experiences through extension of the Visual Studio Code source-code editor. Developers can use it to leverage out of the box auto-suggestions and autocompletion across models. Similarly, data scientists can write custom Python methods and inspect any issues in their logic using an integrated Python debugger, and QA engineers can manage test files across multiple applications.

Another C3 AI feature, on-demand JupyterLab Notebooks, provides an interactive interface to all data and machine learning services. Users can also import libraries from public or private repositories to train models and explore data, or utilize the tools and libraries in place such as JavaScript, React, and SCSS. They can then configure continuous integration (CI) pipelines, inspect the qualities of build artifacts, and deploy builds into production.

DataRobot makes it easy to use open-source modeling techniques from R, Python, Spark, H2O, VW, XGBoost, and more. Small data science teams can use it to build and deploy a great many models. Instead of having to laboriously create each one, it acts as a productivity multiplier. Its library of algorithms and pre-built prototypes is useful for feature extraction and data preparation. Automation makes it possible to select and combine multiple algorithms to produce more accurate predictive models. It incorporates data science best practices. The platform enables analysts and others to perform data scientist-like modeling tasks that previously required a high level of skill.

There is little to differentiate between these two platforms in this category, but the laser focus of DataRobot on data science gives it an advantage.

C3 AI vs. DataRobot: Analyst View

The most recent Forrester Research analysis of AI/ML platforms places C3 AI slightly ahead of DataRobot, although both are classified as Leaders. Forrester is especially enamored by C3 AI’s vision for enterprise AI, presumably influenced by its close relationship with Google Cloud. In the eyes of the analyst firm, this gives it a big edge in terms of gaining market share and enhancing its popularity.

“At a time when most vendors focus on tools for data scientists, C3 AI has always envisioned a platform approach to AI,” said Forrester analyst Mike Gualtieri. “Ahead of its time, C3 AI’s strategy is to make AI application-centric by building a growing library of industry solutions, forging deep industry partnerships, running in every cloud, and facilitating extreme reuse through common data models.”

DataRobot is also well-regarded by Forrester. The analyst firm said its customers, “appreciate the company’s rise from a niche automated machine learning (AutoML) player to a full-lifecycle AI platform.”

Strengths are listed as tooling and functionality in data preparation, model evaluation and explanation, ModelOps, and application building. It has gained ground recently by being able to augment and enrich data for modeling and adding a plug-in framework to make it easy for partners to add platform capabilities. Forrester said it has a relatively low barrier to entry for those adopting the platform and users commented on its ease of use and model documentation.

“DataRobot is a solid option for enterprises that want a platform that has tooling for extended AI teams while simultaneously providing collaboration and scale to manage existing use cases and crank out new one,” said Gualtieri of Forrester.

On Gartner Peer Reviews, though, DataRobot is well-featured and C3 AI is entirely absent. No one has reviewed it. Thus, there could be maturity issues still to be ironed out. Forrester graded DataRobot ahead of C3 AI in number of customers and company size. But otherwise, C3 AI came out ahead in inferencing, applications, data, architecture, vision, and marketing. They receive the same grade on performance.

With C3 AI slightly ahead on Forrester, but with no presence at all in Gartner Peer Reviews, this one is a tie.

Also see: Top Natural Language Processing Companies

C3 AI vs. DataRobot: Conclusion

There is a slightly different emphasis between both platforms. C3 AI is more aimed at organizations that want to build, deploy, and operate enterprise AI applications. DataRobot concentrates more on serving data scientists who are looking for a deep learning solution.

But that characterization is an oversimplification. C3 AI also does a good job looking after the needs of data scientists. And DataRobot has plenty of features to help organization build and run AI apps. But that overall emphasis does play a role in the final choice of platform.

Forrester scored them both as Leaders so either one would be a good choice. The analyst firm sees a brighter future for C3 AI. “If the company can accelerate industry partnerships for both solutions and go-to-market, it could become the de facto AI platform standard for the world’s most complex industries.”

In summary, opt for DataRobot if you want to arm data scientists with a great AI platform. But if you are looking for an AI platform with a wider base, C3 AI appears to have broader applicability.

Also see: The Future of Artificial Intelligence

The post C3 AI vs. DataRobot: Top AI CloudPlatforms appeared first on eWEEK.

This week the largest cloud computing vendor, AWS, is holding its 11^th re:Invent conference. Given its leading position in Infrastructure as a Service (IaaS), much of the news revolved around infrastructure.

Over the past few years, AWS has been offering a handful of SaaS services, one of which is its Connect contact center product, and the company announced several new capabilities to this solution.

Also see: What is Artificial Intelligence 

AWS Connect Brings AI and ML with a Pay per Use Model

Amazon Connect, which is built on the same technology that Amazon customer service agents use worldwide, utilizes artificial intelligence (AI) and machine learning (ML) to deliver a personalized customer experience (CX). It’s a single solution with a simple user interface (UI) that companies can implement across multiple channels to connect with customers.

One of the concerns regarding cloud contact centers is that they don’t scale past a few thousand agents. This has never been the case with AWS as its own contact center is almost 100,000 agents. One of the unique attributes of Connect is that it uses a true “pay per use” model. With traditional CCaaS services, customers pay $X/month/user. With Connect, a business can provisioning as many agents as they need but only pay for utilization.

According to AWS, there are hundreds of thousands of customers making more than 10 million contact center interactions every day on Amazon Connect. Some of its big-name customers include Adidas, Adobe, and National Australia Bank, all of which have successfully rolled out interactive voice response (IVR). In the contact center, IVR provides callers with a self-service option before being connected to a live agent.

It’s particularly valuable for seasonal companies where call flows tend to rise and fall. I recently talked with the Global Head of Reservations for a major hotel chain and she said the pay-per-use model saved the company a significant amount of money throughout the pandemic as travel halted. As travel picked up, the company could add to its deployment without having to guess at how many agents to provision.

Also see: Top Digital Transformation Companies

Agent Workspace Reduces Training Time 

While the unique financial model has created the opportunity for AWS, its differentiation comes in AI-based advanced features.

Last year, AWS launched Amazon Connect agent workspace, which unifies all the tools agents need in one place to manage customer inquiries, view information, resolve issues, and handle calls, chats, tasks, and cases. At re:Invent, AWS introduced step-by-step guides for Amazon Connect Agent Workspace to reduce agent training time and resolve customer issues faster. These one-click actions are based on different triggers from customers, such as their profile information, case history, or past interaction from IVR.

The triggers help provide agents with the most relevant, guided actions that they can take to resolve an issue. For instance, if a customer is calling about a lost order, the agent workspace will display the customer profile, the billing details, shipping status of the order, and actions in the step-by-step guide for replacing or refunding the item.

Also see: Top Business Intelligence Software 

Amazon Connect Flows Brings No-Code to IVRs

Supervisors can design guided experiences for agents using Amazon Connect flows, a no-code workflow tool for IVR. The guides are use-case dependent and customizable. There are relevant drag-and-drop-components that support the guides, which can be configured within Amazon Connect flows.

In addition to rolling out step-by-step guides for agent workspace, which is available now in preview, AWS improved the search and filtering function in flows as well.

“This is a great example of a capability where we heard consistent problems like inefficient apps  and long, hard training for agents—all of which finds its way to end customers,” said Ryan Braastad, senior product marketing manager for Amazon Connect. “We had the ability to test this with Amazon customer service, and working closely with them, we were inspired to make this available as part of Connect.”

Machine Learning Improves Forecasting 

The second announcement is around the simplicity and single-click functionality of Amazon Connect. Earlier this year, AWS launched Connect forecasting, capacity planning, and scheduling in preview. It allows companies to predict, allocate, and verify that they have the right number of agents scheduled at the right time to serve their needs in the contact center. This ML-powered staffing capability became generally available this week.

“The folks that do this for a living don’t have a lot of time. It’s usually a supervisor who also does the forecasting or planning or scheduling, and they want to get back to what they consider their main job as opposed to doing the administrative work,” said Tom Johnston, principal product manager for Amazon Connect.

One Amazon Connect customer, Litigation Practice Group, was running into challenges with forecasting based on the historical data the company had with its previous system. Once the law firm switched to Connect, it saw accuracy of over 95 percent for forecasts and schedules. For the firm, the simplicity, flexibility, and the high quality of data “were all main reasons why they liked using it,” said Johnston.

Lastly, AWS announced that Contact Lens for Amazon Connect will expand to include omnichannel support. AWS also added evaluation forms in Contact Lens to improve agent performance. Contact Lens is a tool that analyzes conversations between customers and agents through speech transcription, natural language processing (NLP), and sentiment analysis. While it’s been used primarily for voice, it can now analyze conversations across voice and chat channels in one place.

Also see: Top Data Mining Tools 

The post AWS Boosts AI for Connect Contact Center Product appeared first on eWEEK.

On Tuesday, December 13, at 11 AM PT, @eWEEKNews will host its monthly #eWEEKChat. The topic will be Enterprise Tech in 2023, and it will be moderated by James Maguire, eWEEK’s Editor-in-Chief.

We’ll discuss – using Twitter – the near, medium and long term future of enterprise IT. Our ultimate goal: to see the future of tech and so be equipped to prepare for it.

How to Participate: On Twitter, use the hashtag #eWEEKChat to follow/participate in the discussion. But it’s easier and more efficient to use the real-time chat room link at CrowdChat.

Instructions are on the Enterprise IT in 2023 page: Log in at the top right, use your Twitter handle to register. The chat begins promptly at 11 AM PT. The page will come alive at that time with the real-time discussion. You can join in or simply watch the discussion as it flows.

Special Guests: Enterprise IT in 2023

The list of experts for this month’s Tweetchat currently includes the following – please check back for additional expert guests:

• Steven Mih, CEO and Co-Founder, Ahana
• Chris Ehrlich, Managing Editor, Datamation
• James Maguire, Editor-in-Chief, eWeek [moderator]

Chat room real-time link: Go to the Crowdchat page. Sign in with your Twitter handle and use #eweekchat for the identifier.

Tweetchat Questions: Enterprise IT in 2023

The questions we’ll tweet about will include – check back for more/ revised questions:

1. First, let’s look at 2022: what major tech trend most shaped this year?
2. Looking ahead: what’s your most consequential prediction for tech in 2023? A change, milestone, shocking development?
3. What tech sector will be the biggest overall winner in 2023?
4. Running second to this big winner, what’s another key trend on the upswing in 2023?
5. Which tech sector will see the greatest loss of momentum in 2023?
6. What will be a big surprise in 2023 technology? What’s unexpected?
7. Will artificial intelligence live up to its deafening hype in 2023?
8. The security sector? Will ransomware attacks start to fade in 2023?
9. What about digital transformation, remote work, work from anywhere – what’s up in 2023?
10. Let’s look far beyond 2023: What trends/forces will shape enterprise tech over the next several years?

Go here for CrowdChat information.

#eWEEKchat Tentative Schedule for 2022*

July 19: Getting Started with Low Code / No Code
Aug 16: Overcoming Multicloud Challenges
Sept 20: The Future of Edge Computing
Oct 18: Optimizing Your Data Analytics Practice
Nov 15: Building Your AI Deployment
Dec 13: Enterprise Tech in 2023

*all topics subjects to change

The post eWEEK TweetChat, December 13: Enterprise Tech in 2023 appeared first on eWEEK.

Data virtualization is the process of cleansing and integrating data from various sources so it can be more readily accessed and analyzed by business users. It eliminates redundancy, standardizes formats, and makes data accessible through a single source. It integrates business intelligence, data visualization, and databases to help users query and analyze data while keeping them secure.

• What are Data Virtualization Tools
• Best Data Virtualization Tools
• SAP HANA Cloud
• Denodo
• TIBCO Data Virtualization
• Oracle Virtualization
• Red Hat jBoss Data Virtualization
• Actifio GO
• AtScale
• IBM Cloud Pak for Data
• Informatica PowerCenter
• Stone Bond Enterprise Enabler
• SAS Federation Server
• K2View Dynamic Data Virtualization
• Cdata
• Data Virtuality
• Key Feature of Data Virtualization Solutions
• Benefits of Data Virtualization Software
• Choosing the Best Data Virtulization Tool for Your Enterprise

What are Data Virtualization Tools

Data virtualization tools are used to uncover data insights using a combination of data discovery, data modeling, and virtual data integration. They provide access to information without requiring detailed knowledge of its source or structure.

Data virtualization tools simplify data integration processes using metadata to understand each data source’s structure, semantics, and usage. A data virtualization solution bridges the gap between isolated systems. It enables users with self-service capabilities to access various data sets from multiple sources within or outside an organization.

Also see: Best Data Analytics Tools 

Best Data Virtualization Tools

Many companies are looking for the best data virtualization tools to work with as they explore ways to increase their productivity and efficiency. To help them make an informed decision, we have compiled a list of the top data virtualization tools rated highly by experts and users.

These rankings were based on popularity, ease of use, and how much value each data virtualization tool provides its users. Implementing one of these technologies can reduce operational costs and improve data quality.

Also see: Top Data Visualization Tools 

SAP HANA Cloud

SAP HANA Cloud is a single database as a service (DBaaS) foundation for modern applications and analytics across all enterprise data. It’s a comprehensive, scalable and flexible in-memory database – with an elastic, on-demand pricing model that allows organizations to pay only for what they need. It also offers advanced data virtualization capabilities – Including data federation, integration and replication – to move data seamlessly among different sources and formats so users can query any data type or source without concern for underlying schema complexity.

With SAP HANA Cloud, customers can build modern applications that combine data from many sources and run them at scale without compromise. Customers can leverage big data capabilities like distributed processing and real-time event handling to create a new generation of apps that support today’s analytics, collaboration, machine learning and digital transformation needs.

Key features

• Data, federation integration and replication.
• Provides comprehensive business visibility with a real-time gateway to all your data across apps, data storage, multi-cloud, and hybrid environments.
• This tool is capable of handling OLTP and OLAP workloads for a variety of transactional and analytical databases.
• Perform commands, and automate and customize applications.
• Predict IT upgrades’ performance and cost-effectiveness and secure your IT landscape during and after changes.
• Configure, modify, and monitor database performance, and automate advanced processes.
• Stores, processes, and analyzes geospatial, graph, JSON document, and other data.

Pros

• Friendly user interface.
• Integration with other SAP products.
• Saves maintenance cost.
• Feature-rich.

Denodo

Denodo is a leading data integration, management and delivery platform that helps enterprises manage their entire data infrastructure. It offers the widest range of capabilities for accessing, handling and delivering all types of data across clouds, databases and Big Data environments without moving it from its original repositories.

It also delivers a single version of truth for analytics across all sources and governance to ensure compliance with company standards. Denodo is built for both technical and business users, including CIOs, CDOs, CDAOs, enterprise architects, developers, data scientists, and cloud architects.

Key features

• Denodo Platform can be licensed on AWS Cloud, Microsoft Azure, and Google Cloud Platform for faster deployment.
• Connects to any data source, including databases, data warehouses, cloud applications, big data repositories, and Excel files.
• Denodo is built to integrate, manage, and deliver data in complex, hybrid, multi-cloud systems while maintaining high performance, security, and governance.
• Denodo platform enables self-service BI, data science, hybrid/multi-cloud data integration, and corporate data services.
• Combine data from various sources, including structured, streaming, social, and flat files

Pros

• Consolidate data from multiple sources.
• User friendly.

TIBCO Data Virtualization

The TIBCO Data Virtualization solution delivers a complete, self-service data virtualization environment for business intelligence (BI), data warehousing and analytics. With the solution, users can consolidate all their enterprise data sources into one virtualized platform and easily build and manage new data connections to systems they need to access.

TIBCO data virtualization empowers IT departments by consolidating existing disparate toolsets so they can focus on managing how business information is accessed and used across the organization. It gives analysts immediate access to all data, which helps them develop actionable insights and act on them in real-time. Using a self-service directory of virtualized business data, users can search, select, and use BI or analytics tool to get results.

Key features

• Its web UI is a self-service data provisioning and data catalog web user interface that lets users browse for accessible data and develop and publish their views for usage in third-party applications.
• Integrated governance and security ensure the delivery of only sanctioned data, including visibility, traceability, provenance, control, authentication, authorization, and encryption.
• Developers use the graphical modeling environment to model data, design data services, implement transformations, optimize searches, and manage resources.
• Its centralized metadata management enables users to create and show detailed entity relationship diagrams and data models to meet new business needs.
• Its advanced query engines let users query, federate, abstract, and deliver data.

Pros

• Connects to multiple databases.
• Fast data query.
• Easy to use with minimal technical support.

Oracle Virtualization

Oracle Virtualization is a flexible and easy-to-use tool that can be used with various platforms. It also includes high availability, load balancing, and virtual machine management. Built for security, efficiency and performance, Oracle Virtualization provides exceptional reliability. In addition, it’s an easily customizable platform that allows for smooth scalability and large data volumes.

Key features

• Supports on-premise and cloud workload.
• Enable security updates without rebooting.
• Patches hypervisors automatically with zero downtime.

Pros

• Easy to manage
• Integration with other Oracle solutions

Red Hat jBoss Data Virtualization

Red Hat jBoss Data Virtualization enables enterprises to work more efficiently by providing data abstraction, federation, integration and transformation capabilities. It helps organizations reduce costs of development and deployment with the ability to combine data from one or multiple sources into reusable logical data models.

With JDBC (Java Database Connectivity) or ODBC (Open Database Connectivity) access, organizations can access the information with their existing applications. A rich set of web services APIs enables cross-platform connectivity to third-party systems, including other enterprise databases, NoSQL databases, SaaS, and flat files for agile data utilization.

Key features

• Offers centralized access control, and auditing.
• Simplifies data access to speed application development and integration.
• Offers read/write access to heterogeneous data storage in real-time.
• Integrate and transform data semantics per user needs.

Pros

• Highly scalable and flexible.
• Ease of use.
• Comprehensive documentation.
• Efficient support team.

Actifio GO

Actifio GO is a data management platform that provides organizations with automated, self-service provisioning and refresh of enterprise workloads. Actifio enables enterprises to stay current with rapidly evolving IT needs while safeguarding their business-critical data and ensuring regulatory compliance. Additionally, Actifio integrates with Google Cloud Storage (GCS) via its Nearline storage service for backup and disaster recovery for GCP or hybrid environments.

Key features

• Data encryption in transit and at rest.
• Multi-region disaster recovery.
• Self-service sign-up, simple deployment and central management for backup and recovery of VMs, SQL Server and SAP HANA databases inside Google Cloud and on-premises VMware VMs.
• SLA-driven data management.

Pros

• Data recovery.
• Efficient customer support.
• Multi-region disaster recovery.
• Access data instantly, even after unsubscribing.

AtScale

AtScale is an enterprise-grade data virtualization platform that enables organizations to securely and rapidly access data from various sources. The semantic layer solution leverages the robust infrastructure of cloud data platforms such as Snowflake, Databricks, Microsoft Azure, Amazon Redshift and Google BigQuery to provide end-to-end support for analytics. It provides a platform for integration, analytics acceleration and orchestration of these engines via its Semantic Layer.

The tool allows data consumers to seamlessly connect with the analytics tools they already use, like Tableau, PowerBI, Excel, Jupyter and Looker. Companies can have their organization’s complete portfolio of business intelligence (BI) tools in one place while managing all data dependencies.

Key features

• Data integration, blending and migration.
• AtScale’s Insights models and workbooks provide multidimensional Cloud OLAP analysis without data prep or engineering.
• Its virtual cube catalog enables easy, frictionless access to vast data.
• Its design canvas visually and intuitively connects to any data.

Pros

• SQL Server Analysis Services (SSAS) performance improvement.
• Efficient support team.

IBM Cloud Pak for Data

IBM CloudPak for Data is a data and AI platform that delivers on-demand insights to business leaders. It provides instant access to all your company’s data and lets you run predictive analytics. IBM CloudPak for Data offers various deployment options, including an on-premises software version built on the Red Hat OpenShift container platform or a fully managed version built on the IBM Cloud.

The tool manages data spread across distributed stores and clouds by providing an intuitive user interface that makes all of it easily accessible. IBM CloudPak for Data also integrates with other IBM products to provide end-to-end data management and security features.

Key features

• Utilize Netezza, a high-performance data warehouse, to enhance analytics.
• Prepare, cleanse, and govern your data in the same tool.
• End-to-end AI lifecycle.
• Deliver a unified experience based on metadata and active policy management.

Pros

• Intuitive user interface.
• Multi-cloud storage option.
• Feature-rich.

Informatica PowerCenter

Informatica PowerCenter is a leading enterprise-class data integration platform that enables you to integrate and transform data to accelerate business insight. It provides out-of-the-box capabilities for unstructured data, enterprise applications, cloud and SaaS applications, predictive analytics and machine learning. PowerCenter supports analytics and data warehousing, application migration, consolidation, and data governance.

Key features

• Users may use data transformation to parse JSON, PDF, XML, Microsoft Office, and IoT for non-relation data.
• The product offers tools for analytics, data warehousing, data governance, consolidation, and application migration.
• Cloud applications connectivity.
• Rapid prototyping, profiling, and validation.
• Script-free automated data validation testing.

Pros

• Maintains data quality during the ingestion and integration process.
• Supports public clouds such as Microsoft Azure and Amazon Web Services.
• Automation.
• Collaboration.

Stone Bond Enterprise Enabler

Stone Bond Enterprise Enabler is a data and application integration that delivers a set of enterprise-ready components to create and deliver high-quality business information. It integrates data virtualization, extract, transform, load (ETL), enterprise application integration (EAI), service-oriented architecture (SOA) and master data services in a single platform.

It facilitates the delivery of valuable insights from disparate sources of information for decision-making purposes. Integrating data from heterogeneous databases into one uniform presentation with metadata about the underlying structure enables easy access to data for collaboration and visualization purposes.

Key features

• Simple drag-and-drop to auto-generate virtual models in minutes or seconds for BI reports, web services, and apps.
• Automatically develop and host queryable virtual models (ODBC, JDBC, SOAP, REST, oData, ADO.Net, and BDC or BCS).
• Data masking and obfuscation.
• Consolidate all EE features, including Enterprise Enabler Studio, self-service portal, web APIs, and documentation in one console.

Pros

• Supports GDPR compliance, RLS, and CLS.
• Data lineage.

SAS Federation Server

SAS Federation Server data virtualization software provides a business-centric view of all enterprise data, empowering organizations to find, use, and deliver the information they need to make better decisions.

With SAS Federation Server, you can centralize disparate data sources within your organization’s infrastructure, simplify connections to remote locations, and securely share access to the appropriate data sources with those who have been granted access permissions. The result is an easier-to-use environment that optimizes user productivity while reducing administration costs and complexity.

Key features

• Improved performance with in-memory data caches and scheduling.
• Defines a user or group’s catalog, schema, table, column, and row access permissions.
• Apply data quality functions such as match-code generation, parsing and other tasks inside the view.
• Advanced data masking and encryption.
• SAS Federation Server accesses, manages, and shares SAS data with major relational databases such as DB2, Oracle, SAP, SQL Server, Teradata, and Greenplum.

Pros

• Ease of use.
• Interactive interface.
• Improves efficiency.

K2View Dynamic Data Virtualization

K2View is a leading data virtualization software company. Their dynamic data virtualization platform ingests data from various sources. It unifies, transforms, and enriches it to be shared across users and systems to enable more insightful decision-making. K2View’s solution for data virtualization also handles data masking tools when required, ensuring the privacy of sensitive information throughout the process.

Key features

• Enables access to live data through a logical abstraction layer.
• The tool allows you to choose which data product data will be virtualized, unified, processed, orchestrated, and delivered from source to destination.
• Implement reverse ETL.
• Compliant with data privacy regulations such as GDPR, CCPA/CPRA, LGPD, HIPAA, and PCI DSS.

Pros

• Centralized data management.
• Integration on-prem, in the cloud, or hybrid.
• High-scale data delivery.

Cdata

Cdata is a data connectivity and integration tool that offers customers a flexible approach to handling, transforming, and integrating large sets of diverse data sources. It can connect to various data sources, including cloud and SaaS applications, data warehousing, management, governance, AI, ML, BI and analytics and other enterprise application systems.

The solution integrates data from multiple systems and formats into a centralized repository where business users can leverage it, analytic models and advanced processes at both the transactional and deep insights levels.

Key features

• Cdata drivers, adapters, and cloud connectivity solutions provide real-time data to Tableau, Power BI, Excel, Sisense QuickSight, DataStudio, and Spotfire.
• Enterprise-level ETL may bring together the information stored in any large database or data warehouse so that it can be accessed whenever necessary by users, including analysts, data scientists, developers, AI, and decision-makers.
• Cdata offers visual and secure application and data integration solutions based on an open architecture.

Pros

• Easy to implement.
• Supports multiple databases.
• Automatically and continuously integrate data into Google BigQuery, Snowflake, Microsoft SQL Server, and Amazon Redshift.

Data Virtuality

Data Virtuality is a scalable data virtualization platform that combines data virtualization and ETL to provide data professionals with a data integration tool that enables them to extract any data from their legacy systems, transform it into a format suitable for use in modern applications, and distribute it across their environment. The tool offers self-service interfaces for business users and developers who need data connectivity without the involvement of IT experts.

Key features

• Agile data management.
• Offers over 200 data connectors.
• Use metadata repositories to improve master data management.

Pros

• Self-service capabilities.
• Supports several use cases, including data governance, data quality checks, self-service BI, and data preparation.
• Flexible ETL/ELT model building.

Also see: Top Cloud Companies

Key Features of Data Virtualization Solutions

There are several features to consider when shopping for a data virtualization tool. They include:

Fully integrated

Data virtualization solutions integrate the data and processes of all aspects of your organization – from master data management to business intelligence. This means that information is always current, accurate, and seamlessly accessible across the enterprise.

Comprehensive

A data virtualization solution provides a single point for accessing all data types, including transactional, analytic and structured/unstructured data sources. The ability to query by either metadata or content makes it easy to access just the suitable dataset without performing complex searches through multiple databases.

Centralized deployment of rules

Different departments within an organization may have different views on how data should be organized, processed and accessed. Data virtualization software gives you central control over these decisions so organizations can take advantage of data no matter where it resides in its raw form.

Business users don’t need training in specific programming languages; they can create custom data models, views, queries and reports with drag-and-drop simplicity. The fact that there is one version of the truth for all team members removes potential disagreements over data definitions and opportunities to work around IT rules by sending inquiries directly to individual databases.

Access controls

Data virtualization software offers flexible security options for controlling who has access to data. It also provides role-based controls (such as access privileges based on roles) to give users only the rights they require to perform their job functions.

Zero latency

Most data virtualization solutions offer zero latency querying, even when combining datasets stored in disparate systems. Allowing interactive, ad hoc exploration and analysis of datasets as they are being loaded dramatically reduces query timeframes and frees up scarce IT resources for other projects.

Easily consistent

Data virtualization software keeps creating, distributing and maintaining master data consistent and error-free because it supports integration between corporate applications such as CRM, ERP and OLAP tools.

Data transformation

With data transformation, you can quickly and automatically convert your data into the format required by any downstream system. This eliminates the need to manually reformat data whenever you want to use it in a different system.

Multidimensional analysis

Data virtualization software enables this analysis, which involves examining multiple perspectives on a set of data simultaneously. This allows analysts to explore and analyze trends and relationships in ways that would not otherwise be possible. As part of this capability, data virtualization also stores subsets of data from each dimension, allowing them to be combined and analyzed.

Also see: Real Time Data Management Trends

Benefits of Data Virtualization Software

Data virtualization software offers several benefits that can help companies improve their data management and analysis. This includes easily accessing data from all sources, which is often difficult or impossible in the absence of data virtualization software.

It also provides an environment where different data types can be analyzed and queried relatively easily and simplifies storing, managing and accessing this information.

Data virtualization solutions can also eliminate many time-consuming tasks associated with maintaining complex databases and spreadsheets by centralizing disparate datasets. These features, combined with other benefits like mobility and scalability, make data virtualization software an invaluable asset for any company looking to simplify its processes.

This enables businesses to make better decisions by accessing more accurate and complete information. Other benefits include:

• Ability to generate reports faster as the process does not require the same manual intervention as with traditional technologies.
• Improve decision-making processes by merging disparate datasets.
• It allows companies to meet compliance requirements for data reporting across organizational units or jurisdictions.
• Flexible enough to adapt as new hardware or software becomes available.

Choosing the Right Data Virtualization Tool for your Enterprise

There are different types of data virtualization tools available, but not all tools are created equal.

Choosing the right data virtualization tool depends on understanding how much flexibility is required by your company’s business practices; however, there are some general guidelines to follow when shopping around for this type of solution.

• Make sure that any data virtualization software can work with other critical enterprise applications such as ERP or CRM.
• Choose a provider that offers multiple access levels to ensure everyone has the appropriate level for their job function.
• Make sure you’re getting value for money because it can get expensive quickly when deploying large amounts of technology across an organization.
• Be sure to check out customer reviews before making any final decisions. These reviews can help determine whether the product is worth the cost.
• Find out about training and support options offered by each vendor before buying.
• Ensure that training is included in the package price and that you’ll have 24/7 access to support staff after purchase. It’s also helpful if vendors provide templates or plug-ins that make it easier for new users to use the data virtualization tool without having extensive technical knowledge.

Finding the perfect data virtualization software for your enterprise can be challenging with so many factors involved. However, considering these five features can go a long way toward finding success in the data virtualization sector.

The post Top Data Virtualization Tools appeared first on eWEEK.

Recently, Kyndryl executives, including CEO Martin Schroeter, hosted a briefing with industry analysts to discuss the company’s progress during its first year as an autonomous entity. Let’s consider what they shared.

Also see: Top Cloud Companies

Kyndryl Then and Now

When it was officially separated from IBM on November 4, 2021, Kyndryl began its independent life with more than $2B in cash on its books, 4,000+ enterprise customers in 60+ countries and annual revenues of nearly $19B.

Though Kyndryl’s annual revenues are roughly 3X higher than its closest infrastructure services competitor, the company’s leadership recognized and are intent on evolving and revitalizing its business model and focus areas. Those efforts took center stage after the spinoff and during the company’s recent analyst call.

In his introductory remarks, Martin Schroeter noted that Kyndryl’s “Services are not discretionary. They are vital to helping our customers run the systems of the world. The infrastructure services we deliver for our customers are the backbone for how billions of people live their lives, how they communicate, how they get their food, how they move their money, how they manage their health, how they travel, and of course, how they do business.”

He also described the departure from IBM as breaking “out from being a captive business unit inside of a product-oriented company.” Its central goals now are “Refining our systems, our tools and our processes to unwind the complexity we inherited … to build a people-centered culture that will serve as our foundation for our future progress.”

Schroeter outlined specific steps the company is taking to achieve those ends:

• To invest in our business for growth.
• To participate in the ecosystems that matter most to our customers.
• To focus on our existing customers who already trust us with their mission-critical work.
• To expand our wallet share.
• To move decision-making closest to where the data and the relationships are.
• To show up differently with a framework for creating a culture that is restless and empathetic and devoted to customer success.

The bulk of the webinar was devoted to expanding on this outline.

Also see: Why Cloud Means Cloud Native

The Three A’s

On the company’s economic situation, Schroeter said Kyndryl’s plan for returning to profitable growth underscores three areas:

Alliances

Establishing significant new strategic alliances and expanding existing partnerships have been central to the company’s efforts in its first year. To date, Kyndryl has formed agreements with 20+ partners, including cloud-focused partnerships with Microsoft, AWS, Google Cloud and Oracle, enterprise-oriented alliances with Red Hat, Cloudera, NetApp and Nokia and expanded partnerships with Dell and SAP.

Some of these deals have also resulted in new Kyndryl service offerings that would have been, at best, difficult to imagine when the company was part of IBM. The new Mainframe Data Pipe to Azure is a good example since it provides customers a pathway to connect their mainframe and midrange data with Azure cloud, assisted by Kyndryl staff who are deeply knowledgeable about mainframe hardware and software.

Similarly, Kyndryl supports Google Cloud’s Dual Run which enables parallel processing so customers can create digital copies of their mainframe systems, then run them simultaneously on Google Cloud without negatively impacting core business processes or end user experience. These services may seem counterintuitive since Kyndryl maintains deep ties with IBM, including serving thousands of shared customers. Safe to say that it speaks loudly to the company’s unfettered independence.

Advanced Delivery

During the past year, Kyndryl has launched new, homegrown solutions and services attuned to modern and emerging business needs. These include: Kyndryl Bridge, an open integration platform that uses data-driven insights and services expertise to enhance the connections between technology and business processes to deliver improved outcomes with a unified customer experience.

Kyndryl Vital is a collaborative co-creation experience in which the company’s experts and partners work side-by-side with customer development teams to assess problems and design effective solutions. Finally, Kyndryl Consult works with Kyndryl Bridge, Kyndryl Vital and other solutions to help customers achieve digital transformation by aligning outcomes with the technologies that support them, adopt and deploy new solutions and processes, and evolve those offerings over time.

Accounts

Along with new alliances and offerings, engaging new customers is obviously top of mind for any new company. Kyndryl has been remarkably successful in this regard, inking contracts with global enterprise customers, including Michelin, CaixaBank, BMW, Raytheon, Bangalore International Airport, CarreFour Belgium, Spain Ministry of Defense, Ricoh, Honda, Yamaha and Singapore Airlines. This growing list attests to the depth and breadth of Kyndryl’s expertise and market reach.

Also see: Top Edge Companies 

Customer profile: Laurentian Bank

Following the strategic overview and discussion, Kyndryl’s President of Strategic Markets, Xerxes Cooper, interviewed the Laurentian Bank’s EVP and CIO, Beel Yaqub, about the assistance Kyndryl provided during the company’s cloud transformation.

According to Yaqub, the bank believes cloud is vital to its future for several reasons, including enhancing its agility and speed. “Part of our strategy is building out a decoupled architecture which allows us to separate our back-end systems from front-end systems where we build strong new capabilities for our clients.”

Yaqub also noted the importance of cloud native applications and capabilities. “Recently, we launched our API platform, which will be a huge enabler of this effort. A key pillar of our strategy is partnership. Instead of building tools and solutions in-house, we’re partnering with fintechs and other organizations to develop best-in-class capabilities. Cloud is the foundation for that effort.”

Describing Laurentian Bank’s cloud transformation experience, Yaqub said, “We’ve been able to do it with remarkable speed. With Kyndryl’s help, we were able to actually implement our multi cloud strategy with both AWS and Microsoft in under seven months. The fact that we’ve been able to deliver so much value in such a short period of time is truly amazing.”

Final Analysis

What were my takeaways from Kyndryl’s first anniversary briefing? At one level, the company’s presentation followed a familiar corporate script. After an overview of existing assets and strategies by CEO Martin Schroeter, members of his leadership team and an enthusiastic customer, Laurentian Bank, added depth and color to the discussion.

However, while Schroeter and his team members stayed on track, they offered few if any of the bromides and homilies common in similar presentations. Instead, they laid out the case for Kyndryl’s strategy clearly and in substantial depth. An objective appraisal of the company’s progress during its first year as an independent entity would conclude that Kyndryl’s leadership understands its needs and opportunities and is moving apace to address the former while pursuing the latter.

The company’s core focal points, including its pursuit of new strategic alliances and deepening of longstanding partnerships, are particularly well-considered. So are launching new, future-focused offerings, like Kyndryl Bridge and Kyndryl Vital. Finally, the nearly thirty new customers that Kyndryl signed contracts with during the past year is a promising start.

This is not to say that Kyndryl’s road ahead will be bump-free or easy. Like other vendors in managed infrastructure services, many of its traditional practices and areas of expertise are either shrinking or under pressure. In essence, the company needs to assess, design, deploy and achieve a transformation similar to those it is helping other enterprises pursue.

Fortunately for its employees, customers and shareholders, Kyndryl has the leadership, strategy, assets and experience to make that transformation a success.

The post Kyndryl at One: A Year of Transformation appeared first on eWEEK.

I spoke with Workato CEO Vijay Tella about a new report, the State of Business Technology 2022; we also looked at the future of automation.

Among the topics we discussed:

What’s your single biggest takeaway from the report?

Okay, let’s discuss some key findings from the report:

• 57% of IT teams are under pressure to reduce SaaS spend. I’m surprised – I thought companies loved SaaS. So SaaS has gone too far?
• 58% of leaders are focused on using automation to drive efficiency. What’s been slowing down the move toward automation?
• 82% of teams reported they are facing higher expectations. So you continue to see a labor shortage in tech?
• How is Workato addressing the automation needs of its clients?

To wrap up: What’s the future of automation in the workplace? What are some key milestones we can expect in the years ahead?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

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DataRobot and H2O.ai are two popular AI cloud platforms. But which solution is best for your business?

First, some background. AI cloud platforms provide artificial intelligence (AI) and machine learning (ML) capabilities that are packaged to simplify the process of analyzing data: finding patterns, building models, performing simulations and what-if scenarios. The platforms also seek to broaden the scope of this technology to make it available to business analysts, software developments, enterprise architects and IT operations teams.

AI/ML platform vendors are offering platform capabilities and tools for many roles within an enterprise so that teams can develop, operationalize, and manage a growing portfolio of AI solutions,” said Mike Gualtieri, an analyst at Forrester Research.

With that background established, let’s explore these two leading AI cloud platforms, DataRobot and H2O.ai. We’ll review the key features and compare them to help your business select the optimum choice for its needs.

Also see: What is Artificial Intelligence 

DataRobot vs. H2O.ai: Key Features

The H2O AI Cloud is designed to solve complex business problems via automated machine learning (autoML) capabilities that are said to transform how AI is created and consumed. It seeks to democratize AI while continuing to provide data scientists with the features they need.

The platform encompasses the data science lifecycle, optimizing workflows to increase the quantity and quality of projects delivered to business stakeholders. Features are included for machine learning interpretability, and operations. It is used by data scientists, developers, machine learning engineers, analysts, and IT Professionals.

H2O.ai provides high performance computing with Nvidia Rapids integration and can scale workloads with support for network acceleration of Ampere-based Nvidia GPUs and the latest CUDA runtime. Users can monitor system usage and provide metrics for resource monitoring and autoscaling of multi-node clusters through a platform performance API. And its AI AppStore helps business users find and access custom AI applications.

DataRobot AI Cloud is a machine learning platform for automating, assuring, and accelerating predictive analytics. It helps data scientists and analysts to rapidly build and deploy accurate predictive models in a fraction of the time required by other solutions. It can visually track prediction progress with charts and manage workloads and delays. This enables users to view which predictions are delayed, why they are delayed, and the time frame.

Another feature enables users to compare data drift across multiple features (or groups of features) as well multiple time periods. This can be done for both training and scoring data. Available in the cloud, there is also a managed version known as DataRobot Dedicated Managed AI Cloud. This dedicated hosted version of AI Cloud is managed by DataRobot experts to support AI and machine learning projects with the advantage of public cloud services, reducing cost and time-to-value in deploying, upgrading and managing the AI infrastructure.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning

DataRobot vs. H2O.ai: Usability and Support

H2O.ai includes several features aimed at usability. It can automatically visualize and address data quality issues that transform your data into an optimal modeling dataset. It can visually display statistical properties within datasets and expose unexpected data quality issues like outliers, correlations, or missing values. Visual and text descriptions automatically help detect trends and insights including topics in text, correlations, and outliers.

Users, too, seem to appreciate H2O.ai’s no code approach that accelerates the process of building complex ML models. This is said to make it possible to create detailed models in minutes. It is well integrated with Amazon S3, Microsoft SQL Server database and other tools to provides a decent level of security, according to users. On the downside, users say it is still playing catchup with more established vendors like DataRobot and lacks some capabilities. One user said it was hard to modify data once uploaded and that cross-user access is limited. Others commented on a learning curve for deployment and dealing with expert settings aimed at data scientists. Customer support and documentation are also works in progress.

DataRobot AI Cloud comes with plenty of automation bells and whistles to speed up mundane data science and model creation operations. It can run on any combination of public clouds, data centers, or at the edge. Users like its governance and data protection capabilities.

DataRobot AI Cloud’s single-platform approach serves data scientists, analytics experts, IT, and the business with a single view of all data from any source, any type. Users comment that it is simple to use and adapt to your own needs. It is also said to be quick and effective at building models of most types.

Automation eliminates much of the coding work. It automates model setup, testing, and administration. Models can be built from any data source including tables and plain text, as well as graphic and geographical data. But there are a few negative comments, too. Sometimes the tools to decipher results are not the most user friendly, and there can be a lack of flexibility. Some say it favors modelling quantity over quality at times. And others complain that there are so many options that it can be difficult to know what to choose. One user said he wanted it to be easier to upload and integrate massive datasets.

In terms of usability and support, there is little to differentiate the two solutions. H2O.ai gets the nod due to a slightly higher ease of use rating from Gartner Peer Reviews.

DataRobot vs. H2O.ai: Data Scientists and Developers

Both platforms are used by data scientists. H20.ai has advanced autoML capabilities that encompass the entire data science lifecycle, optimizing workflows to increase both the quantity and quality of data science projects delivered to business stakeholders.

It contains robust features for machine learning interpretability and operations, and offers control over the entire explainability pipeline. For developers, H2O.ai automatically converts deployment artifacts from Python/R to Java/C++ to simplify the handoff of projects between data science teams and developers. It takes an open-source, low-code approach to development with enough flexibility to work with all major libraries and frameworks. Model monitoring features support for both H2O.ai and third-party models. They are accessible through a visual interface or API.

DataRobot also makes it easy to use open-source modeling techniques from R, Python, Spark, H2O, VW, XGBoost, and more. Small data science teams can use it to build and deploy a great many models. Instead of having to laboriously create each one, it acts as a productivity multiplier. Its library of algorithms and pre-built prototypes are useful for feature extraction and data preparation. Automation makes it possible to select and combine multiple algorithms to produce more accurate predictive models. It incorporates data science best practices. The platform enables analysts and others to perform data modeling tasks that previously required a high level of skill.

There is little to choose between both platforms in this category.

Also see: The Future of Artificial Intelligence

DataRobot vs. H2O.ai: Analyst View

The most recent Forrester Research analysis of AI/ML platforms places DataRobot well ahead of Leader H2O.ai. Forrester named DataRobot as one of three companies to earn a Leader designation. The analyst firm said its customers “appreciate the company’s rise from a niche automated machine learning (AutoML) player to a full-lifecycle AI platform.”

Strengths are listed as tooling and functionality in data preparation, model evaluation and explanation, ModelOps, and application building. It has gained ground recently by being able to augment and enrich data for modelling and adding a plug-in framework to make it easy for partners to add platform capabilities. Forrester said it has a relatively low barrier to entry for those adopting the platform and users commented on its ease of use and model documentation.

“DataRobot is a solid option for enterprises that want a platform that has tooling for extended AI teams while simultaneously providing collaboration and scale to manage existing use cases and crank out new one,” said Forrester’s Gualtieri.

H2O.ai earned a Contender rating from Forrester. The analyst firm liked its approach to ML but said it needed more of an AI platform focus. Its open-source ML algorithms leverage distributed computing architectures and automation that data scientists can use. But it lacks tooling for extended AI teams that would make the platform more attractive to technology partners. “H2O.ai has strengths in training, inferencing, and ModelOps. AI teams using Driverless AI can be significantly more productive than straight code-first data science teams,” said Gualtieri. “Areas of improvement include data management, security, and pluggable architecture for partners.”

On Gartner Peer Reviews, DataRobot also came out ahead. Users scored DataRobot higher on data exploration, visualization data preparation, model management, and collaboration. H2O.ai scored higher on forecasting, optimization, simulation, and user experience.

DataRobot wins in this category.

DataRobot vs. H20.ai: Conclusion

Pricing of both platforms is far from open. But the consensus is that DataRobot works out less expensive overall. Analyst ratings, user comments, and feature set appear to favor DataRobot over H2O.ai.

But H2O.ai is rising fast. It is steadily adding features as it scales from an ML algorithm specialist into becoming more of a full-featured AI platform. DataRobot has already made that journey so remains ahead. But for how long?

Also see: Top Data Mining Tools 

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The proliferation and importance of data in our daily lives is driving the need for digital trust. On the enterprise side, the increase in connected devices, a greater threat surface and concerns over cybersecurity are the main reasons why organizations are looking for ways to safeguard their online business processes and interactions.

To better understand how organizations perceive digital trust and how far they’ve come in adopting it, DigiCert and Eleven Market Research surveyed 400 enterprises and 400 consumers around the world in September 2022. The respondents consisted of IT, information security, and DevOps senior and C-level managers from enterprises with 1,000 or more employees. The survey findings were published in the 2022 State of Digital Trust report, which was released by DigiCert this month.

Also see: Digital Transformation Guide: Definition, Types & Strategy

Four Pillars of Digital Trust

Before diving into the findings, it’s important to understand the foundation of digital trust. Digital trust consists of four key building blocks:

• Standards that define trust.
• Compliance and operations that establish trust and verify the digital certificates or instruments of that trust.
• Trust management to ensure companies are properly handling certificate life cycles.
• Connected trust for more complex supply chains.

“Digital trust matters. All enterprises (100% of the respondents) view digital trust as important,” said Diana Jovin, vice president of product marketing at DigiCert. “Interestingly, with consumers it’s also important (68%) but not to the same degree. But, both groups suggest that if you broke that chain of trust, they would either switch business partners or stop doing business with a company that violated the trust.”

Trust is Core to Customer Retention

According to the data, 99% of enterprises believe their customers would switch to a competitor if they lost trust in the company. In fact, about two-thirds have switched vendors after losing trust in that vendor. Nearly half (47%) of the consumers surveyed said they stopped doing business with a company that lost their trust in the past, while 84% would consider switching in the future.

Digital trust is just as important for employees (100%) as it is for enterprises. The majority would consider switching vendors if they lost trust, with approximately half (51%) saying switching would be “likely.”

Not surprisingly, the industry where digital trust ranks the highest in importance is financial services, followed by tech companies, retail, and manufacturing. Both financial services firms and their employees value digital trust due to the sensitive nature of their data.

Also see: Top Digital Transformation Companies

Data, Security, and Customer Expectations Raise the Bar on Digital Trust

The top driver of digital trust is the growing importance of data. Every person in the world will consume around 20,000 gigabytes (GB) by 2025 — that’s 174 zettabytes (ZB) in total data. While the amount of data is significant, the importance of the data is even more valuable, whether it’s personally identifiable information, medical records, or personal photos.

The second driver is the increasing threat surface as enterprises move to hybrid work environments. With remote sites, multiple clouds, and connected devices, enterprise networks have become more vulnerable to cyberattacks. As a result, the number of bad actors like hackers, cyber criminals, hacktivists, and inside actors grows each year — another reason why having digital trust is crucial.

Customer expectations have gotten greater. As mentioned above, people are quick to switch to a competitor if they lose trust in a company. Although all companies value customer loyalty, one of the top concerns for consumers is security because more than half have experienced cyberattacks. The most common one is hacked accounts (26%), followed by data breaches (20%), bank and credit account theft (18%), account takeover (14%), hacked devices (13%), and identity theft (12%).

With so much at stake, enterprises are optimizing digital trust to protect their customers and employees. A typical organization started its digital trust journey two to three years ago and has completed 75% (or more) of the journey so far, the report found.

However, the top challenge cited by IT has been managing digital certificates. Regulatory compliance, the complexity of securing multi-vendor networks, and the lack of staff expertise were cited as the other leading challenges.

There are several digital-trust initiatives enterprises have implemented to address these challenges. The main one is device identity and operations security, now fully implemented by 74% of enterprises. Zero-trust policies have been fully implemented by 58% of enterprises and certificate life cycle management by more than half of enterprises (55%).

Across the board, top-tier companies are doing much better in driving digital trust outcomes, said Jovin. That’s because the top tier is four-and-a-half times as likely to believe a loss of customer trust will lead to losing the customer. These companies are also more likely to believe digital trust affects their brand, sales, and margin. Furthermore, they’re about five times as likely to switch partners if they lost trust in them.

The top-tier companies are also further along in their digital trust journey and are likely to complete that journey much earlier than the bottom tier. Unlike the bottom tier, the top tier is more concerned about cyber threats. These companies take cyber threats seriously and are up to three times as likely to be engaged with important cybersecurity safeguards.

Also see: Top Data Visualization Tools 

It’s Time to Establish a Digital Trust Office

DigiCert recommends organizations establish a digital trust office and appoint a leader who can be in charge of key initiatives. It’s a new concept, and those in IT architect positions most often take on the role of a digital trust officer. However, it will be essential going forward to create this stand-alone position within IT since it combines systems and data.

“When we look at the findings, we see digital trust attached to outcomes in a meaningful way. It can affect top-line revenue, customer loyalty, and customer engagement,” said Jovin. “Our advice to organizations is to think about digital trust as a strategic imperative that supports the business, not just an amorphous security concept.”

Also see: The Successful CISO: How to Build Stakeholder Trust

The post DigiCert Report Highlights Importance of Digital Trust appeared first on eWEEK.

Natural language processing (NLP) is a branch of artificial intelligence (AI) that focuses on computers incorporating speech and text in a manner similar to humans. This area of computer science relies on computational linguistics—typically based on statistical and mathematical methods—that model human language.

NLP plays an increasingly prominent role in computing—and in the everyday lives of humans. Smart assistants such as Apple’s Siri, Amazon’s Alexa and Microsoft’s Cortana are examples of systems that use NLP. In addition, various other tools rely on natural language processing. Among them: navigation systems in automobiles; speech-to-text transcription systems such as Otter and Rev; chatbots; and voice recognition systems used for customer support. In fact, NLP appears in a rapidly expanding universe of applications, tools, systems and technologies.

In every instance, the goal is to simplify the interface between humans and machines. In many cases, the ability to speak to a system or have it recognize written input is the simplest and most straightforward way to accomplish a task. While computers cannot “understand” language the same way humans do, natural language technologies are increasingly adept at recognizing the context and meaning of phrases and words and transforming them into appropriate responses—and actions.

Also see: Top Natural Language Processing Companies

Natural Language Processing: A Brief History

The idea of machines understanding human speech extends back to early science fiction novels. However, the field of natural language processing began to take shape in the 1950s, after computing pioneer Alan Turing published an article titled “Computing Machinery and Intelligence.” It introduced the Turing Test, which provided a basic way to gauge a computer’s natural language abilities.

During the ensuing decade, researchers experimented with computers translating novels and other documents across spoken languages, though the process was extremely slow and prone to errors. In the 1960s, MIT professor Joseph Weizenbaum developed ELIZA, which mimicked human speech patterns remarkably well. Over the next quarter century, the field continued to evolve. As computing systems became more powerful in the 1990s, researchers began to achieve notable advances using statistical modeling methods.

Dictation and language translation software began to mature in the 1990s. However, early systems required training, they were slow, cumbersome to use and prone to errors. It wasn’t until the introduction of supervised and unsupervised machine learning in the early 2000s, and then the introduction of neural nets around 2010, that the field began to advance in a significant way.

With these developments, deep learning systems were able to digest massive volumes of text and other data and process it using far more advanced language modeling methods. The resulting algorithms had become far more accurate and utilitarian.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning

How Does Natural Language Processing Work?

Early NLP systems relied on hard coded rules, dictionary lookups and statistical methods to do their work. They frequently supported basic decision-tree models. Eventually, machine learning automated tasks while improving results. Today’s natural language processing frameworks use far more advanced—and precise—language modeling techniques. Most of these methods rely on convolutional neural networks (CNNs) to study language patterns and develop probability-based outcomes.

For example, a method called word vectors applies complex mathematical models to weight and relate words, phrases and constructs. Another method called Recognizing Textual Entailment (RTE), classifies relationships of words and sentences through the lens of entailment, contradiction, or neutrality. For instance, the premise “a dog has paws” entails that “dogs have legs” but contradicts “dogs have wings” while remaining neutral to “all dogs are happy.”

A key part of NLP is word embedding. It refers to establishing numerical weightings for words in specific context. The process is necessary because many words and phrases can mean different things in different meanings or contexts (go to a club, belong to a club or swing a club). Words can also be pronounced the same way but mean different things (through, threw or witch, which). There’s also a need to understand idiomatic phrases that do not make sense literally, such as “You are the apple of my eye” or “it doesn’t cut the mustard.”

Today’s models are trained on enormous volumes of language data—in some cases several hundred gigabytes of books, magazines articles, websites, technical manuals, emails, song lyrics, stage plays, scripts and publicly available sources such as Wikipedia. As the deep learning system parse through millions or even billions of combinations—relying on hundreds of thousands of CPU or GPU cores—they analyze patterns, connect the dots and learn semantic properties of words and phrases.

It’s also often necessary to refine natural language processing systems for specific tasks, such as a chatbot or a smart speaker. But even after this takes place, a natural language processing system may not always work as billed. Even the best NLPs make errors, they can encounter problems when people misspell or mispronounce words and they sometimes misunderstand intent and translate phrases incorrectly. In some cases, these errors can be glaring—or even catastrophic.

Today, prominent natural language models are available under licensing models. These include the OpenAI codex, LaMDA by Google, IBM Watson and software development tools such as CodeWhisperer and CoPilot. In addition, some organizations build their own proprietary models.

Also see: Best Machine Learning Platforms 

How is Natural Language Processing Used?

There are a growing array of uses for natural language processing. These include:

Conversational AI. The ability of computers to recognize words introduces a variety of applications and tools. Personal assistants like Siri, Alexa and Microsoft Cortana are prominent examples of conversational AI. They allow humans to make a call from a mobile phone while driving or switch lights on or off in a smart home. Increasingly, these systems understand intent and act accordingly. For example, chatbots can respond to human voice or text input with responses that seem as if they came from another person. What’s more, these systems use machine learning to constantly improve.

Machine translation. There’s a growing use of NLP for machine translation tasks. These include language translations that replace words in one language for another (English to Spanish or French to Japanese, for example). Google Translate and DeepL are examples of this technology. But machine translation can also take other forms. For example, NLP can convert spoken words—either in the form of a recording or live dictation—into subtitles on a TV show or a transcript from a Zoom or Microsoft Teams meeting. Yet, while these systems are increasingly accurate and valuable, they continue to generate some errors.

Sentiment analysis. NLP has the ability to parse through unstructured data—social media analysis is a prime example—extract common word and phrasing patterns and transform this data into a guidepost for how social media and online conversations are trending. This capability is also valuable for understanding product reviews, the effectiveness of advertising campaigns, how people are reacting to news and other events, and various other purposes. Sentiment analysis finds things that might otherwise evade human detection.

Content analysis. Another use case for NLP is making sense of complex systems. For example, the technology can digest huge volumes of text data and research databases and create summaries or abstracts that relate to the most pertinent and salient content. Similarly, content analysis can be used for cybersecurity, including spam detection. These systems can reduce or eliminate the need for manual human involvement.

Text and image generation. A rapidly emerging part of natural language processing focuses on text, image and even music generation. Already, some news organizations produce short articles using natural language processing. Meanwhile, OpenAI has developed a tool that generates text and computer code through a natural language interface. Another OpenAI tool, dubbed Dall-E-2 creates high quality images through an NLP interface. Type the words “black cat under a stairway” and an image appears. GitHub Copilot and Amazon CodeWhisperer can auto-complete and auto-generate computer code through natural language.

Also see: Top AI Software 

NLP Businesses Use Cases

The use of NLP is increasingly common in the business world. Among the top use cases:

• Chatbots and voice interaction systems. Retailers, health care providers and others increasingly rely on chatbots to interact with customers, answer basic questions and route customers to other online resources. These systems can also connect a customer to a live agent, when necessary. Voice systems allow customers to verbally say what they need rather than push buttons on the phone.
• As organizations shift to virtual meetings on Zoom and Microsoft Teams, there’s often a need for a transcript of the conversation. Services such as Otter and Rev deliver highly accurate transcripts—and they’re often able to understand foreign accents better than humans. In addition, journalists, attorneys, medical professionals and others require transcripts of audio recordings. NLP can deliver results from dictation and recordings within seconds or minutes.
• International translation. NLP has revolutionized interactions between businesses in different countries. While the need for translators hasn’t disappeared, it’s now easy to convert documents from one language to another. This has simplified interactions and business processes for global companies while simplifying global trade.
• Scoring systems. Natural language is used by financial institutions, insurance companies and others to extract elements and analyze documents, data, claims and other text-based resources. The same technology can also aid in fraud detection, financial auding, resume evaluations and spam detection. In fact, the latter represents a type of supervised machine learning that connects to NLP.
• Market intelligence and sentiment analysis. Marketers and others increasingly rely on NLP to deliver market intelligence and sentiment trends. Semantic engines scrape content from blogs, news sites, social media sources and other sites in order to detect trends, attitudes and actual behaviors. Similarly, NLP can help organizations understand website behavior, such as search terms that identify common problems and how people use an e-commerce site. This data can lead to design and usability changes.
• Software development. A growing trend is the use of natural language for software coding. Low-code and no-code environments can transform spoken and written requests into actual lines of software code. Systems such as Amazon’s CodeWhisperer and GitHub’s CoPilot include predictive capabilities that autofill code in much the same way that Google Mail predicts what a person will type next. They also can pull information from an integrated development environment (IDE) and produce several lines of code at a time.
• Text and image generation. The OpenAI codex can generate entire documents, based a basic request. This makes it possible to generate poems, articles and other text. Open AI’s DALL-E 2 generates photorealistic images and art through natural language input. This can aid designers, artists and others

Also see: Best Data Analytics Tools 

What Ethical Concerns Exist for NLP?

Concerns about natural language processing are heavily centered on the accuracy of models and ensuring that bias doesn’t occur. Many of these deep learning algorithms are so-called “black boxes,” meaning that there’s no way to understand how the underlying model works and whether it is free of biases that could affect critical decisions about lending, healthcare and more.

There is also debate about whether these systems are “sentient.” The question of whether AI can actually think and feel like a human has been expressed in films such as 2001: A Space Odyssey and Star Wars. It also reappeared in 2022, when former Google data scientist Blake Lemoine published human-to-machine discussions with LaMDA. Lemoine claimed that the system had gained sentience. However, numerous linguistics experts and computer scientists countered that a silicon-based system cannot think and feel the way humans do. It merely parrots language in a highly convincing way.

In fact, researchers who have experimented with NLP systems have been able to generate egregious and obvious errors by inputting certain words and phrases. Getting to 100% accuracy in NLP is nearly impossible because of the nearly infinite number of word and conceptual combinations in any given language.

Another issue is ownership of content—especially when copyrighted material is fed into the deep learning model. Because many of these systems are built from publicly available sources scraped from the Internet, questions can arise about who actually owns the model or material, or whether contributors should be compensated. This has so far resulted in a handful of lawsuits along with broader ethical questions about how models should be developed and trained.

Also see: Top Business Intelligence Software 

What Role Will NLP Play in the Future?

There’s no question that natural language processing will play a prominent role in future business and personal interactions. Personal assistants, chatbots and other tools will continue to advance. This will likely translate into systems that understand more complex language patterns and deliver automated but accurate technical support or instructions for assembling or repairing a product.

NLP will also lead to more advanced analysis of medical data. For example, a doctor might input patient symptoms and a database using NLP would cross-check them with the latest medical literature. Or a consumer might visit a travel site and say where she wants to go on vacation and what she wants to do. The site would then deliver highly customized suggestions and recommendations, based on data from past trips and saved preferences.

For now, business leaders should follow the natural language processing space—and continue to explore how the technology can improve products, tools, systems and services. The ability for humans to interact with machines on their own terms simplifies many tasks. It also adds value to business relationships.

Also see: Top Data Visualization Tools 

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The vast amount of data that companies manage comes in many forms, including structured and unstructured. To be effective, enterprises must be able to view all of their data in one place, in real time. They must also be able to translate the data to glean actionable insights from it. Data virtualization is the solution.

Data virtualization is a method of data management that refers to the compiling of all enterprise data in one place, regardless of its source. This is completed via a virtual data layer that aggregates all data across disparate systems.

Also see: Understanding Database Virtualization: 5 Key Points

How Does Data Virtualization Work?

The process of data virtualization is quite simple. Data is accessed in its original form and source. Unlike typical “extract, transform, and load” (ETL) processes, virtualization doesn’t require data to be moved to a data warehouse or data lake first.

Data is aggregated in a single location, known as a virtual data layer. Using this layer, enterprises can develop simple, holistic, and customizable views (also known as dashboards) for accessing and making sense of data. 

Using these tools, users can also pull real-time reports, manipulate data, and perform advanced data processes such as predictive maintenance. Data is easily accessible via dashboards from anywhere.

The virtualization process is most commonly completed using data virtualization software. Many platforms exist, including those from industry leaders such as IBM, Oracle, and TIBCO.

The Benefits of Data Virtualization

The benefits of data virtualization for growing enterprises are many. Data virtualization enables IT teams to gain access to real-time data regardless of source to improve decision-making, increase productivity, and reduce overall costs.

Combines Structured and Unstructured Data

The data held in various sources such as relational and non-relational databases will be both structured and unstructured. While structured data is typically numbers and other values, unstructured data is more complex and comes in the form of video files, IoT sensor data, and other examples.

Analyzing unstructured data can be a challenge. However, it’s an incredible source of insights.

The best way forward is to combine structured and unstructured data, a process that is made simple through data virtualization processes and software.

Eliminates Data Replication

Enterprises require real-time access to up-to-date data at all times. Unfortunately, traditional processes such as ETL require data to be replicated whenever updated data is requested. Data replication is expensive as it requires ever-growing levels of storage. It also results in duplicate and incorrect data that can skew datasets.

Data virtualization doesn’t require replication. Instead, data is kept in its original source yet viewed in a virtual layer. This means virtualization can result in higher-quality data faster and at a lower cost.

Improves Decision-Making

While data is critical to the decision-making process, not just any data will do. The data used must be accurate, up-to-date, and logical. It must also be displayed in a way that all stakeholders can understand, whether a user is a data scientist or a C-level executive.

Data virtualization enables stakeholders to access the specific data they need when they need it. Because data isn’t just a replication from any given time, all data is accurate to the minute. This results in full business visibility that enables stakeholders to pivot quickly with confidence.

When integrated with data visualization tools, virtualization software allows users to see real-time data in an easy-to-understand form. For example, data can be displayed in charts or graphs.

See also: Best Data Visualization Tools in 2022

Enhances Productivity

Data virtualization enhances productivity in many ways. First, virtualization improves data access. There’s no need for users to access multiple applications or servers—all data is collected into a single resource. Users can get in, grab the data they need, and get back to work.

Data virtualization also simplifies the data analysis process. Virtualization software offers an easy-to-use interface that even those unfamiliar with data analysis can use. Stakeholders can quickly access the data they need to make quick business decisions. Plus, these self-service capabilities will reduce the workload for IT and data teams.

The enhanced productivity resulting from data virtualization enables enterprises to move quickly. Processes such as product development can also become more efficient. As a result, enterprises gain a competitive advantage within their industries.

Reduces Infrastructure Costs

Data virtualization requires fewer resources than traditional data integration methods. For example, due to the elimination of replication, enterprises can reduce the amount of data storage they must pay for. Virtualization also results in fewer data sources to manage, which saves teams time and effort.

Simplifies Data Security and Governance

Data security and governance strategies ensure data is protected and used appropriately. Unfortunately, as enterprises grow, data and its sources become increasingly complex. Security and governance quickly become impossible to manage.

Data virtualization simplifies data governance by delivering a single source of truth. All data sources are integrated into one, enabling IT teams to enforce centralized data security and governance policies. In addition, data virtualization platforms include features such as access control and the ability to integrate with other data security tools.

How Is Data Virtualization Used Today?

The increase in data volume and the need for real-time access are resulting in serious growth within the data virtualization market. Recent data shows that the global market is expected to reach $22.2 billion by 2031, growing at a CAGR of 21.7%.

Many enterprises across industries are utilizing data virtualization to improve data access, reduce costs, and boost productivity.

• In retail, data virtualization enables enterprises to view everything from inventory levels to customer behavior across stores in one centralized location. 
• In manufacturing, data virtualization enables users to quickly pinpoint areas of improvement in production, resulting in improved manufacturing yield.
• In healthcare, data virtualization enables stakeholders to access real-time patient data across sources to provide the highest quality care.
• In telecommunications, data virtualization enables enterprises to use accessible data to quickly resolve support tickets and improve the customer experience.

The Challenges of Data Virtualization

While it’s incredibly beneficial across industries, data virtualization does come with its own set of challenges. For example, just like any other digital transformation project, an upfront investment is required. Enterprises must invest in data virtualization software, staff training, and other components.

In addition to the investment, other challenges exist:

• Virtualization is complex: Modernizing a data infrastructure isn’t easy. And data virtualization isn’t as simple as purchasing and implementing software. In many cases, data virtualization tools must be implemented in conjunction with other tools such as ETL. The virtualization process can quickly get complex.

• Specific expertise is required: The virtualization of the data and its future management requires specific expertise that some enterprises may not have current access to. Companies may need to hire professionals specifically for virtualization or look for third-party assistance, which can drive up costs.

• Data retrieval depends on source uptime: As data is gathered directly from the source in real time, data retrieval requires the source to be up and running when a query is made. If the source (such as a database or software) is experiencing downtime, data retrieval won’t occur.

Data Virtualization: Related Terms

The topic of data virtualization includes a wide range of terms, some of which are used interchangeably. To gain a solid understanding of data virtualization and management, it’s important to be clear on these terms. Some common, data-specific terms enterprises must be familiar with include:

• Data management: Data management is the process of collecting, storing, and managing data. Data virtualization is a process included within the practice of data management.

• Data consolidation: Data consolidation is the process of combining data from various sources into one. For example, all data may be consolidated into a data warehouse or lake. While data consolidation is similar to virtualization, consolidation is missing the virtual data layer that enables access to real-time data.

• Data warehouse: A data warehouse is a large set of data gathered from various sources. Data inside the warehouse is structured and processed, ready for analytics.

• Data lake: A data lake is a simple repository of data. Unlike the data warehouse, data inside the data lake is unstructured and raw.

See also: Top Data Analytics Tools & Software for 2022

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Environmental threats, ranging from the rise of extreme weather events to global climate change, do not impact all people or groups equally. The populations at greatest risk are those in developing countries that have fewer resources to devote to large scale programs.

While many enterprises have embraced efforts to reduce the environmental impact of doing business, including employing renewable energy sources and using recyclable materials in products and packaging, fewer are focusing on larger challenges and global programs.

At the recent 2022 United Nations Climate Change Conference (COP27) in Sharm El Sheikh, Egypt, IBM announced that five organizations focused on accelerating clean energy projects will become members of its global pro bono social impact program, the IBM Sustainability Accelerator. Let’s consider IBM’s efforts and the company’s sustainability goals.

IBM’s Sustainability Accelerator

What exactly is the IBM Sustainability Accelerator? Launched in February 2022, it is a social impact program designed to help at-risk communities address multiple global environmental threats, including climate change, pollution and extreme weather.

I wrote about the company’s effort shortly after IBM announced that the three initial members of the Sustainability Accelerator (focusing on sustainable agriculture) had successfully completed the first phase of the program in December 2021. In essence, the IBM Sustainability Accelerator follows a three-step process over a two-year period:

1. Define – Annually, IBM defines a theme for the RFP (request for proposal) process and project selection from nonprofit and governmental organizations.
2. Determine – Engagements kick off with Phase One discussions at IBM Garage facilities which apply IBM design thinking and agile techniques to speed innovation and project development. IBM experts work with beneficiary non-profits to identify their needs and establish a clear roadmap to determine public challenges and potential solutions.
3. Design/Deploy – In Phase Two, IBM cross-industry experts configure resources and technology designed to scale their projects and help participants meet their community and environmental impact goals. Potential technologies include IBM Watson, IBM Cloud, and the IBM Environmental Intelligence Suite. In addition, beneficiary organizations will receive monthly IBM Cloud credits, weather data credits mentorship, and access to the IBM partner ecosystem. IBM experts will also support pilot deployments of solutions to help facilitate optimal implementation, scale long-term impact and drive key societal outcomes.

During the launch event, the company also invited nonprofit and governmental organizations to apply to a public RFP for the 2022 cohort’s focus on clean energy. During last week’s COP27 event, IBM announced the five organizations chosen for the 2022 program.

Also see: Digital Transformation Guide: Definition, Types & Strategy

The Clean Energy Cohort

The selection process for 2022’s Sustainability Accelerator considered the applicants’ commitment to support communities especially vulnerable to environmental threats, their ability to increase access to affordable clean energy services, their strategic focus and transparency on measurement and reporting, and other factors.

After evaluating more than 100 submissions from around the world, IBM selected these nonprofit and governmental organizations to become the clean energy cohort of the program:

• United Nations Development Programme: UNDP is working with IBM to increase access to sustainable, affordable and reliable energy in African countries, focusing on those furthest left behind. The goal is to use UNDP’s technical knowledge and IBM’s artificial intelligence and geospatial analytics technologies to forecast electricity access and improve policy and investment decisions.

• Sustainable Energy for All: IBM is working with Sustainable Energy for All to build an intelligent model to project energy needs based on current and future human activity. This model will be designed to help to address key development challenges (e.g., lack of energy access and poor healthcare) and support the development of robust infrastructure planning, such as electrification plans. IBM machine learning and IBM Cloud technology and expertise will be used to build and scale open-access datasets and an energy needs model using temporal and spatial data. Kenya and India are being explored as the first regions to pilot the model.

• Net Zero Atlantic: Net Zero Atlantic is collaborating with IBM to create an interactive digital tool that will geospatially display environmental and socioeconomic impacts of possible energy system futures for Nova Scotia. Ultimately, the goal is for Indigenous communities in Nova Scotia to leverage advanced modeling capabilities to inform their input into energy and development planning. The tool aims to be easy-to-use, locally relevant and time efficient. It will be designed to produce easy-to-understand visual results using IBM technology such as the IBM Environmental Intelligence Suite and IBM Cloud.

• Miyakojima City Government: The Miyakojima City Government is working with IBM to support the development of a renewable energy strategy, including a microgrid on Miyakojima Island, a distant community facing severe climate issues due to typhoons in Japan. The goal is to help their residents, who rely on a clean local environment for the tourist industry and agriculture. Miyakojima City Government and IBM will collaborate to leverage technologies such as IBM Environmental Intelligence Suite weather data and IBM Cloud to model electricity demand and advise energy infrastructure development.

• Environment Without Borders Foundation: Environment Without Borders Foundation’s collaboration with IBM will be aimed to develop a platform to forecast, track, and communicate clean energy usage options in Egypt. The goal is to enable resilient and sustainable infrastructure and operations for clean energy in Egypt, helping residents of remote villages for whom energy is currently both expensive and unreliable. The clean energy management platform will leverage IBM Environmental Intelligence Suite and IBM Cloud.

Also see: Top AI Software 

Final Analysis

At one level, it is no surprise that IBM is focusing substantial time, attention and resources on efforts like the Sustainability Accelerator. The company has a long history of developing successful solutions for often seemingly intractable challenges.

Those solutions include advanced analytics technologies, hybrid cloud services and AI-based offerings. Some of these are specifically designed to help organizations successfully plan and manage the impact of severe weather and climate change events, like IBM’s Environmental Intelligence Suite, which will be used in many Sustainability Accelerator projects.

It is worth noting that since IBM operates in over 175 countries and serves clients and communities in virtually every corner of the world, “thinking globally” is deeply embedded in its DNA. Additionally, the company understands “acting locally”—addressing often massively complex, widespread problems and challenges one client or market at a time.

Finally, IBM is highly adept at applying lessons learned and new innovations across broader applications and use cases. The key is to maintain hope, learn everything you can, act with a sense of mission and find or build, and then use the required tools to get the job done. The IBM Sustainability Accelerator is just such a tool.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning

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I spoke with George Tubin, Director of Product Strategy at Cynet, about why XDR has so many definitions – and why this emerging security technology has great potential.

Among the topics we discussed: 

• Let’s talk about XDR in the cybersecurity market. What is it, and why is it a growing solution?
• As you survey the XDR market, what forces are driving the market?
• How is Cynet addressing the security needs of its clients?
• The future of XDR and cybersecurity? What are some significant developments we can expect in the years ahead?

The post Cynet’s George Tubin on XDR Cybersecurity appeared first on eWEEK.

I spoke with Mike Micucci, COO at Automation Anywhere, about the “automation economy” and the trends that are driving the AI/automation sector.

Among the topics we discussed:

• As you survey the AI and automation market, what are a couple of major trends driving the sector?
• What is the “Automation Economy”?
• How is Automation Anywhere addressing the AI/automation needs of its clients?
• The future of automation and AI? What are some key milestones we can expect in the years ahead?

The post Automation Anywhere’s Mike Micucci on Automation and AI appeared first on eWEEK.

I spoke with Pavel Pragin, CEO at ClearScale, who provided guidance about how to optimize the migration and management of AWS cloud deployments.

Among the topics we discussed: 

• Okay, let’s talk problems and solutions. What are a couple of typical problems with AWS migration, and how do you recommend that companies solve them?
• What about AWS management in general? How do you advice solving the typical pain points?
• How is ClearScale addressing the AWS needs of its clients? What’s the ClearScale advantage?
• The future of AWS migration and management? What major changes do you expect?

Listen to the podcast:

Also available on Apple Podcasts

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Automation is changing the world as we know it. As technology advances and companies grow, the use of automation increases constantly. As a result, companies are increasingly seeking to automate their day-to-day processes to save time, money, and resources.

• • What is Automation?
  • Best Automation Companies
  • Honeywell
  • Siemens Automation
  • ABB
  • OMRON Industrial Automation
  • Mitsubishi Electric
  • Rockwell
  • Emerson Process Management
  • Fortra’s Automate
  • Yokagawa Electric
  • Schneider Electric
  • Benefits of Automation Companies
  • How to Choose the Best Automation Provider
  • History of Automation

Also see: Top Business Intelligence Software 

What is Automation?

Automation is the process by which machines perform tasks without human involvement. This is achieved through robots and other automated devices or computer software. Companies automate processes to increase efficiency and productivity as well as reduce errors.

The four most common types of automation include:

Fixed automation

This type of automation provides a predetermined response to predetermined input values. For example, an assembly line might be set up so that every time one product part is inserted at one end, it travels down the assembly line automatically until it comes out the other side with all of its pieces assembled.

Programmable automation

This automation allows users to set rules for decisions based on input values. So, instead of having a single preset response to input values, programmable automation lets you set different responses depending on other inputs.

Flexible automation

This type of automation combines the best features of both fixed and programmable automation. It allows you to use preset options and enable changes to them like with programmable automation.

Integrated automation

Integrated automation is when software and hardware are seamlessly integrated to produce an automated system that can perform various tasks. These systems rely on predefined data, rules, and procedures to complete functions without human intervention or time delay.

Also see: Top AI Software 

10 Best Automation Companies

Automation companies provide ways to balance human creativity with machine-driven efficiency. With the help of automation, humans can do more in less time. The best automation companies offer various services, such as industrial equipment, environmental engineering, and marketing for worldwide businesses.

Honeywell

Honeywell International Inc. is a Fortune 100 company that offers industrial and consumer products, technologies, and services. Mark Honeywell founded the company in 1906 in Wabash, Indiana, to manufacture and market his invention, the mercury seal generator.

The company operates four main business groups, including aerospace, home and building technologies (HBT), safety and productivity solutions (SPS), and performance materials and technologies (PMT).

Honeywell provides various automation solutions across its business groups, such as Automatic Identification System (AIS) and RF Receivers for aerospace; access systems; biometric readers; building operation experts (BOE); intrusion detection systems; heating, ventilating, and air conditioning control systems for residential and commercial buildings; safety systems for homes, schools, businesses, plants, and public spaces. It also provides specialty materials with innovative properties used to create engineered products from advanced materials found in everything from aircraft engines to home appliances.

Siemens Automation

Siemens is an industrial company that provides automation products and services. With a global workforce of more than 303,000 employees worldwide, and offices in over 100 countries, Siemens is one of the largest engineering companies in the world. Many industries use their products, including power generation and energy production, healthcare and medical engineering, automotive manufacturing, process industry (like petrochemicals or chemicals), aerospace, transportation systems including trains, and vehicles.

Siemens offers several automation products, including industrial automation systems (SIMATIC), motion control systems (SIMOTION), CNC automation systems (SINUMERIK), and process control systems like SIMATIC PCS 7 and SIMATIC PCS neo.

Totally Integrated Automation (TIA) is Siemens’ industrial automation tool that integrates hardware, software, and services to provide customers with a complete digital enterprise solution. In addition, TIA leverages connectivity and cloud technologies to make automation solutions available on demand.

Siemens’ portfolio includes solutions for factory operations optimization, predictive maintenance, quality assurance, smart buildings and city management, project management support tools (PMST), service operations excellence, plus other solutions tailored to specific customer needs. It also offers a 3D simulation environment called Simcenter, which allows users to perform design, simulation, test, and data management.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning

ABB

ABB is one of the world’s leading automation technology providers, products, and services. With a presence in over 100 countries, ABB employs around 105,000 people. ABB has four main divisions: electrification, power grids division, robotics division, and process automation division.

ABB process automation and digitalization solutions make processes more efficient and productive by automating typically time-consuming and labor-intensive tasks. It offers controllers for pneumatic valves and actuators, programmable logic controllers (PLCs), servo amplifiers, and DC drive systems as well as Fieldbus protocols such as Profibus, Ethernet/IP, or DeviceNet.

ABB robotics division develops, manufactures, and markets robots for heavy manufacturing to light assembly. Their product portfolio includes industrial robots, collaborative robots, autonomous mobile robots, controllers, equipment and accessories, OmniVance application cells, functional modules, and machine automation solutions.

In addition to robots themselves, the robotics division also provides their customers with application software, connected services, robot control mate, robotstudio, and wizard easy programming tools. The company has production facilities worldwide, including Finland, Italy, the U.S., and India.

OMRON Industrial Automation

OMRON Industrial Automation is a leading global manufacturer of industrial automation equipment. Headquartered in Kyoto, Japan, it has approximately 150 offices in 40 countries worldwide and employs over 30,000 people. It is one of the world’s largest PLCs and SCADA (supervisory control and data acquisition) software producers.

The company offers comprehensive systems solutions for semiconductors, medical, food, beverage, oil and gas, pulp, and paper. Its product range includes programmable logic controllers, servo motors, sensors, switches, robot arms and grippers, and machine automation controllers.

Their products are used in various fields, from manufacturing to robotics to heavy industry. They employ many technologies like wireless communications, artificial intelligence, and big data for their product development.

Mitsubishi Electric

Mitsubishi Electric is a Japanese multinational electronics and electrical equipment company founded in Tokyo, Japan, in 1921. The company’s major focus is manufacturing electronic parts such as semiconductors, electric and hybrid cars, electric trains, aircraft parts, and household appliances.

Mitsubishi Electric employs more than 100,000 people worldwide. It provides automation systems for power plants; robotics for factories and warehouses; air-conditioning units for buildings; and I&C systems for monitoring, protecting, and controlling nuclear power plants.

Mitsubishi Electric factory automation tools include industrial computer MELIPC, human-machine interfaces (HMIs)-GOT, SCADA, industrial/collaborative robots-MELFA, programmable controllers MELSEC, simple application controllers, motion controllers, computerized numerical controllers(CNCs), and data logging analyzer-MELQIC.

Also see: Best Machine Learning Platforms 

Rockwell Automation

Rockwell Automation, Inc. is a global automation company headquartered in Milwaukee, Wisconsin. The Fortune 500 company was founded in 1903 by Lynde Bradley and Stanton Allen as the Compression Rheostat Company and changed its name to Allen-Bradley in 1909. It later merged with Rockwell in 1985 and changed its name to Rockwell International.

Rockwell Automation’s products include programmable logic controllers (PLCs), motion control solutions, power systems, and software. This makes it one of the best companies for industrial automation because it provides systems that can be used in various industries such as energy, food and beverage, and pharmaceuticals.

Many devices are available on the company’s website, including PlantPAx distributed control systems, actuators, positioners, and conveyors. These products can be utilized in various ways, and Rockwell also offers complete solutions through its design service.

Emerson Process Management

Emerson Process Management is an engineering and consulting services company focusing on industrial automation, process control, and power delivery. The business offers a wide range of products and services to help clients improve their production processes.

Founded in 1890, the Fortune 500 company’s headquarters are located in St. Louis, Missouri. The company operates through two segments: automation solutions and commercial & residential solutions.

Emerson Process Management also provides solutions for smart buildings, energy management systems, industrial motors and drives, specialty chemicals plants, food processing plants, pharmaceutical plants, refineries, and petrochemical plants. In addition, the company has recently seen growth in sectors such as oil and gas, healthcare, and water treatment.

In addition to designing automated equipment for clients across various industries, Emerson Process Management provides automation and control software solutions such as SCADA software, data acquisition, human-machine interface software, data historian software (DeltaV), and configuration tools.

Its current product portfolio includes components like distributed slice I/O systems, programmable logic controllers, actuators and valves, variable frequency drives (VFD), energy meters, sensing and protection devices, motor controls and relays, and transformers.

Also see: Data Analytics Trends 

Fortra’s Automate

Fortra’s Automate is the leading automation company specializing in building and designing custom automated programs for various industries. Formerly known as HelpSystems, LLC, the company was founded in 1982 to develop solutions to automate complex business processes.

In 2022, it rebranded as Fortra to create a new image for its cybersecurity and automation solutions. Fortra currently employs more than 3,000 people worldwide, with offices in 18 countries.

Fortra’s Automate can automate business processes in various industries, including IT, banking, health, accounts payable, human resources, and user provisioning.

Yokogawa Electric

Yokogawa Electric is a Japanese multinational company headquartered in Tokyo, Japan. It was founded in 1915 by Tamisuke Yokogawa and was incorporated in 1920 as Yokogawa Electric Works Ltd.

This company offers services and solutions for process control technology, industrial automation, and energy management. It has many well-known products, such as PLC, distributed control system, SCADA, and HMI.

Yokogawa works with various industries, including oil and gas, LNG supply chain, chemical, power, pharmaceutical, and food and beverage.

Schneider Electric

Schneider Electric is a French multinational corporation headquartered in Rueil-Malmaison, France. The company operates in the energy and automation markets. Schneider Electric provides various industrial automation and control products for field, factory, and office use to improve customer operations’ reliability, efficiency, and profitability.

Schneider Electric’s offerings include telemetry and remote SCADA systems, PLC and PAC-dedicated controller, motor starters, motion control, robotics, and HMIs. In addition, Schneider Electric’s EcoStruxure product line provides edge control and apps, analytics, and services to homes, buildings, data centers, infrastructure, and industries.

Also see: Top Data Visualization Tools 

Benefits of Automation Companies

Automation is a vital part of business development in the current day and age. Automation has become ubiquitous, with many industries adopting it for various purposes. For example:

• The finance industry often uses automation to reduce human error when calculating complex mathematical formulas.
• Retailers use it to manage inventory and perform data analysis for future marketing campaigns.
• The healthcare sector employs automation to keep up with demand from aging populations and new technologies that require constant monitoring.
• Manufacturing companies had used automation since the 1980s when robot arms started replacing assembly line workers on their production lines.
• AI has also become an essential component of automation, especially in customer service jobs. Chatbots can answer common questions more efficiently than humans, freeing time for customer service representatives to focus on more complicated problems.

Automation companies come in all shapes and sizes, but they share two things in common: They make life easier for people through reduced labor needs and improved efficiency, and they offer a wide range of services related to specific industries.

A good automation company will know what its customers want, how to give it to them efficiently, and how it can provide more benefits than competitors. It also should have excellent technical capabilities, so customers can rely on them for maintenance and support over the long term.

How to Choose the Best Automation Provider

When it comes to automation, various considerations come into play. From the specific tasks that need to be automated to the level of investment you’re willing to make, many factors exist to consider when choosing an automation company.

Industry experience

When determining which automation company is right for you, you must ensure they have experience with your industry. The best way to do this is by asking for references from people in your industry or looking at the portfolio of their completed projects. If they don’t have any testimonials or past work in your field, you may want to move on and find another company with more relevant experience.

Range of services and pricing

Before deciding which automation company to hire, take some time to explore what they offer and how much they each cost. Some companies provide one service, while others offer many solutions. Make sure you research each type of service they provide before deciding on a particular company, so you can efficiently compare prices and offerings.

Dedicated team

Working with an automation company with dedicated teams ensures every project gets the attention it deserves. Dedicated teams also allow for faster turnarounds, so if speed is vital, then this factor could be essential to consider.

Customer satisfaction rate

You should always look at customer satisfaction rates when researching different automation companies. Reviews are usually available online and give valuable insight into how well a company performs. Another way to assess the quality of a company is by looking at the number of times they’ve been awarded contracts and accolades. Doing your due diligence upfront can help you avoid future problems and costly mistakes.

Company certification

Look for certifications such as ISO 9001 (Quality Management) and ISO 14001 (Environmental Management). These certifications speak to a company’s commitment to providing high-quality products and sustainable practices. Choosing a company with these certifications is wise because they demonstrate good intentions in providing green solutions with little to no environmental impact.

Also see: Best Data Analytics Tools 

History of Automation

Automation has been developing for a surprisingly long time. In the late 1800s, factories used primitive automation to replace human labor and make production more efficient. Automation has come a long way from steam to internal combustion engines.

The invention of automated flour mills in 1785 by Oliver Evans helped push this movement even further, as they could produce continuously without any human intervention. Then, in the 1950s, Joseph F. Engelberger co-founded Unimation, an American company that developed and manufactured robotic machines.

The first machine it built was the Unimate, which could automatically perform tasks like picking up and moving parts on a production line. George Devol invented the first-ever industrial robot arm with six degrees of freedom. His company, Unimation, would license its technology to major U.S. industries, including General Motors.

After that, automation grew into new industries like manufacturing, retailing, transportation, and communication. Today, robots are everywhere, from automobile manufacturing plants to hospitals performing procedures such as brain surgery or installing computer chips into electronic devices. Automation has advanced from solely mechanical and electrical systems to incorporating robotics and artificial intelligence.

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Machine learning (ML) is being incorporated into virtually all aspects of enterprise IT. ML speeds up data analytics, facilitates real-time data processing and decision making, and greatly enhances modeling. Microsoft Azure ML and Databricks both offer top-rated ML tools. But which is best for your company? 

As usual, there are similarities and differences. In many cases, the choice boils down to the specific ML needs of the environment.

Also see: Best Machine Learning Platforms 

Azure ML vs. Databricks: Key Features

Azure Machine Learning is designed to help data scientists and developers quickly build, deploy, and manage models via machine learning operations (MLOps), open-source interoperability, and integrated tools. It streamlines the deployment and management of thousands of models in multiple environments for batch and real-time predictions.

Repeatable pipelines can be used to automate workflows for continuous integration and continuous delivery (CI/CD). Developers can use cross-workspace collaboration using registries. It also offers continuous monitoring of model performance metrics and the detection of data drift, and it can trigger retraining to improve model performance. Azure ML also has features to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis.

Like Azure ML, Databricks is cloud-based. Its management layer is built around Apache Spark’s distributed computing framework to make management of infrastructure easier. It uses a batch in-stream data processing engine for distribution across multiple nodes.

Databricks positions itself as a data lake more than a pure ML system, but it incorporates heavy duty ML capabilities. The emphasis is on use cases such as streaming, ETL, and data science-based analytics/ML. It can be used to handle raw unprocessed data in large volumes.

Databricks is delivered as software as a service (SaaS) and can run on all major cloud platforms; there is even an Azure Databricks combo available. There is a data plane as well as a control plane for back-end services that delivers instant compute. Its query engine is said to offer high performance via a caching layer. Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

The latest version has added advanced data warehousing and data governance capabilities, Databricks Marketplace and Data Cleanrooms for collaborative data sharing, data engineering optimizations to automatically execute batch and streaming data pipelines, automatic cost optimization for ETL (extract, transform, load) operations, and ML life cycle improvements.

For those needing robust ELT, data science, and machine learning features within a data lake/data warehouse framework, Databricks is the winner. For those just wanting to add ML to existing applications, Azure ML wins.

Also see: Data Mining Techniques 

Azure ML vs. Databricks: Support and Ease of Use

Azure ML enables users to collaborate with Jupyter Notebooks using built-in support for open-source frameworks and libraries. Users can create accurate and automated ML models quickly for tabular, text, and image. And those familiar with SQL and Azure will find it particularly easy to use. But in general, the platform is designed to simplify ML processes.

Databricks, on the other hand, is best for those used to Apache and open-source tools. It takes a data science approach using open-source and machine libraries, which may be challenging for some users. It can run Python, Spark Scholar, SQL, NC SQL, and other platforms, and it comes packaged with its own user interface as well as ways to connect to endpoints such as JDBC connectors. Some users, though, report that it can appear complex and is not user friendly, as it is aimed at a technical market and needs more manual input for cluster resizing clusters or configuration updates. There may be a steep learning curve for some.

There is a version that runs on Azure, but this does not seem like the ideal combination. Garter Peer Reviews score Databricks way ahead of Azure-Databricks in terms of data access and manipulation, optimization, performance, scalability, data preparation, ease of deployment, and support. In most cases, it is probably best to pick one or the other and not try to cobble them both together.

Azure ML wins in terms of overall ease of use.

Also see: Top AI Software 

Azure ML vs. Databricks: Security

Azure ML offers data protection, access control, authentication, network security, and threat protection to identify unusual access locations, SQL injection attacks, and authentication attacks.

Further security features include component isolation limits. Developers can use it in a managed and secure environment with cloud CPUs (central processing units), GPUs (graphics processing units), and supercomputing clusters while enjoying continuous monitoring with Azure Security Center.

Databricks provides role-based access control (RBAC), automatic encryption, and plenty of other security features. Both platforms do a good job of security, so there is no clear winner in this category. For Microsoft shops, Azure wins. Beyond that, it is a tie.

Azure ML vs. Databricks: Integration

Microsoft does a good job tying its various ecosystems together. Azure ML, Azure Synapse, and the rest of the Azure offerings are well integrated. That applies as well to Windows and other Microsoft offerings, including Power BI for analytics. It even does a decent job integrating Apache tools, although not as well as Databricks, which is built solidly on an Apache bedrock.

In comparison, Databricks requires some third-party tools and application programming interface (API) configurations to integrate governance and data lineage features. Databricks also supports any format of data including unstructured data, which gives it an edge in that area over Azure ML.

More recently, Databricks added open-source connectors for Go, Node.js, and Python to make it simpler to access from other applications. A Databricks SQL query federation feature offers the ability to query remote data sources including PostgreSQL, MySQL, AWS Redshift, and others without the need to first extract and load the data from the source systems.

Azure ML is the obvious winner here for Microsoft and Azure shops. Outside of that sphere, Databricks wins.

Azure ML vs. Databricks: Pricing

There is a great deal of difference in how these tools are priced. But speaking very generally, Databricks is priced at around $99 a month. There is also a free version. As storage is not included in its pricing, Databricks may work out cheaper for some users and not for others. It all depends on the way the storage is used and the frequency of use. Compute pricing for Databricks is also tiered and charged per unit of processing. That said, some users complain about how expensive it can be.

Azure ML is a little complex on pricing, too. There are different parameters included that add to cost beyond a general pay per use model. But in general, it looks like it is cheaper than Databricks overall.

Azure ML wins on price, although it isn’t possible to do a full comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and analysis requirements. For some users, Databricks may turn out cheaper, but for most, Azure ML will probably come out ahead.

Choosing Between Azure ML and Databricks

Azure ML and Databricks are both excellent ML tools. Each has pros and cons, but it all comes down to usage patterns, data volumes, workloads, and data strategies.

Azure ML is more suited for those who want to build models and crunch lots of data through an ML engine. It is also good for developers who want to build ML features into applications.

Databricks does similar things, but has ML as one component in a bigger data lake suite that includes streaming, data warehousing, and ELT. As such, it should be viewed more as a broad data platform with wider scope than Azure ML. Users store data in managed object storage of their choice. The focus, then, is on the data lake and data processing.

Databricks wins for a technical audience. Azure ML can work well for that same audience but is also designed for a less tech-savvy user base. Databricks isn’t as easy to use, is said to have a steep learning curve, and may require more maintenance. But, it can address a wider set of data workloads and languages.

The choice largely comes down to user preference and needs. Those familiar with Apache Spark will tend to gravitate toward Databricks. Those comfortable with Azure and Microsoft tools will be well suited to use Azure ML.

But, there may be occasional cases where Azure ML doesn’t provide all of the functions data scientists need, even if they are operating on Azure/Windows. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, and other languages makes it attractive to developers in those camps.

Azure wins for those that just need to augment existing infrastructure and applications with ML functionality. Databricks wins for those favoring open-source technologies and who are looking for a broader data lake/data warehouse and data management platform.

Also see: Top Data Mining Tools 

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I spoke with Sylvain Tremblay, Sr. VP of the Video AI Business at Uniphore, about how AI systems “read the room” to help sales reps and executives.

Among the topics we discussed: 

• What is Emotion AI, and what are a couple of key trends you see driving the sector?
• What can companies do to improve consumer trust in AI?
• How is Uniphore addressing the AI needs of its clients?
• The future of Emotion AI? What are some major improvements we can expect in the years ahead?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

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Phishing attacks—where hackers try to collect personal information using deceptive emails and links—continue to impact organizations of all sizes.

It’s been well documented that phishing as an attack vector has exploded over the past several years. SlashNext reported that over the first six months of 2022, there were over 255M attacks, a 61% increase in the rate of phishing attacks compared to 2021.

What is often overlooked is the total cost to a business. Such attacks require a great deal of time and energy from IT and security teams, which, on average, spend approximately 28 minutes dealing with a single phishing email at a cost of about $31 per message.

The findings come from a new report conducted by IRONSCALES and Osterman Research, surveying 252 IT and security professionals in the U.S. The report, The Business Cost of Phishing, uncovered that IT and security teams typically spend one-third of their time handling phishing threats weekly. For 70 percent of organizations, dealing with a single phishing email takes 16 to 60 minutes.

Also see: The Successful CISO: How to Build Stakeholder Trust

Dealing with Phishing: Like Finding a Needle in a Haystack

As the attack occurs, IT is looking for the message and reading the headers on the message, explained Ian Thomas, VP of product marketing at IRONSCALES. “If there’s an attachment, they’re running it through a sandbox to see if it’s malicious. Once IT realizes phishing is involved, they look for it in other mailboxes—potentially hundreds of mailboxes. When they find it, all the messages have to be pulled out. This requires a lot of investigating and takes roughly a half hour every single time.”

In addition to spending a lot of time on phishing-related activities, organizations have to pay a higher salary to every IT or security professional that handles phishing. According to the data, an organization with five IT and security professionals is currently paying $228,630 in annual salary and benefits to handle phishing. An organization with 10 IT and security professionals is paying $457,260 per year, and an organization with 25 IT and security professionals is currently paying $1,143,150 per year.

Phishing Attacks: Increasingly Challenging

Phishing is an increasingly sophisticated form of cyberattack that’s becoming more prevalent and better at evading detection. Most of the IT and security professionals surveyed in the report expect the volume of phishing attacks to increase over the next 12 months. As the attacks become more complex and damaging, organizations will spend more time and money on mitigating them.

Eighty percent of organizations feel the dynamics of phishing have worsened or remained the same over the past year. These dynamics include the number of phishing attacks, their sophistication, and their ability to bypass current detection mechanisms.

For example, hackers are using adaptive techniques or polymorphic attacks that slightly alter each phishing message, which decreases the likelihood of a message being flagged as a phishing scam.

Also see: Secure Access Service Edge: Big Benefits, Big Challenges

Targeting Vulnerable End Users

The most common way phishing attacks are carried out is by tricking an email recipient into believing that the message they received is from a trusted source, such as a bank, a friend, or a fellow employee.

Hackers can obtain account credentials from earlier phishing messages or purchase the credentials on the dark web. The messages are sent from an organization’s own email infrastructure, which means they are likely to bypass detection.

Any messaging platform where two-way communication takes place is now a target for phishing attacks, said Thomas. Social media platforms like Facebook, LinkedIn, and Instagram are the most common ones, but Webex and Zoom users are also at risk. Hackers with employee credentials can take over a legitimate account and disguise themselves as the account holder to contacts, who receive phishing links and attachments directly in these apps.

Also see: Best Website Scanners 

Social Media in the Crosshairs

The report identified a worrying trend, where phishing attacks are spreading beyond email. According to the respondents, they’re seeing phishing attacks in messaging apps like:

• WhatsApp, Telegram, and Snapchat (57%)
• Cloud-based file sharing platforms like Dropbox and Google Drive (50%)
• Text messaging services (49%)
• Social media and direct messages (44%)
• Video conferencing platforms like Zoom, Webex, and Google Meet (43%)
• Collaboration platforms like Microsoft Teams and Slack (40%)

The number of phishing attacks is only expected to grow over the next 12 months. Therefore, organizations should prepare by revamping their cybersecurity tactics to include more robust solutions that can detect and stop phishing attacks—even advanced polymorphic and nested threats—and safeguard their communication and collaboration apps, not just email.

In the report, IRONSCALES recommends creating awareness among employees through surveys and training materials, so they can identify phishing scams. Organizations can use phishing simulation and training exercises to help employees understand various phishing techniques. For organizations that have a bring your own device (BYOD) policy, it should be revised to include guidance on text-based phishing scams.

IRONSCALES also recommends using the principle of least privilege access to reduce the attack surface. Even if an employee’s account gets compromised, their access will be restricted only to their job functions and duties. Taking these important steps can help organizations educate their employees and mitigate potential attacks.

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A digital twin is essentially an avatar of a physical object in the virtual world, allowing people to operate it remotely, manage it, and monitor its performance by collecting data from embedded sensors. Digital twins technology is a core element of digital transformation. 

The concept of digital twins can be traced back to NASA’s efforts with its space exploration project in the 1960s. This technology took a step further in 2002 when Michael Grieves publicly introduced the concept and model of the digital twin at a Society of Manufacturing Engineers conference in Troy, Michigan. In 2010, NASA’s John Vickers introduced a new use of the term digital twin that was not confined to manufacturing industries but applied across all sectors, including construction, engineering, transportation, and aerospace.

Also see: What is Artificial Intelligence 

Digital Twins Definition

A digital twin is a 3D digital representation of a physical object. Digital twins are virtual replicas of physical devices, infrastructures, or buildings that can control, monitor and analyze their operation, performance, and wear and tear. These models can also be used to simulate the future state of a system before actually making any changes, so any potential outcomes can be predicted.

Digital twins allow users to make more informed decisions about when to replace or upgrade parts, reduce downtime, or even avoid it altogether by providing proactive notifications for preventative maintenance.

They have been proven helpful for maintenance and predictive analytics in fields such as utilities, transportation, healthcare, construction, retail, medicine, manufacturing, agriculture, infrastructure management, automotive, aerospace, and defense industries.

Manufacturing companies most popularly use them to make production processes more efficient by managing operations in both the virtual and real worlds. These twins work together to improve maintenance efficiency, uptime, and performance.

For example, Lockheed Martin uses digital twins to track its spacecraft, planes, helicopters, and rockets. By using sensors to collect data about these products as well as through algorithmic modeling of their behavior over time, digital twins allow for diagnostic, predictive, and prescriptive analytics in the context of operational excellence.

So instead of waiting until the machine breaks, these models can identify potential problems before they happen, saving money on unplanned repair costs, labor, downtime, and lost productivity.

Also see: The Future of Artificial Intelligence

How Do Digital Twins Work?

A digital twin is an artificial intelligence (AI)-powered 3D model of a physical asset created to provide insight into the performance of the corresponding physical object. It does this by aggregating all data about the object.

You can then analyze this data and decide what needs to be fixed or improved. Digital twins also can predict potential failure before it happens, so you can take preventative measures to avoid any issues before they happen. With a digital twin, companies can better understand the operational status of their assets and make smarter decisions based on insights from the data.

As each company is unique, there’s no one size fits all approach to creating a digital twin for them. One thing remains constant, though: The initial step is always obtaining access to as much data as possible about the physical asset. The more detailed, the better.

From there, create a 3D model of that asset to use as the basis for building the digital twin. Once the model is done, install various sensors and collect multiple data points.

If a decision needs to be made about which sensor would best suit your business, try to match its attributes with those that exist on similar models. Doing so will help ensure you don’t end up with information gaps in your digital twin.

Also see: Top Digital Transformation Companies

Types of Digital Twins

Digital twins can monitor, analyze, and predict the future behavior of an entire production system or individual asset performance. These digital models can then simulate various scenarios to make strategic decisions on how best to operate an asset. There are four types of digital twins.

Component or parts twins

Component twins or parts twins refer to the digital representation of individual parts of a system or machine. It’s the basic form of digital twins and simulates the smallest example of a functioning component to maintain data about its condition, use, and performance.

Asset twins

Asset twins or product twins are a virtual simulation of the interoperability of two or more individual components. For example, a data facility has many pieces that all need to be working for the facility to function optimally.

A digital twin could allow you to simulate the interactions between these different components and see if one part is disrupting the functionality of another within this simulated environment.

System or unit twins

System twins, also known as unit twins, are a virtual representation of multiple systems assets’ interoperability. These twins consist of data that relates to the whole system or product and its individual components.

With system twinning technology, you can keep tabs on how your products behave over time. This allows engineers to detect anomalies in the way these products interact with each other and make any necessary adjustments.

Process twins

Process twins are digital representations of systems working together to create an entire production facility. They are not limited to physical objects; they help map out what goes on in the business world and provide data that can be used to predict potential problems before they happen.

Process twins represent different parts of a business, including machines, workflows and employees. Process twins allow companies to proactively identify risks and problem areas, leading to increased efficiency throughout all levels of the company. They help businesses identify bottlenecks, increase throughput, and cut costs.

Also see: Data Analytics Trends 

Benefits of Digital Twins

The benefits of digital twins can be immense for any business. They allow for more efficient operations, faster response times, better demand forecasting, more cost-effective maintenance, rapid innovation, improved safety, and increased employee collaboration.

Even a small company can take advantage of the benefits that come from digital twins. However, it is essential to recognize that not every industry will have the exact needs for digital twins as other industries might have.

• Digital twins enable companies to optimize their operations on a granular level.
• It helps businesses be more efficient in optimizing the performance of their assets, resources, and personnel performance.
• They also help organizations save money by predicting problems before they happen.
• They give managers insight into critical KPIs that have historically been difficult to measure or monitor because there was no way for an organization to know what its equipment is doing 24/7.
• It also offers a visualization of real-time data, which means you can see events unfold as they happen and respond accordingly.
• It provides employees access to operational data, so they know what’s happening at any given time, leading to better communication among employees about potential issues and opportunities.

Use Cases and Examples of Digital Twins

Manufacturing

Digital twins have been incredibly beneficial to manufacturing companies using 3D printing for decades. The best part is that the process is almost entirely automated with the help of artificial intelligence (AI).

In manufacturing, a digital twin provides a virtual representation of the modeled real-world objects or machines. If the AI-powered system detects an issue, it will automatically notify an engineer about what’s happening, so they can fix it before anything goes wrong.

Healthcare

Digital twins in the healthcare sector monitor the usage and maintenance of medical equipment like oxygen tanks, x-ray scanners, CT scanners, MRI machines, and dialysis equipment. Digital twins also help predict future equipment requirements based on the load on current equipment.

For example, suppose the digital twin detects that a piece of equipment has started malfunctioning due to aging. In that case, it will signal engineers accordingly, and they would switch over to another device while repairing the malfunctioned one.

Supply chain

The supply chain industry could use digital twins to gain insight into its logistics system. A digital twin can be a virtual map of warehouses, ports, and distribution centers. Virtual mapping lets organizations know where products are at any time in the supply chain, so they can make more informed decisions.

These include knowing which warehouse is the most efficient to ship from, determining which order should come first when processing shipments, ensuring safety standards are met during transportation, and identifying risks along the way.

Building  and construction

Construction sites across the globe are equipped with smart devices that measure everything from temperature, humidity, lighting levels, and air pressure. With data generated by these devices collated together in a digital twin simulation environment, builders now have access to comprehensive information about building designs and materials long before construction starts.

Retail

Digital twins are used in retail for customer modeling and simulations to improve customer experience. Customer data collected through digital twins gives retailers valuable information on shoppers’ habits and enables brands to create tailored marketing campaigns.

The Future of Digital Twins

In the future, digital twins will become commonplace in all industries. Enterprises will use them to make smarter decisions, reduce risk, improve efficiency, and provide consumers with a better experience.

According to Allied Market Research, the global digital twin industry is valued at approximately $6.5 billion annually, and it’s projected to reach $125.7 billion by 2030, growing at a compound annual growth rate (CAGR) of 39.48% from 2022 to 2030. This data reflects the immense potential of this technology as more companies leverage its capabilities.

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