The communications industry’s largest show, Enterprise Connect, is being held this week in Orlando, Fl. At the event, Amazon Web Services (AWS) announced its new Amazon Chime SDK call analytics, which are a set of capabilities that simplify and lower the cost of recording and generating insights into real time audio calls.

The service works with any SIP or WebRTC system, so customers can use their favorite UC or CC solution and still take advantage of the Chime SDK.

Also see: Top Cloud Service Providers and Companies

Building Communications Into Applications

Amazon Chime SDKs are a set of real-time communications capabilities for developers to embed voice, video, messaging and other functions into applications. Chime came to Amazon via the acquisition of Biba as a UC application but AWS has shifted it away from a pre-built client to the SDKs. Given how crowded the market is today, this is the right move as it addresses what I believe are the future needs of communications.

Knowledge workers are more productive with a UC client, as it facilitates better collaboration. However, other job functions would benefit from having communications integrated directly into the application.

For example, a doctor could more efficiently talk to a patient if the medical records and video were in one app instead of having to toggle between them. Also, the developer can optimize the app to the use case. An example of this is a call in a regulated vertical, which would automatically be recorded and archived instead of requiring a worker to turn on recording.

Voice Tone Analysis Extracts Sentiment

There are two core capabilities of the new SDK. The first is voice tone analysis, which uses machine learning to extract sentiment from a speech signal based on a combined analysis of lexical and linguistic information as well as acoustic and tonal data.

The product works across multiple systems and enables an organization to create a single data lake of all voice information. Also, administrators can use the AWS services to create custom dashboards to visualize the data the way they want to see it, using tools like Amazon Tableau or QuickSight.

The low hanging fruit for voice tone analysis is regulated industries where strict compliance requirements need to be met. For example, a financial firm could deploy the technology on its trading desk. The product can be used to record all the calls and use voice tone analysis to understand if traders are saying things they should not, or if the tone of a customer goes from positive to negative or vice versa as this could indicate a problem.

While industries like finance and healthcare are the most obvious, voice tone analysis can be used in every vertical as the ability to understand the sentiment of a call can be used for coaching or process improvement. The data can be analyzed, problems can be identified and corrective action taken.

Speaker Search Simplifies Caller Identification

The other capability is called speaker search, where a user is “voice printed” and the identity of that individual is stored in a database. The algorithms are very efficient and require only a small snippet of the voice to recognize a known user.

Speaker search helps expedite caller lookup and enrich call records and transcripts with identity attribution. Speaker search delivers a suggested unique internal identifier for the speaker and a confidence score. The decision to match the current speaker with a known speaker from the database can be made on an application-by-application basis depending on how it’s being used.

Also see: Unified Communications Tools Fuel Hybrid Work but Creates Risks

Bottom Line: Promoting the AWS Cloud Platform

For AWS customers, while the SDK itself adds value, there is a greater benefit. Once all the voice information is aggregated it can be stored in data lake in a location of the customers choosing. Given the customer is using AWS, it’s likely they will store in the AWS Cloud. By doing this, customers can use other AWS applications and services to get more value from the data.

As an example, Amazon Transcribe and Transcribe Call Analytics can be applied to the data lake to generate insights. Similarly, the data can be stored in an Amazon S3 bucket and voice analytics can send real-time notifications to a function deployed on AWS Lambda or an SQS queue.

Over the past few years, AWS has done an excellent job of making it easier to use a broader set of cloud services enabling customers to accelerate innovation. As communications becomes more composable, I would expect to see AWS come to market with more SDKs to enable customers to build communications-rich applications.

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The technology industry likes to consider itself a fount of innovation for addressing big, real-world problems. But high-tech companies, the media and consumers often focus much of their time and attention on the latest shiny new objects, click-bait headlines and attention-capturing services.

That said, some vendors are investing significant financial and human resources in tackling serious, often seemingly intractable issues affecting millions or billions of people. IBM’s Sustainability Accelerator is one such effort and the company’s recent announcement that it is accepting proposals for water management solutions is worthy of close examination.

Also see: Top Digital Transformation Companies

IBM Sustainability Accelerator: Strategy and Aims

Before diving into IBM’s latest request for proposals (RFPs), what exactly is the Sustainability Accelerator, how does it work and what are its goals?

Launched in February 2022, the Sustainability Accelerator is a pro bono program designed to leverage IBM technologies such as artificial intelligence (AI) and hybrid cloud, along with an ecosystem of company experts. The goal of the program is to enhance and scale projects focused on global populations vulnerable to environmental threats like climate change, pollution and extreme weather.

The program follows a three-step/two-year process that begins with IBM defining a central theme, then requesting RFPs. After a cadre of beneficiary organizations are considered and chosen by IBM and other representatives, they begin Phase One by meeting at IBM Garage facilities. Those discussions apply IBM design thinking and agile techniques to speed the identification of potential solutions and project development.

In Phase Two, IBM cross-industry experts configure resources and technologies to scale participants’ projects and help them meet community and environmental impact goals. Beneficiary organizations receive additional benefits, including monthly IBM Cloud credits, weather data credits, mentorship and access to the IBM partner ecosystem. IBM experts also support pilot solutions and deployments to facilitate optimal implementation, help determine long-term impacts and drive societal outcomes.

The initial cadre of Sustainability Accelerator beneficiaries that focused on sustainable agriculture include The Nature Conservancy of India, Heifer International, Plan21 Foundation for Sustainable Human Development, Deltares and Texas A&M Agrilife.

The second cadre, focusing on clean energy, announced last November at the COP27 event, includes the United Nations Development Programme, Sustainable Energy for All, Net Zero Atlantic, Miyakojima City Government and Environment Without Borders Foundation.

On a related topic: What is Generative AI?

Major Challenges

Why has IBM chosen water and water-related ecosystems for the next Sustainability Accelerator cadre? As noted by the company, the World Health Organization (WHO) reports that some 26 percent of the world’s citizens, over 2 billion people, live in water-stressed countries.

Additionally, at least 2 billion people use drinking water sources contaminated with feces that can transmit diseases such as diarrhea, cholera, dysentery, typhoid and polio. The WHO estimates that diarrhea causes or contributes to as many as 485,000 deaths annually.

These challenges, along with chemical pollution risks, are likely to be exacerbated in some regions because of climate change and population growth. Unfortunately, many of the areas most deeply impacted by these issues and problems also lack the resources to fully or effectively address them.

Non-profits and government initiatives focused on water management can apply to the RFP for IBM’s Sustainability Accelerator from March 14^th until the end of May 2023. The selected participants will be announced in November 2023.

Final Analysis: The Crucial Importance of Water

In the developed world, clean, safe drinking water is commonplace. Many consumers prefer bottled water despite having access to excellent tap water. Many others indulge in flavored or vitamin and mineral enriched water. Public sanitation is overseen and policed by local governments and utilities.

The system isn’t perfect, as pollution scandals and public health crises in Flint, Michigan, Jackson, Mississippi and many other places make clear. But while nearly three quarters of the world’s people have access to safe water and water resources, 2 billion other people are suffering, getting sick and dying because they do not.

Without water, life is impossible. With polluted water, the potential for illness, incapacitation and death can be extreme. It is difficult to think of a more fundamentally important problem that is complicated by so many economic, political and societal issues, or one that is in more dire need of original thinking and innovative solutions.

Reimagining and working to provide healthy water for 2 billion people is not impossible, but it is profound. IBM is not the only company searching for such solutions or supporting efforts to find them. However, given the company’s history of taking on and taming large-scale, real-world problems it will be worthwhile to see what participants and RFPs will be chosen for IBM’s latest Sustainability Accelerator program.

For more information, also see: Digital Transformation Guide

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With today’s profusion of enterprise applications and data sources, companies struggle to integrate it all. I spoke with Steve Lucas, CEO of Boomi about key issues and solutions.

Among the topics we covered: 

• When it comes to integration of applications and data in the enterprise, what are the issues that companies are struggling with these days?
• How do you recommend they address these tech integration challenges?
• How is Boomi addressing the integration needs of its clients? What’s distinct about the Boomi approach?
• The future of integration and automation in the enterprise? What do you see a few years out?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video: 

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The 2023 Edition of Enterprise Connect kicks off this week in Orlando, Fl. This is the industry’s largest telecom and networking event and while it’s unlikely we will see Mickey there, there will be a veritable cornucopia of news from vendors.

One of the more interesting announcements was from Bandwidth, whose new Maestro offering will enable organizations to integrate voice apps and artificial intelligence (AI) capabilities with their preferred unified communications (UC) and contact center (CC) platforms.

In a fast moving, all digital, CX driven world, the ability to roll out new communications-centric services faster can be the difference between being a market leader or a laggard.

Also see: Top Digital Transformation Companies

Communications: Core to Digital Transformation

Although not the sexiest part of IT, telecom remains a critically important part of digital transformation initiatives. Customer experience often starts in the contact center and then involves using voice plus other channels to interact with the customer.

There is heavy investment in moving both UC and CC to the cloud and then layering in other capabilities. The challenge is that managing telecom services from multiple providers is very complicated.

Bandwidth’s 2023 Enterprise Communications Landscape study quantified the complexity. The survey of over 1,000 enterprise IT leaders worldwide revealed that over 90% felt that managing communications systems is a significant challenge for their organizations. The biggest problem is trying to decide between choice and ease of deployment, particularly for global companies.

One option is to cobble together the different providers for different needs. That might mean a separate UCaaS, CCaaS and AI vendor along with a carrier per country. Then companies have to manage the interconnections at a global level.

The other option is to choose a single cloud communication provider that can offer all the required functions. This might work in the short term but what happens if they are slow to roll out new features? Or the business moves into a country the vendor doesn’t operate in?

The first option leads to slow deployments and disjointed management while option two lacks flexibility and can be expensive. This is choosing the lesser of two evils.

For more information, also see: Digital Transformation Guide

Multi-Vendor or Single Vendor: Lesser of Two Evils? 

Bandwidth Maestro is an open platform that enables customers to choose their favorite UCaaS or CCaaS provider and then easily add in the advanced capabilities they need. Because Bandwidth is the underlying network provider that powers almost all communications, companies can streamline connectivity to and between key platforms.

Maestro has four categories of services:

• Global network: Emergency calling, number management and global carrier coverage.
• Core voice modules: In-app voice calling (WebRTC), insight and analytics, enterprise voice (SIP/PSTN).
• Add-on modules: Fraud and spoof detection and integration with voice authentication engines, and a visual call flow builder.
• Connect modules: UCaaS and CCaaS carrier connectivity via their Duet service and conversational AI integrations.

On a pre-briefing, Bandwidth provided a demonstration in which a global organization picked their preferred CCaaS provider, used Bandwidth Duet for global connectivity and call verification/fraud detection, Cognigy for conversational AI, and Pindrop for voice authentication.

Bandwidth Maestro: Simplified Migration, Consistent Communications

Maestro is configurable through a visual builder and is available at launch in 38 countries where Bandwidth’s cloud has full PSTN replacement. Some of the more valuable use cases for Maestro include:

• Simplified migration from legacy on-prem systems to CCaaS and UCaaS platforms.
• Rapid integration of conversational AI capabilities into contact centers. This includes both speech to text and text to speech tools.
• Reduction in fraud by utilizing call verification integrated into SIP trunks.
• Custom call flows across multiple cloud communication vendors through a no-code visual flow builder.
• Communications consistency across the globe by integrating once into the Bandwidth cloud and taking advantage of the pre-existing integrations with global carriers.
• Customer specific-insights via dashboards and inbox alerts to monitor and troubleshoot using call logs, quality indicators and call/message responses.

The world of telecom is more complicated than ever before. The rise of cloud communications has enabled more vendors to offer UC, CC and AI services over the top, which is great for businesses as it creates more choice and faster innovation. The downside is having to bring all the capabilities together. Bandwidth’s Maestro orchestrates all the necessary services and simplifies deployment via its pre-integrations.

Also see: NTT Addresses the Why and When of Private 5G 

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At the recent GPU Developer Conference (GTC), NVIDIA announced a range of new hardware, software and cloud solutions and partnerships. One of the most consequential and exciting areas of news is a series of announcements related to Omniverse.

On a related topic: What is Generative AI?

Omniverse Brings Industrial Metaverse to Life

NVIDIA’s Omniverse is a computing platform based on Universal Scene Description (USD), an open ecosystem for developing 3D worlds in support of what NVIDIA refers to as the “industrial metaverse.”

NVIDIA sees the metaverse as the next generation of the internet – a 3D-connected internet – where users will go from 3D world to 3D world to work, collaborate and play. Developed by Pixar, USD has been adopted by Apple, Autodesk and many other companies, with NVIDIA calling it the “building block of the metaverse.”

One of the most important aspects of Omniverse is that it is designed to be as realistic as possible. This means all of the laws of physics applies, objects cast shadows, light reflects causing distortion and colors look different in the shade. This allows developers to create a digital twin of almost any environment, which can accelerate the deployment of new services. For example, a retailer could use Omniverse to simulate traffic flows in different store designs and test hundreds of options before doing any construction.

For more information, also see: Top AI Software 

NVIDIA Announces Enhancements to Omniverse

At the conference, several of NVIDIA’s announcements were related to the use of 3D worlds in design and visualization and the management of warehouses and factories, and in the automotive industry.

Below is a summary of the main Omniverse announcements at GTC23.

Growing the Ecosystem with New Omniverse Connectors

NVIDIA announced several new USD-based connectors to Omniverse, including with Siemens Teamcenter NX Process Simulate, Rockwell Automation Emulate 3D, FlexSim, Blackshark.ai, and Unity.

NVIDIA also announced the general availability of Bentley Systems LumenRT for NVIDIA Omniverse, which was announced last year. LumenRT for NVIDIA Omniverse allows companies to harness Omniverse to deliver 4D visualizations of digital twins.

NVIDIA now has hundreds of connectors to Omniverse – from software to sensors to cameras – creating a broad development ecosystem that enables companies to create more powerful and flexible solutions based on visualizing a solution before actually developing it.

For more information, also see: Top AI Startups

New Features in Omniverse

New features in the Omniverse platform itself include:

• Real-time ray-traced subsurface scattering shader – This is the process of accurately capturing what happens when light hits an object and is refracted or split or shattered through different types of surfaces. NVIDIA was the first company to do real-time photorealistic ray tracing a few years ago, and the addition of real-time ray-traced subsurface scattering in Omniverse is impressive.
• Performance improvements – Improvements around runtime data transfer and scene optimizers will support operating more complex digital twins, including for a factory, warehouse, city block and more.
• More SimReady assets – SimReady is a standard for creating photo-realistic 3D assets that include standard metadata that allows anyone on the Omniverse platform to use them. NVIDIA has now made hundreds of free SimReady assets available to customers.
• Generative AI – New generative AI capabilities include Text-to-Material, Text-to-Code, and Audio2Face.

The company also introduced NVIDIA Picasso, a new cloud service that developers can use to build and deploy generative AI-powered text-to-image, text-to-video and text-to-3D applications.

NVIDIA is partnering with Getty Images, Adobe and Shutterstock to provide developers with USD-based content they can easily incorporate into their workflows.

For more information, also see: Cloud and AI Combined: Revolutionizing Tech

NVIDIA Omniverse Cloud and Microsoft

One of the more intriguing announcements at GTC was that Omniverse Cloud will be available on Microsoft Azure.

The availability of a subscription-based, platform-as-a-service Omniverse offering via the Azure Marketplace will make it easier for enterprises to adopt and begin experimenting with the solution. NVIDIA Omniverse Cloud on Microsoft Azure will include onboarding, direct access to NVIDIA Omniverse experts and 24×7 enterprise support and training.

NVIDIA also announced that it is collaborating with Microsoft on advancing the “industrial metaverse” by providing access to Omniverse for hundreds of millions of Microsoft Enterprise users. In addition to Omniverse Cloud on Azure, the two companies are integrating Omniverse into Microsoft 365, including Teams, OneDrive and SharePoint. At a basic level, this opens the door to remote collaboration on Omniverse objects and environments. Omniverse is also being connected to Azure IoT to enable real-world inputs from Azure Digital Twins to Omniverse models.

This collaboration with Microsoft on the industrial metaverse is already being played out extensively in the automotive industry. Omniverse has the ability to connect diverse teams using a variety of software packages across concept and styling, design engineering, car electronics, smart factory, retail experience, and autonomous driving.

Connecting teams this way has the potential to make the manufacture of automobiles far more efficient. This includes the use of digital twins that will enable teams across each phase of manufacturing to test out designs in the virtual world before committing dollars in the physical world. BMW, Mercedes Benz, Volvo, Toyota, General Motors, Lotus Cars, and Lucid Motors all are using Omniverse in parts of their workflows.

BMW Opens the World’s First Entirely Virtual Factory

By far the most interesting announcement related to manufacturing at NVIDIA GTC was that the BMW Group is starting a global rollout of Omniverse across all of its factories, and is creating the first entirely virtual factory in Debrecen, Hungary.

Due to come online this year – approximately two years before the completion of the physical factory in 2025 – the virtual factory will allow workers to get a head start on designing the factory layout, training of robots, and even sales and marketing, which will save money and increase safety.

On a related topic: The AI Market: An Overview 

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ChatGPT and Google Bard are both generative AI platforms. Users can enter a query into either ChatGPT or Google Bard, and both generative AI tools will provide long and detailed content creation across many areas. 

The term generative AI entered the public consciousness like a whirlwind in November 2022, when OpenAI debuted ChatCPT. Stories about AI chatbots and generative AI were all over social media news outlets. This emerging technology was going to change the world and make whole industries obsolete, or so the news suggested. 

Google soon debuted its own generative AI with the launch of Google Bard. And while the debut event wasn’t perfect – Google Bard made an factual error – the AI platform’s potential is enormous. Google has vast expertise in algorithms and artificial intelligence and so it’s reasonable to forecast that Bard will develop rapidly. 

Which generative AI tool is best? There are similarities between ChatGPT and Google Bard but there are also differences. Let’s compare and evaluate them.

On a related topic: What is Generative AI?

And: ChatGPT: Understanding the ChatGPT ChatBot 

ChatGPT vs. Google Bard: Key Features

ChatGPT is the most recent iteration of natural language processing models. Its GPT-3 language model has been trained at length using online text written by actual people, as well as news items, novels, websites, and many more sources. It uses this database to create responses to queries. And the ChatGPT AI-powered chatbot takes advantage of machine learning to respond conversationally. 

ChatGPT versions range from more than 100 million parameters to as many as six billion to churn out real-time answers. It includes AI Text Classifier, which is a plagiarism checker.

ChatGPT has already gravitated to the Microsoft side of the cloud computing market due to its relationship with Bing. But that isn’t to say that other hyperscalers and big tech giants won’t employ it – or develop their own alternatives.

Enter Google Bard, which has been around as an experimental language model since the middle of 2021. Google runs it on top of its BERT AI language model as a way to answer questions, conduct sentiment analysis, and perform language translation. Its answers go far beyond those typically given during a traditional Google search. 

While ChatGPT can manage up to six billion parameters, Bard tops out at 1.6 billion.  It also lacks a plagiarism checker. On the plus side, Bard has the Google search engine universe to glean data from whereas ChatGPT has to rely on the less comprehensive Bing search engine.

As Google Bard uses a smaller model than ChatGPT, it requires a smaller amount of computational power. For those with limited computing resources, this gives a clear advantage to Google.

Category Winner

Chat GPT wins on overall feature set, but Google Bard wins on computational resources. And don’t expect Google to accept overall second place. It is throwing a lot of resources at the AI competition. 

On a related topic: Top Natural Language Processing Companies

Also see: The Future of Artificial Intelligence

ChatGPT vs. Google Bard: Content Generation

ChatGPT can summarize long texts, articles, and reports to reflect the primary ideas with some accuracy. It can also highlight key passages within text.

However, it may struggle with long and detailed texts. I can’t imagine it would do well summarizing the Old Testament or the Buddhist texts such as the Bhagavad-Gita, which combine many concepts together. As for requesting in-depth analysis of poetry, early reports suggest that it’s not ChatGPT’s strong suit.

It is apparently possible to train ChatGPT to produce text that can mimic the style and grammar as an original piece of material. This is probably good for consistency in social media posts and email marketing. 

A big strength of ChatGPT is translation of English into other languages. It has already offered improvements in translations. The next time you see a Chinese post in Facebook, translate it if you want a laugh. Often the translations are awful. Improvement in this area is much needed and ChatGPT is a definite step forward.

Google Bard lacks many of these features. But it is supposedly better at creative writing via its ability to offer thematic, word, and phrasal suggestions that are designed to help writers come up with ideas. Being a writer myself, I’m doubtful of this capability but we will see.

Perhaps the best use of Google Bard for writers might be to provide feedback on aspects such as setting, style, tone, and overall structure, as well as making it easy to find synonyms, alternative phrasings, and to spot overused words and terms.

Category Winner

Currently, ChatGPT wins on content generation.

On a related topic: The AI Market: An Overview 

ChatGPT vs. Google Bard: Search Engine Integration

You would think that Google would lead in this category. But it fumbled in its launch launch and demonstration of Bard and has fallen behind in search engine integration. In any case, Google could be accused of resting on its laurels to some degree on search. Arguably, Google’s 93% global market dominance of search is under threat with the appearance of AI-enhanced search.

ChatGPT, meanwhile, has surpassed the 100 million user mark and is already integrated into Microsoft Bing and Microsoft Edge. Bing has received a major boost from this technology. Forever lagging behind Google on accuracy, relevancy, and popularity, Bing now finds itself surging in usage due to far greater relevancy.

Category Winner 

ChatGPT wins here but expect Google to rapidly play catch up. There are already rumors that it plans to release a snowstorm of AI-based chat products this year. The search giant has enormous incentive to make up for lost ground. 

For more information, also see: Top AI Software 

ChatGPT vs. Google Bard: Plagiarism

Google Bard does not yet have any kind of plagiarism mechanism built in. ChatGPT is well ahead in this regard. Its existing classifier checks for plagiarism with some success.

However, ChatGPT parent OpenAI admits that it is not yet fully reliable. Work is being done to help it better distinguish between AI-written and human-written text. Currently, the classifier correctly identifies 26% of AI-written text and incorrectly labeled human-written text as AI-written 9% of the time. This is a big improvement from the original classifier but it has a ways to go. This feature is particularly important when it comes to the detection of automated misinformation campaigns that take advantage of AI tools.  

OpenAI makes it clear that its classifier should never be used to condemn something as plagiarized. It helps detect potential examples with some accuracy. This helps narrow the attention of people to determine if actual plagiarism has taken place. Yet it’s also relatively inaccurate with texts below 1,000 characters, is only strong in English, and of course, AI systems can be programmed to avoid the patterns that classifiers monitor. 

Category Winner

Regardless of these cautions and shortcomings, ChatGPT wins hands down on plagiarism checking.

For more information, also see: Top Robotics Startups

ChatGPT vs. Google Bard: Customer Service-Automation

ChatGPT is probably better than Google Bard on responding to customers using a frequently asked questions format. Queries about shipping schedules, progress, product returns, product and service availability and options, as well as technical support matters seem to be relatively well handled by ChatGPT.

Thus, ChatGPT will prove valuable in lowering customer support costs, and easing the load of human representatives. It has also proven to do a decent job in analyzing information and online behavior and making product recommendations. Automation features extend to appointment scheduling, reservations, payment processing, and more.

Google Bard appears to be not so well suited to these tasks. It is more suited to time management, appointment reminders, and ensuring all steps of a process are carried out in sequence. It can be used to automate tasks like restaurant reservations and travel arrangements.

Category Winner

ChatGPT wins on customer service and automation.

For more information, also see: History of AI

ChatGPT vs. Google Bard: Pricing

ChatGPT has a basic version available for free but the main ChatGPT Plus version costs $20/month. Subscribers get access to ChatGPT at peak times, faster responses, and priority access to new features and improvements. In contrast, Google Bard is free. 

Category Winner

Given that Google Bard is currently free, it wins on pricing.

For more information, also see: Best Machine Learning Platforms

ChatGPT vs. Google Bard: Bottom Line

Both Google Bard and ChatGPT use a transformer-based AI architecture as part of a neural network that handles sequential data.

Google has the world wide web as its source of data, and thus has access to a broader data set. ChatGPT, on the other hand, harnesses a smaller, more fine-tuned neural network focused on text inputs.

Thus, each will display strengths and weaknesses due to the differences in their data sets. Use cases will arise where it becomes clear that ChatGPT is best for XYZ use cases whereas Bard is better for ABC use cases. But as one gains ground in a particular sector or use case, the other will likely innovate and add that feature, too.

Speaking generally, Google Bard looks good for text processing and summarization, whereas ChatGPT seems to perform better in chatbots, language translation, and answering questions. Some say that Google Bard brings with it a broader understanding of language, while ChatGPT brings a deeper understanding of language and how it is utilized in different contexts.

Over time, Google Bard may not only catch up, it may leap ahead due to its ability to include recent events in results, and its ability to pull data from a wider pool of information. Be aware, though, that this is all machine learning and is not actual human intelligence.  These systems have the potential to be a wonderful aid to human existence, but they will never fully replace individual brilliance or the need for human decision making.

For more information, also see: 

•  Cloud and AI Combined: Revolutionizing Tech 
• AI vs. ML 
• Algorithms and AI 

The post Chat GPT vs. Google Bard: Generative AI Comparison appeared first on eWEEK.

This week, GPU market leader NVIDIA is holding its virtual GPU Technology Conference (GTC). Over the years, GTC has evolved from a graphics and gaming show to an industry event dedicated to all things artificial intelligence (AI).

One of the major use cases for AI and NVIDIA technology is in the area of robotics. This includes the entire lifecycle of robot development including training, simulation, building, deployment and management.

NVIDIA’s Isaac robotics platform is accelerating the way intelligent robots are developed, deployed, and managed. The end-to-end solution aims to optimize the efficiency of robot fleets across different environments.

At GTC, NVIDIA made several major announcements related to the Isaac robotics platform, which are set to enhance the fields of robotic simulation, synthetic data generation, and more. Here is a recap:

For more information, also see: Top AI Software 

Isaac Sim on Omniverse Cloud

NVIDIA made its Isaac Sim robotic simulation platform available on Omniverse Cloud for enterprises.

Omniverse Cloud is NVIDIA’s platform-as-a-service (PaaS) offering for compute-intensive workloads, such as synthetic data generation and software validation. By integrating Isaac Sim with Omniverse Cloud, NVIDIA now offers access to its software development kits (SDKs) and tools. Other NVIDIA products like Drive and Universal Scene Description (USD) Composer will also be available on Omniverse Cloud for enterprises.

NVIDIA already offers various options for running Isaac Sim in the cloud. One option is to deploy Isaac Sim containers from the NGC repository—a catalog of AI frameworks—on any cloud service provider, which requires some setup and management by the enterprise. Another option is to use AWS RoboMaker and run a fully managed version of Isaac Sim.

Gerard Andrews, Senior Product Marketing Manager of Robotics at NVIDIA, emphasized the value of cloud computing in robotics, particularly when it comes to scalability of model training: “You’re always going to be training models, updating these models, and will continue to need a methodology to revalidate those models. We believe that type of interaction with the simulator is best done in a cloud infrastructure.”

On a related topic: What is Generative AI?

Isaac ROS Enhancements

Isaac robot operating system (ROS) is a collection of GPU-accelerated modules aimed at providing perception AI to the robotics development community. The latest release features a new grid localizer package that uses LiDAR to locate robots on a map, an updated Envy Blocks 3D reconstruction software with a people detection mode, and open-sourced modules. The release also supports the Jetson Orin NX and Orin Nano modules.

The development of robots is a complex process involving engineers from different disciplines. Therefore, it’s important to have collaborative tools that provide access to the same datasets and simulation for all team members. Using the cloud, simulations can be accessed from anywhere, even without a GPU-accelerated computer.

Jetson Portfolio Expansion

NVIDIA has expanded its Jetson portfolio of computing boards with a new addition, the Jetson Orin Nano entry-level developer kit, which can be pre-ordered at $499.

This developer kit leverages the 1024-core Ampere GPU and supports a wide range of AI models, including transformers. It also offers performance and energy efficiency for cloud-to-edge workloads, which is a concept that involves two computers working together to optimize robot performance.

Jetson (the first computer) acts as the robot’s brain or control center. The second computer exists in the cloud and is used for training, simulating, and continuously testing the software that runs on the robot. By combining the power of both computers, cloud-to-edge technology enables intelligent robotics.

Jetson consists of six modules, which are now all available for production use. The modules are divided into three main categories:

• The high-computing Jetson AGX Orin, in 64 GB and 32 GB for complex edge AI workloads
• The Jetson Orin NX in 16 GB and 8 GB
• The Jetson Orin Nano in 8 GB and 4 GB, the new entry-level option for robotics

On a related topic: The AI Market: An Overview 

TAO 5.0 Release

NVIDIA rolled out TAO 5.0, a low-code AI training toolkit, which brings sophisticated vision and model development to any data scientist, cloud service, or device. TAO 5.0 paves the way for more efficient AI integration across various cloud services and automates the tedious process of dataset curation. It also comes with auto machine learning (ML) capabilities, making it easier to integrate ML into any cloud service.

The release of TAO 5.0 is a major expansion for the vendor, said Adam Scraba, NVIDIA’s Director of Product Marketing. It democratizes AI development and makes it more accessible to a wider audience.

Metropolis Platform Advancements

NVIDIA is working on making computer vision more accessible through Metropolis, an end-to-end development workflow that automates physical spaces—such as buildings, factories, warehouses, and smart cities—with AI and data analytics.

NVIDIA customers like Procter and Gamble, PepsiCo, BMW, and Siemens have already adopted Metropolis to streamline operations. With the latest expansion, Metropolis now includes a low-code AI training toolkit, which is expected to promote computer vision and AI adoption in various industries.

Additionally, NVIDIA has partnered with ST Microelectronics to bring sophisticated visionary models to microcontrollers on a large scale. The goal is to improve computer vision in various industrial settings, such as factories, warehouses, and supply chains.

“We’re further pushing accessibility of these latest vision capabilities. We’re packaging up really sophisticated solutions to effectively solve these grand computer vision challenges in the form of API-driven services,” said Scraba.

By providing powerful tools, workflows, and accessible AI development solutions, NVIDIA is contributing to the advancement of robotics across industries. These advancements are expected to not only improve efficiency but also pave the way for safer and more reliable human-robot interactions in the future.

While NVIDIA makes excellent silicon, its strength is its ability to put together “full stack” solutions, making deployment much easier. Instead of having to cobble together parts from different companies or even across the NVIDIA portfolio, developers have access to everything they need. The work in robotics is an excellent example as it requires more than building the hardware. There are training issues, simulations, and cloud integration challenges. NVIDIA has taken much of the complexity out of the process, enabling companies to focus on use cases rather than technology integration.

On a related topic: The Future of Artificial Intelligence 

The post NVIDIA Eases The Complexity Of Robot Training appeared first on eWEEK.

Data governance tools are software applications that enable organizations to ensure data availability, usability and integrity, with the ultimate goal of ensuring compliance with data management standards. 

With the right data governance software, organizations can efficiently manage their data asset and ensure it’s used in accordance with company policies, industry regulations, and best practices. Optimal data governance is not limited to just technology or tools; it’s all-encompassing and involves people, processes and technology. Data governance helps enterprises maintain consistent data quality, mitigate risks, meet compliance requirements, and – most important – support better decision-making.

For more information, also see: What is Data Governance?

Jump to:  

• Top Data Governance Tools
  
• Collibra: Best For Customization
  
• erwin Data Governance: Best for Business Collaboration
  
• Informatica Axon Data Governance: Best for Business Users
• IBM Cloud Pak for Data: Best for Governing Structured and Unstructured Data
  
• Talend Data Fabric: Best for Data Integration
• SAS Information Governance: Best for Analytics
  
• SAP Master Data Governance: Best for Master Data Management
  
• Ataccama One: Best for Ease of Use
  
• Alation Data Governance: Best for Automation
  
• OvalEdge: Best for SMEs
  
• Apache Atlas: Best for Hadoop Clusters
  
• Precisely Data360 Govern: Best for Affordability
  
• Rocket Data Intelligence: Best for Automated Data Lineage
  
• Semarchy xDM: Best for ETL/ELT Data Integration Tools
  
• Best Data Governance Tools: Comparison Chart
  
• Honorary Mentions
  
• What are Data Governance Tools?
  
• Key Features of Data Governance Platforms
  
• How to Choose the Best Data Governance Software and Tools
  
• Benefits of Data Governance Solutions
  
• Bottom Line: Data Governance Software and Tools
  

Top Data Governance Tools

The best data governance tool offers full visibility and control over data quality, security and compliance. Here are 15 top-rated data governance software and tools, including their key features, pricing, pros and cons.

We analyzed hundreds of data governance tools and selected the top 15 for different user categories to help you determine the best solution for your organization. During our analysis, we considered various factors and data points, including features, affordability, quality service, customer satisfaction, and their ability to meet requirements. We also note the tools that are strong contenders for the top spots for your consideration.

For more information, also see: Best Data Analytics Tools 

Collibra

Best for Customization

Collibra is a cross-organizational data governance software that enables companies to define data policies properly, catalog and classify data assets, define relationships between them, monitor and audit their usage, protect sensitive information, and understand the flow of their data. 

Users praise the tool for its flexibility and customizable workflows, which allow them to tailor data governance processes within their organization. Collibra integrates with several third-party tools, including SAP ERP, Salesforce, Microsoft Dynamics, Workday Financial Management, Splunk Cloud, and Apache Parquet.

Key Features

• Data business glossary
• Centralized policy management to maintain enterprise-wide compliance
• Data reconciliation across systems
• Stewardship management
• It provides end-to-end data lineage visualization

Pros

• A 20-day free trial is available
• Easy to customize
• Ensure compliance with regulatory mandates such as GDPR

Cons

• Steep learning curve

Pricing

• Contact the company for custom quotes.

erwin Data Governance

Best for Business Collaboration

erwin Data Governance (formally known as erwinData Intelligence by Quest) is an enterprise-level solution that allows organizations to automate, manage and monitor the flow of data across their enterprise. 

It provides a single source of truth for data governance by consolidating data silos and providing visibility into the data stewardship process. The platform consolidates its data catalog and literacy capabilities to provide a unified view of enterprise data, monitor its usage, and ensure compliance with data regulations and best practices. 

Key Features

• Data stewardship management capabilities
• Automate data profiling and data quality assessments
• ​​Create a data governance model to manage standards, policies and best practices 
• Automate metadata management and data mapping
• Allows the connection of physical metadata to specific data models, business terms, definitions and reusable design standards

Pros

• Enhance data intelligence self-service
• Highly customizable
• Reporting and analytics

Cons

• Search capabilities could be improved

Pricing

Available on request.

Informatica Axon Data Governance

Best for Business Users

Informatica Axon is a data governance tool used by business, IT, and security teams to ensure data integrity, accuracy, privacy, and regulatory compliance. It offers a marketplace portal where business users can find approved data assets. 

It integrates with Informatica data quality and Informatica enterprise data catalog to facilitate collaborative data governance programs across business and IT leaders.

Key Features

• Integrates with the electronic data capture (EDC)
• Comply with regulations such as GDPR, CCPA, BCBS 239, and HIPAA
• Data visualization capabilities
• Dictionary, search, and rating capabilities
• Leverage AI and ML for automation 

Pros

• Excellent support team
• Business process mapping
• Data discovery

Cons

• No free trial

Pricing

Pricing is available on request.

IBM Cloud Pak for Data

Best for Governing Both Structured and Unstructured Data

IBM Cloud Pak for Data is an open, extensible data and AI platform built on Red Hat OpenShift that helps enterprises modernize their data. It provides an integrated set of services, tools, and frameworks to help data teams securely access, govern, integrate, and analyze data across hybrid clouds. 

Cloud Pak for Data includes profiling, cleansing, monitoring, matching and enriching data capabilities. The Data Quality service helps improve data accuracy and consistency by profiling and validating data, and identifying and fixing inconsistencies. 

Key Features

• Metadata management
• Search, visually explore and better understand data
• Enhance governance processes with standardization rules
• Data lineage and privacy risk assessment
• Advanced data discovery and privacy

Pros

• Manage policies and rules
• Address data quality issues with capabilities for profiling, cleansing, monitoring, matching, and enriching data
• Enforce data protection rules
• Data lifecycle management

Cons

• Some users reported that the tool is cost prohibitive for small business

Pricing

Available on request.

For more information, also see: Top Data Mining Tools 

Talend Data Fabric

Best for Data Integration

Talend is an open-source data integration platform that provides many data integration solutions to help businesses connect, access, and analyze data from over 140 data sources. It offers a graphical data model designer, connectors, and components library for data integration and management. 

With Talend, users can build, deploy, and manage data pipelines for various use cases, such as ETL, ELT, and data warehouse automation. Talend data fabric platform combines data integration, data integrity and governance, and Application and API integration.

Key Features

• Automatically clean and profile data in real-time
• Application-to-application integration
• Data profiling to identify data quality issues
• Data sharing with internal and external stakeholders through API 
• Allows users to embed data quality, stewardship, preparation, and cataloging into their data pipelines

Pros

• Multi-/hybrid-cloud and on-premises integration flexibility
• Over 900 data connector
• Self-service capabilities

Cons

• Some users reported that the graphical data model designer gets slow when running heavy projects

Pricing

Available on request.

SAS Information Governance

Best for Analytics

With SaS Information Governance, analysts, data stewards and business users can find, catalog and discover data, reports, models and other analytics assets. It also helps organize, classify and govern data assets and improve data quality, trust and consistency. 

The software integrates with SaS Viya, an AI-driven SaS analytics platform, to provide a unified view of all data assets. It helps manage data quality and security, automates tasks, and governs decisions. This tool is sold as standalone software and an optional add-on with other products.

Key Features

• Automatic content classification and discovery of privacy data
• Data catalog, preparation and lineage capabilities
• Open metadata and governance
• Includes a business glossary and lineage viewer

Pros

• A 14-day free trial
• Self-service interface
• Cloud-native
• Ease of use for non-technical users

Cons

• According to some users, support could be improved

Pricing

Available on request.

SAP Master Data Governance

Best for Master Data Management

SAP Master Data Governance (MDG) is a centralized, enterprise-wide solution for managing master data. It enables organizations to effectively govern and manage master data across the enterprise while helping to ensure data accuracy, consistency, and compliance with corporate and regulatory policies. 

MDG helps to automate and standardize master data processes, improve data quality, and provide real-time insights into master data. 

Key Features

• On-premise, private, and public cloud deployment
• Data quality and process analytics
• Create a unified view source 
• Identify duplicate records and merge them in the master data consolidation run
• Mass data processing across the organization and enterprise-wide data quality validation

Pros

• Integration with SAP ERP
• Customizable
• Enforce compliance
• Customer praises its rules-based workflows

Cons

• Some users say that implementing SAP Fiori is challenging

Pricing

Pricing for this tool starts from $55 per block of 5,000 objects per month. Contact the SAP MDG sales team for detailed quotes.

Ataccama One

Best for Ease of Use

Ataccama One is a Platform-as-a-Service self-driven data management platform that provides a comprehensive suite of tools for data governance, quality, integration, reference data management, and data stories. 

The platform is designed for various data professionals with varying experience and use cases, including data stewards, governance pros, data analysts, data scientists, and data engineers. With Ataccama ONE, you can automatically assign business rules for all your data, customize data quality rules with its no-code capability, and automatically detect anomalies in your data.

Key Features

• Deployable on-premise, public and private cloud
• Integrates with big data platforms like Spark, AWS, Databricks, Hadoop, Hortonworks, Cloudera, MapR, Google and Azure
• Orchestrate, schedule, and monitor pipelines
• Automatically assign business rules
• Automated data quality calculation and anomaly detection
• Built-in data quality processing
• Automated data discovery and change detection

Pros

• Easy to setup and use
• All-in-one ​​for data quality, MDM, data catalog, RDM, data integration and data stories
• Full audit history
• Role-based security

Cons

• Usually requires a technical expert to deploy 

Pricing

Available on request.

Alation Data Governance

Best for Automation

Alation Data Governance helps organizations improve their data quality, governance, and security. The platform automates data governance by leveraging artificial intelligence, machine learning, and crowdsourcing to enhance data stewardship, classification, business glossary, and quality documentation. 

Its business glossary automatically suggests business terms and identifies stewards and subject matter experts based on usage patterns to boost collaboration and scale documentation.

Key Features

• Centralize policy management
• Automate stewardship and curation
• Support hybrid and multi-cloud environments
• Its governance dashboard enables stakeholders to measure progress on open tasks and policy usage 

Pros

• Automates governance tasks
• Users can create and configure governance workflows without coding
• Apply policies and controls
• Behavior analysis

Cons

• The tool can get expensive, as per user review

Pricing

Available on request.

For more information, also see: Top Data Visualization Tools 

Atlan

Best for Data Team Collaboration

Atlan helps teams collaborate and democratize access to data across the enterprise. The platform features a google-like search, a central repository for data, and an active data governance system that gives users the power to organize and control data. 

It enables users to discover, curate, and manage data securely with powerful search, workflow automation, and audit trail capabilities. Some of Atlan’s customers include Cisco, Autodesk, Nasdaq, Unilever, DigitalOcean and more.

Key Features

• Custom classifications
• Programmable PII bots to auto-identify sensitive PII, HIPAA, and GDPR data
• Masking and hashing policies
• Personalize user roles by setting metadata, data and glossary policy

Pros

• User friendly
• Automation
• Users praise its integration with Snowflake and Power BI 

Cons

• Documentation can be improved

Pricing

Available on request.

OvalEdge:

Best for SMEs

OvalEdge is a data catalog and governance tool that crawls various data sources, including data lakes, files, tables, analytics software, and more to index metadata. It helps defines roles and responsibilities for data governance, tracks data lineage, provides data quality insights and automates data cataloging. 

OvalEdge allows data stewards to build and manage a common business lexicon, track and monitor data usage, and manage the data lifecycle. The tool also provides visibility into data security, privacy and compliance.

Key Features

• Its data classification capabilities classify data into various categories, including PII, sensitive, protected and more
• Business data glossary
• Data quality monitoring and control
• Data access request and control
• Metadata change management
• Data quality rules
• Data valuation
• Build data lineage by parsing SQLs, ETLs, reporting systems, and a variety of codes

Pros

• AI-driven classification
• Lineage building
• Customizable and easy to use

Cons

• Documentation could be improved

Pricing

OvalEdge offers three pricing plans, including essential (Starts at $15,600 per year), professional (custom pricing) and enterprise (custom pricing).

Apache Atlas

Best for Hadoop Clusters

Apache Atlas is an open-source metadata management and governance platform developed to enable a data-driven approach to enterprise management. 

The tool fosters collaboration among data scientists, analysts and the data governance team by providing open metadata management and governance capabilities for organizations to build a catalog of their data assets and classify and govern them.

Key Features

• Ability to define new types for the metadata to be managed
• Pre-defined types for various Hadoop and non-Hadoop metadata
• Enable users to dynamically create classifications such as PII, EXPIRES_ON, DATA_QUALITY, SENSITIVE
• Security and data masking
• Atlas supports ingesting and managing metadata from the following sources HBase, Hive, Sqoop, Storm and Kafka

Pros

• Includes security features such as SSL, Service Authentication and SPNEGO-based HTTP authentication 
• Community driven
• Feature-rich

Cons

• Steep learning curve

Pricing

Free.

Precisely Data360 Govern

Best for Affordability

Precisely Data360 Govern is a comprehensive solution designed to automate and streamline data governance processes. It works with Data360 DQ+, Spectrum Quality, Trillium Quality and other data quality tools to provide an end-to-end solution for managing data quality and compliance. 

Data360 Govern provides a suite of governance tools, including data lineage, data glossaries, and policy management, to help organizations identify and track data issues and remediate them. 

Key Features

• Data policy documentation
• Data detection and tagging
• Business glossary
• 3D data lineage
• Data stewardship
• ​​Harvests metadata, imports data quality scores, and automates workflows

Pros

• Contextual automation capabilities
• Data enrichment and location intelligence
• Leverage AI/ML for data classification and tagging

Cons

• Limited documentation

Pricing

Available on request.

Also see: Real Time Data Management Trends

Rocket Data Intelligence

Best for Automated Data Lineage 

Rocket Software acquired ASG Technologies in 2021. The company refers to its Data Intelligence (DI) platform as “the Solution for Data Distrust,” and it provides features to help organizations manage, analyze, and gain insight from their data. 

The platform is designed to help organizations gain trust in their data, increase operational efficiency, and reduce risk. It provides compliance with various industry mandates such as SOX and HIPAA and enables organizations to drive more value from their data.

Key Features

• Visualize regulatory lineage
• Removes data bloat and redundancies
• Data stewardship
• Complies with mandates such as GDPR, CCPA/CCPR, BCBS 239, CCAR, FRTB, GDPR, HIPAA, and SOX 
• Automated support for harvesting metadata

Pros

• Compliance and audits
• Analytics and insights
• Automate workloads across hybrid infrastructures

Cons

• Steep learning curve

Pricing

Available on request.

Semarchy xDM

Best for Customers with ETL/ELT Data Integration Tools 

Semarchy xDM is an intelligent data hub that includes Master Data Management (MDM), Reference Data Management (RDM), Application Data Management (ADM), Data Quality, and Data Governance capabilities.  

With its data-driven capabilities, Semarchy xDM provides features such as data quality, validation, matching, de-duplication, authoring, and workflows. It helps organizations to understand and manage their data, improve data quality, and build applications that rely on accurate and trusted data.

Key Features

• Includes tools for data visualization, predictive and prescriptive analytics, data science, and AI/ML
• Integrates with ETL/ELT, BPM, Virtualization, and APIs
• Its Discover xDM can be used for data profiling, data preparation, data lineage, and search
• Deployable on-premise and cloud environment

Pros

• Rich knowledge-base
• Scalable and flexible
• Feature-rich

Cons

• Users reported that the user interface is less intuitive.

Pricing

Available on request.

Best Data Governance Tools: Comparison Table

Honorary Mentions

The following 20 data governance software solutions are notable mentions for their data governance capabilities. 

• Alteryx
• Dataddo
• Integrate.io
• Google Cloud Data Catalog
• Immuta
• Avo
• Oracle Enterprise Metadata Management
• Egnyte
• Segment
• Transcend
• Syniti Knowledge Platform
• Alex Solutions
• OneTrust Data Governance
• ObservePoint
• Microsoft Purview
• Melissa Data Quality Suite
• Compliance Manager GRC
• LogicGate
• Riskonnect
• Io-Tahoe

For more information, also see: Top Data Visualization Tools 

What are Data Governance Tools?

Data governance tools are software solutions that help organizations ensure compliance of data management, and also manage the quality and security of their data. 

Data governance tools include capabilities such as data cataloging, data classification, data cleansing, access control, data encryption, data masking. They also facilitate compliance with data privacy regulations, such as the GDPR and HIPAA. 

These tools can also help organizations develop better data quality practices by allowing them to detect and remove incorrect or incomplete data from their systems. Additionally, data governance tools can be used to ensure the accuracy of data used in data analytics and artificial intelligence initiatives.

Key Features of Data Governance Platforms

The following are the key features to look out for in a data governance tool:

• Data cataloging and metadata management: The data cataloging and metadata management feature provides users with the ability to easily organize, classify, and store their data in a central repository. This helps ensure that all data is properly indexed and easily retrieved for analysis or other uses.
• Data quality assurance and governance: Data governance tools provide users with the ability to monitor the accuracy, completeness, and validity of their data, as well as ensure that data is being used in accordance with corporate policies.
• Security and access controls: Security and access controls are essential features of data governance platforms, as they allow users to control who has access to which data sets and limit access to certain data elements.
• Automated workflows: Automated workflows are also essential features of data governance platforms, as they enable organizations to automate processes such as data validation, data transformation, and other data-related tasks.
• Data visualization: Data visualization tools allow users to gain insights into their data by visualizing it in charts, tables, and other forms. This makes it easier to spot trends and correlations that would otherwise go unnoticed.
• Data lineage and traceability: Data governance platforms enable users to track data from its source to where it is used. This allows organizations to understand the data better and ensure it’s handled and used securely and competently.

How to Choose the Best Data Governance Software and Tools

To select the best data governance software and tools for your organization, it’s important to consider the size and complexity of your data environment as well as your data governance needs.

• ​​Evaluate your needs: Before shopping for data governance software, clearly define your organization’s data governance requirements and objectives. Ensure your data governance goals are aligned with your business strategy.
• Understand the different types of data governance software: It’s important to understand the different types of software and tools available to determine which best suits your organization’s needs. Talk to not just sales reps but also industry peers. 
• Research vendors and solutions: Once you clearly understand your data governance needs, it’s time to start researching vendors and solutions. Look for vendors with experience working with organizations like yours and a proven track record of successful implementations.
• Consider the total cost of ownership (TCO): When evaluating data governance software, consider the TCO. This includes the initial purchase price and ongoing maintenance, training, and support.
• Get user feedback: Before making a final decision, getting user feedback from people in your industry who may be using the software is a good idea. Ask questions like “How easy is it to use?,” “What features do you find most useful?,” and “What do you think could be improved?”
• Test the software: Once you’ve narrowed down your list of potential software solutions, it’s important to ensure it meets all your requirements. Many vendors offer free trials or demos so you can get a feel for how the software works before making a commitment.

To learn more, also see: Data Analytics Trends 

What are the Benefits of Data Governance Solutions?

By implementing data governance solutions, organizations can reap many benefits. Improved data quality is one of the main advantages, as these solutions establish standards for data management and introduce controls to ensure that only accurate and reliable data is used. This leads to better decision-making and improved customer service. Additionally, data governance solutions help organizations stay compliant with changing regulatory requirements, reducing the risk of hefty fines.

Data governance solutions also help to reduce data management costs, as they reduce redundant or outdated data and help to eliminate manual processes. Increased data security is another benefit, as these solutions provide robust security measures to protect data from unauthorized access. Furthermore, they provide better visibility into the available data, allowing decision-makers to make better decisions faster. 

Bottom Line: Data Governance Software and Tools

When shopping for data governance software, consider the specific needs of your organization, such as its size, its existing data landscape, and desired outcomes. The selected solution should be capable of providing the necessary features and capabilities to support your enterprise’s data governance efforts effectively.

To learn more, also see: Top Business Intelligence Software 

The post Best Data Governance Software and Tools appeared first on eWEEK.

I spoke with Stephen Franchetti, CIO of Samasara, about automation, cloud and IoT as tools to help companies rapidly scale their IT infrastructure to support hyper-growth.

Among the topics we addressed:

• Companies face a real challenge in the current hyper-growth period as they work to scale IT to meet increasing business demands. What trends do you see as companies grapple with this issue? What are the typical problems?
• What advice do you give companies that are working to scale their IT infrastructure? What about the role of automation?
• Samsara focuses on the IoT sector – how is Samsara addressing the needs of its clients?
• The future of scaling IT to meet business demands? What are some key milestones we can expect in the years ahead?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

The post Samsara CIO Stephen Franchetti on Scaling IT to Meet Business Demands appeared first on eWEEK.

Predictive analytics is the use of data and other tech tools like artificial intelligence (AI) and machine learning (ML) to predict future outcomes. Predictive analytics uses historical data to discover patterns and trends that predict future occurrences.

Currently, many industries are actively using predictive analytics, including manufacturing, healthcare, finance, education, retail, cybersecurity, and agriculture. For example, predictive analytics can be used for everything from predicting business revenue to machine downtime.

As data science evolves, new methods of using data are taking hold. Now, organizations can use data proactively through the use of predictive analytics.

Read more: What is Predictive Analytics? 

7 Predictive Analytics Best Practices

For organizations ready to take advantage of predictive analytics, there are several best practices to follow for success. These include identifying objectives, testing predictive models, and making continuous improvements. 

1. Identify Objectives

The first step organizations should take is to define objectives for using predictive analytics. This involves outlining what the organization wants to predict, which will inform how predictive models are developed. These objectives should align with overarching business goals.

For example, if one business goal is to reduce operating expenses, predictive analytics models could predict unnecessary costs such as downtime.

2. Define Key Metrics

Organizations must also define the key metrics they’ll use to ensure the success of their data initiatives. These are the key performance indicators (KPIs) that show progress toward predictive analytics objectives. 

For the example above, KPIs for reducing operating expenses may include total expenses or operational expense ratio (OER). Organizations should stick to measuring only the KPIs that align with their predictive analytics and business objectives.

3. Select Relevant & High-Quality Data

A high-quality prediction requires high-quality data. The data sets used for predictive analytics must be accurate, large, and relevant to the objectives. 

For the best results, organizations must have access to both historical data and real-time data, as well as both structured and unstructured data. 

To build a data set, organizations should extract data from all relevant sources, clean the data in preparation for analysis, and place that data inside a data warehouse. Or, data virtualization tools can aggregate data from disparate sources into one location.

For more information, also see: Four Pillars of a Successful Data Strategy: Making Better Business Decisions

4. Test Predictive Models First

Before using predictive analytics models to predict outcomes, they must be thoroughly tested or validated. Otherwise, predictions may be inaccurate and result in poor business decisions.

Organizations should run tests using sample data sets to determine the accuracy of predictions first. Once a predictive model is proven to be accurate, it can then be put to use.

5. Put Insights to Use

After testing and deploying predictive models, insights that are uncovered must be put to proper use. Organizations should document what occurs with insights and who’s responsible for employing them. 

Some questions to consider include:

• Who (individual, department, team, etc.) should insights be shared with? 
• What actions should be taken? 
• Are there insights that require immediate action? 
• Are there insights that should be revisited at a later time?

6. Make Continuous Improvements

Data changes over time and predictive models should follow suit. Organizations must monitor predictive model performance and make continuous improvements for the best results. This ensures models remain useful and accurate.

There are various ways organizations can improve their predictive models. For example, they can add more data to the model’s data set or re-tune, re-train, and re-test the model to determine areas that are in need of improvement.

7. Implement Predictive Analytics Software

The last step is to actually implement the software. There are a number of predictive analytics software tools that can be deployed. Examples include:

• Microsoft Azure Machine Learning: Microsoft’s Azure Machine Learning platform is one of the simplest tools out there for building and deploying predictive models. Its features include data labeling, integrations with other data tools, and support for hybrid and multi-cloud environments.

• H2O Driverless AI: The H2O Driverless AI platform is completely automated, making it unique to other tools on the market. For each data set, the software can automate processes such as tuning and deployment.

• SAS Advanced Analytics: SAS, a leader in analytics software, offers its Advanced Analytics platform to organizations that need a robust set of capabilities. The platform offers predictive modeling, data mining, statistical analysis, and more.

For more information, also see: Best Data Analytics Tools 

What Are The Three Main Predictive Analytics Models? 

There are three predictive analytics models that are most commonly used:

Classification: Classification models categorize data to show relationships within a dataset. These models are used to answer questions with binary outputs like “yes or no.” 

Clustering: Clustering models group data based on attributes without human intervention.

Time series: Time series models work to analyze data points that are collected over specific time periods, such as per hour or daily.

Once these models are planned, predictive analytics is quite simple. First, data is collected based on the type of prediction an organization wants to make. Then, one of these statistical models is developed and trained to predict outcomes using the collected data.

Once the model generates any kind of prediction, it can then be used to inform decisions. Through automation, some predictive models can even be instructed to perform actions based on predictions.

How Does Predictive Analytics Help Business?

Predictive analytics takes data analysis a step further. While basic data analysis can show us what happened and what to do about it, predictive analytics shows us what could happen and how we can intervene.

Predictive analytics offers a wide range of benefits across industries, from manufacturing to cybersecurity.

Reduces costly downtime in manufacturing

The average automotive manufacturer stands to lose $22,000 per minute during unplanned production downtime. Fortunately, through predictive analytics, manufacturers can make unplanned downtime a thing of the past.

Predictive analytics models can use historical data to find patterns that result in machine breakdowns, required maintenance, etc. Manufacturers can then mitigate risks before they result in costly downtime.

Improves patient outcomes in healthcare

The healthcare industry can benefit from predictive analytics in many ways. For example, predictive models can be used to determine a patient’s risk factors for diseases such as diabetes and heart disease. As a result, physicians can provide better preventative care.

Ensures product availability in retail

Retailers must be privy to what customers want to drive revenue. That’s why many retailers are turning to predictive analytics to improve product availability. 

For example, predictive models can predict which products that will be in higher demand during certain seasons. Retailers can then ensure they have adequate inventory to deliver on customer needs.

Predicts and minimizes risks in cybersecurity

Cyber attacks can be seriously damaging to any organization. According to research by IBM, the average data breach costs $9.44 million on average. Predictive analytics can support organizations in minimizing and even preventing damage. 

For example, predictive models can pinpoint trends that indicate potential risks. Organizations can then improve security in these areas to prevent attacks and data loss.

For more information, also see: Data Mining Techniques

Bottom Line: What is the Future of Predictive Analytics?

As mentioned earlier, the predictive analytics market is expected to grow quickly in the next five years. But what does the future look like?

Predictive analytics will continue to gain in popularity. And as technology such as machine learning and AI become more widely accessible, more organizations, both large and small, will be able to take advantage of predictive analytics.

Predictive analytics will lead the charge in pioneering the use of other forms of data analytics, such as prescriptive analytics. This method not only predicts outcomes but instructs organizations on the actions they should take in relation to the outcomes.

What comes next? For now, organizations must develop data analytics strategies that fit their goals and make room for new, forward-looking analytics methods as they evolve.

Also see: What Is Descriptive Analytics?

The post Predictive Analytics Best Practices appeared first on eWEEK.

By any measure, artificial intelligence (AI) has become big business.

According to Gartner, customers worldwide will spend $62.5 billion on AI software in 2022. And it notes that 48 percent of CIOs have either already deployed some sort of AI software or plan to do so within the next twelve months.

All that spending has attracted a huge crop of startups focused on AI-based products. CB Insights reported that AI funding hit $15.1 billion in the first quarter of 2022 alone. And that came right after a quarter that saw investors pour $17.1 billion into AI startups. Given that data drives AI, it’s no surprise that related fields like data analytics, machine learning and business intelligence are all seeing rapid growth.

But what exactly is artificial intelligence? And why has it become such an important — and lucrative — part of the technology industry?

Also see: Top AI Software 

And: What is Generative AI? 

What Is Artificial Intelligence?

In some ways, artificial intelligence is the opposite of natural intelligence. If living creatures can be said to be born with natural intelligence, man-made machines can be said to possess artificial intelligence. So from a certain point of view, any “thinking machine” has artificial intelligence.

And in fact, one of the early pioneers of AI, John McCarthy, defined artificial intelligence as “the science and engineering of making intelligent machines.”

In practice, however, computer scientists use the term artificial intelligence to refer to machines doing the kind of thinking that humans have taken to a very high level.

Computers are very good at making calculations — at taking inputs, manipulating them, and generating outputs as a result. But in the past they have not been capable of other types of work that humans excel at, such as understanding and generating language, identifying objects by sight, creating art, or learning from past experience.

But that’s all changing.

Today, many computer systems have the ability to communicate with humans using ordinary speech. They can recognize faces and other objects. They use machine learning techniques, especially deep learning, in ways that allow them to learn from the past and make predictions about the future.

So how did we get here?

Also see: How AI is Altering Software Development with AI-Augmentation 

A Short History of Artificial Intelligence

Many people trace the history of artificial intelligence back to 1950, when Alan Turing published “Computing Machinery and Intelligence.” Turing’s essay began, “I propose to consider the question, ‘Can machines think?'” It then laid out a scenario that came to be known as a Turing Test. Turing proposed that a computer could be considered intelligent if a person could not distinguish the machine from a human being.

In 1956, John McCarthy and Marvin Minsky hosted the first artificial intelligence conference, the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI). It convinced computer scientists that artificial intelligence was an achievable goal, setting the foundation for several decades of further research. And early forays into AI technology developed bots that could play checkers and chess.

The 1960s saw the development of robots and several problem-solving programs. One notable highlight was the creation of ELIZA, a program that simulated psychotherapy and provided an early example of human-machine communication.

In the 1970s and 80s, AI development continued but at a slower pace. The field of robotics in particular saw significant advances, such as robots that could see and walk. And Mercedes-Benz introduced the first (extremely limited) autonomous vehicle. However, government funding for AI research decreased dramatically, leading to a period some refer to as the “AI winter.”

Interest in AI surged again in the 1990s. The Artificial Linguistic Internet Computer Entity (ALICE) chatbot demonstrated that natural language processing could lead to human-computer communication that felt far more natural than what had been possible with ELIZA. The decade also saw a surge in analytic techniques that would form the basis of later AI development, as well as the development of the first recurrent neural network architecture. This was also the decade when IBM rolled out its Deep Blue chess AI, the first to win against the current world champion.

The first decade of the 2000s saw rapid innovation in robotics. The first Roombas began vacuuming rugs, and robots launched by NASA explored Mars. Closer to home, Google was working on a driverless car.

The years since 2010 have been marked by unprecedented increases in AI technology. Both hardware and software developed to a point where object recognition, natural language processing, and voice assistants became possible. IBM’s Watson won Jeopardy. Siri, Alexa, and Cortana came into being, and chatbots became a fixture of modern retail. Google DeepMind’s AlphaGo beat human Go champions. And enterprises in all industries have begun deploying AI tools to help them analyze their data and become more successful.

Now AI is truly beginning to evolve past some of the narrow and limited types into more advanced implementations.

Also see: The History of Artificial Intelligence 

Types of AI

Different groups of computer scientists have proposed different ways of classifying the types of AI. One popular classification uses three categories:

1. Narrow AI does one thing really well. Apple’s Siri, IBM’s Watson, and Google’s AlphaGo are all examples of Narrow AI. Narrow AI is fairly common in the world today.
2. General AI is a theoretical form of AI that performs most intellectual tasks on par with a human. Examples from popular movies might include HAL from “2001: A Space Odyssey” or J.A.R.V.I.S. from “Iron Man.” Many researchers are currently working on developing general AI.
3. Super AI, which is also still theoretical, has intellectual capacities that far outstrip those of humans. This kind of artificial intelligence is not yet close to becoming a reality.

Another popular classification uses four different categories:

1. Reactive machines take an input and deliver an output, but they do not have any memory or learn from past experience. The bots you can play against in many video games are good examples of reactive machines.
2. Limited memory machines can look a little way back into the past. Many of the vehicles on the road today have advanced safety features that would fall into this category. For example, if your car issues a backup warning when a vehicle or person is about to pass behind your car, it is using a limited set of historical data to come to conclusions and deliver outputs.
3. Theory of mind machines are aware that human beings and other entities exist and have their own independent motivations. Most researchers agree that this kind of AI has not yet been developed, and some researchers say that we should not attempt to do so.
4. Self-aware machines are aware of their own existence and identities. Although a few researchers claim that self-aware AI exists today, only a handful of people share this opinion. Developing self-aware AI is highly controversial.

While these classifications are interesting from a theoretical standpoint, most organizations are far more interested in what they can do with AI. And that brings us to the aspect of AI that is generating a lot of revenue — the AI use cases.

Also see: Three Ways to Get Started with AI 

AI Use Cases

The possible AI use cases and applications for artificial intelligence are limitless. Some of today’s most common AI use cases include the following:

• Recommendation engines — Whether you’re shopping for a new sweater, looking for a movie to watch, scrolling through social media or trying to find true love, you’re likely to encounter an AI-based algorithm that makes suggestions. Most recommendation engines use machine learning models to compare your characteristics and historical behavior to that of people around you. The models can be very good at identifying preferences even when users aren’t aware of those preferences themselves.
• Natural language processing — Natural language processing (NLP) is a broad category of AI that encompasses speech-to-text, text-to-speech, keyword identification, information extraction, translation and language generation. It allows humans and computers to interact through ordinary human language (audio or typed), rather than through programming languages. Because many NLP tools incorporate machine learning capabilities, they tend to improve over time.
• Sentiment analysis — AI can not only understand human language, it can also identify the emotions underpinning human conversation. For example, AI can analyze thousands of tech support conversations or social media interactions and identify which customers are experiencing strong positive or negative emotions. This type of analysis can allow customer support teams to focus on customers that might be at risk of defecting and/or extremely enthusiastic supporters who could be encouraged to become advocates for the brand.
• Voice assistants — Many of us interact with Siri, Alexa, Cortana or Google on a daily basis. While we often take these assistants for granted, they incorporate advanced AI techniques, including natural language processing and machine learning.
• Fraud prevention — Financial services companies and retailers often use highly advanced machine learning techniques to identify fraudulent transactions. They look for patterns in financial data, and when a transaction looks abnormal or fits a known pattern of fraud, they issue alerts that can stop or mitigate criminal activity.
• Image recognition — Many of us use AI-based facial recognition to unlock our phones. This kind of AI also enables autonomous vehicles and allows for automated processing of many health-related scans and tests.
• Predictive maintenance — Many industries like manufacturing, oil and gas, transportation, and energy rely heavily on machinery, and when that machinery experiences downtime, it can be extremely costly. Firms are now using a combination of object recognition and machine learning techniques to identify in advance when equipment is likely to break down so that they can schedule maintenance at a time when it minimizes disruptions.
• Predictive and proscriptive analytics — Predictive algorithms can analyze just about any kind of business data and use that as the basis for forecasting likely future events. Prescriptive analytics, which is still in its infancy, goes a step further and not only makes a forecast, but also offers recommendations as to what organizations should do to prepare for likely future events.
• Autonomous vehicles — Most of the vehicles in production today have some autonomous features, such as parking assistance, lane centering and adaptive cruise. And while they are still expensive and relatively rare, fully autonomous vehicles are already on the road, and the AI technology that powers them is getting better and less expensive every day.
• Robotics — Industrial robots were one of the earliest implementations of AI, and they continue to be an important part of the AI market. Consumer robots, such as robot vacuum cleaners, bartenders, and lawn mowers, are becoming increasingly commonplace.

Of course, these are just some of the more widely known use cases for AI. The technology is seeping into daily life in so many ways that we often aren’t fully aware of them.

Also see: Best Machine Learning Platforms 

The Future of AI

So where is the future of AI? Clearly it is reshaping consumer and business markets.

The technology that powers AI continues to progress at a steady rate. Future advances like quantum computing may eventually enable major new innovations, but for the near term, it seems likely that the technology itself will continue along a predictable path of constant improvement.

What’s less clear is how humans will adapt to AI. This question poses questions that loom large over human life in the decades ahead.

Many early AI implementations have run into major challenges. In some cases, the data used to train models has allowed bias to infect AI systems, rendering them unusable.

In many other cases, business have not seen the financial results they hoped for after deploying AI. The technology may be mature, but the business processes surrounding it are not.

“The AI software market is picking up speed, but its long-term trajectory will depend on enterprises advancing their AI maturity,” said Alys Woodward, senior research director at Gartner.

“Successful AI business outcomes will depend on the careful selection of use cases,” Woodware added. “Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders.”

Organizations are turning to approaches like AIOps to help them better manage their AI deployments. And they are increasingly looking for human-centered AI that harnesses artificial intelligence to augment rather than to replace human workers.

In a very real sense, the future of AI may be more about people than about machines.

Also see: The Future of Artificial Intelligence

For more information about artificial intelligence, also see: 

What is Generative AI? 

The AI Market: An Overview 

Cloud and AI Combined: Revolutionizing Tech 

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I spoke with Ajay Bhatia, Global VP & GM of Digital Compliance at Veritas, about how companies can remain data compliant even as AI chatbots ingest – and reproduce – ever more data.

Among the topics we discussed: 

• What are the key risks to companies in terms of chatbot-related data compliance?
• What advice do you give to companies to remain compliant, long term?
• How is Veritas addressing the digital compliance needs of its clients? What services are the most important in this area?
• The future of chatbots and compliance? Where is the sector headed?

Listen to the Podcast:

Also available on Apple Podcasts

Watch the video:

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The new Lenovo ThinkEdge servers and enhancements to Lenovo Open Cloud Automation and Lenovo XClarity that the company announced at the recent Mobile World Congress (MWC) event are good examples of how a vendor can help customers overcome major challenges. Let’s take a look at the solutions.

For more information, also see: Understand the Differences Between 5G, WiFi 6 and WiFi 6E

ThinkEdge SE10 and SE10-I: Small and Rugged

Lenovo’s ThinkEdge edge computing portfolio includes numerous solutions and form factors. They range from core data centers to remote or branch office (ROBO) local/closet data centers to far edge servers designed for particular environments.

So far as form factors go, the company’s new ThinkEdge SE10 and SE10-I (industrial) are its most compact far edge servers yet. Clocking in at 0.8 L, for the SE10, and 1.4 L for the SE10-I (industrial), these fanless solutions are designed to securely collect analog data, such as information collected from temperature and air flow sensors, and to support business equipment, like digital signage and kiosks.

The two solutions use the same Intel ATOM Elkhart Lake processors and firmware and share many characteristics. However, the SE10-I solution is more ruggedized for cold/heat (-20C to 60C), supports IP50 dust/water resistance and supports more I/O ports.

During a briefing with analysts, Charles Ferland, Lenovo’s VP and GM of Edge Computing in the company’s Infrastructure Solutions Group (ISG) noted the difficulties that enterprises are having with deploying and managing edge computing at scale, due to the complexity of those environments and constraints in human and technical resources. He said that the design of the ThinkEdge SE10 and SE10-I, along with advancements in Lenovo’s Open Cloud Automation (LOC-A 2.6) and XClarity solutions, make them ideal solutions for a range of enterprise retail, manufacturing and smart building applications.

Ferland also described how one Lenovo customer, a retail grocery chain, is using the new servers to better monitor and manage the temperatures in its vegetable, meat/seafood and frozen food departments, thus reducing spoilage and improving energy usage.

Also see: What is Edge Computing

Automation and Cloud Orchestration at the Edge

How will Lenovo’s enhanced LOC-A 2.6 and XClarity offerings impact customers’ edge computing efforts?

LOC-A 2.6 is likely to make the greatest initial impact but requires some context to understand. In many or most edge environments, deployment requires trained IT staff to physically place individual servers and components. They then load firmware and software, and connect the system to the corporate network for final installation and updates. The process requires seamless product delivery, is time consuming and can be easily derailed by operator error.

Lenovo LOC-A 2.6 radically alters this process. First, the company ships blank ThinkEdge servers, without any firmware or software loaded, to the customer’s destination. There, staff unpack and place the servers where they are needed, then connect them to the physical infrastructure. Using a smartphone, tablet or laptop application, staff can securely activate the servers and connect/confirm them with Lenovo.

At that point, LOC-A 2.6 begins an automated download and configuration process and executes all necessary tasks in the right sequence so that the server becomes operational. Any necessary updates and patches are automatically installed so that within a few minutes or hours the server is ready to host its application(s).

Understandably, this remote activation reduces the time and resources that businesses need to put into deployment efforts and can dramatically decrease their carbon footprint. Additionally, the changes to Lenovo XClarity should significantly streamline and simplify edge computing management.

Once available only for Lenovo’s data center systems, the XClarity platform can now support the company’s entire ThinkEdge portfolio, including far edge solutions, like the ThinkEdge SE10 and SE10-I. As a result, businesses can better monitor and manage even the largest edge environments through a single pane of glass interface.

Also see: Top Edge Companies 

Final Analysis

Along with its new and improved edge computing solutions, Lenovo is also leveraging its AI Innovation Centers to educate and train customers and partners to gain the most from their edge computing investments.

As a result of these and other efforts, Lenovo is helping to substantially extend and expand the practical benefits of edge computing. Over time, we expect that Lenovo will continue delivering new innovations and improvements via its ThinkEdge portfolio and associated solutions and services.

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I spoke with Tarek Madkour, Director of Product Management at Databricks, about how to make “AI and ML readily useable by mere mortals.” What data management practices assist in this process?

Among the topics we discussed: 

• I see on your LinkedIn profile that you’re experienced in making “AI and ML readily useable by mere mortals.” How do you do this – how can mere mortals and average companies do this?
• You’ve mentioned that it’s “important to invest in the developer ecosystem and meet developers where they are.” Why is this important and how does it relate to data analytics?
• How is Databricks addressing the data needs of its clients? What’s the Databricks advantage?
• The future of the data warehouse and date lake? 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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I discussed the upcoming release of Wi-Fi 7 in my latest ZKast with David Coleman, Director of Wireless at the Office of the CTO at Extreme Networks, which already has Wi-Fi 7 products in the works.

Coleman explained what’s new in the standard and how it compares to Wi-Fi 6, Wi-Fi 6E, 5G, and private 5G. Highlights of the ZKast interview, done in conjunction with eWEEK eSPEAKS, are below.

For more information, also see: Understand the Differences Between 5G, WiFi 6 and WiFi 6E

The Enterprise Wi-Fi Market

The importance of Wi-Fi in society today can’t be overstated. Technically, it’s the access technology that connects devices to corporate and home networks, enabling fast and easy access to the Internet.

In the early days, Wi-Fi was used primarily by consumers at home and then made its way to the enterprise. Today, the technology has matured enough to be used in every vertical industry and has become the foundation for digital experiences – many of which start on mobile devices.

Over the years, Wi-Fi generations have evolved (and improved) to include new capabilities like faster speeds, less bandwidth congestion, and better battery life. Wi-Fi 6E extends the efficiency capabilities of Wi-Fi 6 to the 6 gigahertz (GHz) band, a multi-lane superhighway for high-bandwidth applications.

Despite Wi-Fi 6E being a fairly new standard, there’s an even newer standard on the horizon: Wi-Fi 7. As was the case with previous generations, Wi-Fi 7 promises major improvements over Wi-Fi 6 and 6E.

For more information, also see: WiFi 6 E is Critical to Healthcare Modernization

Interview and Highlights

See highlights of the video interview below:

• Wi-Fi 7, officially known as IEEE 802.11be, is still being finalized by the Wi-Fi Alliance, an industry organization that’s responsible for certifying interoperability between devices based on the standard. While the Wi-Fi Alliance announced plans to have a certification for Wi-Fi 7, the official date hasn’t been released. Like previous standards, Wi-Fi 7 will be backward compatible and coexist with legacy devices in the 2.4, 5, and 6 GHz bands.
• Wi-Fi 7 builds on Wi-Fi 6E by utilizing the 6 GHz band and increasing throughput even more. The new standard focuses on physical (PHY) and medium access control (MAC), capable of supporting a maximum throughput of at least 30 gigabits per second (Gbps). It can potentially reduce latency and jitter for time-sensitive apps like augmented reality (AR)/virtual reality (VR), 4K and 8K video streaming, as well as mission critical and industrial apps.
• Wi-Fi 7 is expected to enable new enterprise-grade services like multi-link operation (MLO), where multiple Wi-Fi links could be used to lower latency, increase reliability, and boost throughput. Theoretically, Wi-Fi 7 will cover three channels in all three bands. A Wi-Fi 7 client and Wi-Fi 7 access point (AP) could transmit on different channels in different bands through link steering. This could potentially provide high throughput and backhaul links, offering the most value for the enterprise.
• Another unique feature of Wi-Fi 7 is 4K QAM modulation, which boosts peak rates and increases throughput/capacity compared to Wi-Fi systems using 1K QAM modulation. These complex modulation methods require a pristine radio frequency (RF) environment and higher signal-to-noise ratio (SNR). Wi-Fi 5 required an SNR of about 25 decibels (dB) and Wi-Fi 6 required about 32 dB. Now with the 4K QAM modulation, there needs to be an SNR of about 41 to 42 dB, which is difficult to achieve in 5 GHz.
• Wi-Fi 7 also supports the ultra-wide 320 megahertz (MHz) channel, which has a throughput that’s four times greater than 80 MHz and two times greater than 160 MHz. The bigger the channel, the more data it can modulate. While it sounds great in theory, this doesn’t scale in the enterprise. With multiple APs in use, enterprises are likely to experience channel interference.
• Most of the hype around Wi-Fi 7 is its ability to provide high speed multi-gigabit internet connections. Wi-Fi 6 has a maximum theoretical speed of 9.6 Gbps, while Wi-Fi 6E offers the same speed with the added advantage of the 6 GHz band. Wi-Fi 7 can potentially reach 33 Gbps, although it’s not likely. Realistically, it’s going to be less than that when Wi-Fi 7 is deployed in actual enterprise environments with multiple clients and APs.
• Some vendors have already announced Wi-Fi 7 chipsets and radios, including Broadcom and Intel. The chipsets are currently being tested and incorporated into future products. The first Wi-Fi 7 consumer-grade home routers are likely to launch between March and May of this year. Enterprise-grade products are expected to follow, probably in the first half of 2024. None of these products are certified by the Wi-Fi Alliance since there isn’t yet a certification.
• Any company that’s currently in a refresh cycle should be thinking beyond Wi-Fi 6. Those who aren’t planning another refresh for five to six years will have no path to 6 GHz. Moving to Wi-Fi 6E now will ensure access to the 6 GHz superhighway of spectrum. Yet, organizations that recently deployed Wi-Fi 6E aren’t likely to upgrade to Wi-Fi 7 right away. Wi-Fi 6E and Wi-Fi 7 will be a replacement for those who currently have Wi-Fi 5 or Wi-Fi 6.
• Upgrading switches now is also a good idea for future proofing, but this can be done gradually over time. Despite the promise of faster speeds provided by Wi-Fi 7, organizations should primarily focus on their individual use cases and enhancing the wireless user experience.

For more information, also see: Artificial Intelligence Ease Network Challenges

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I spoke with Jack Parmer, Product Strategy Advisor at Domino Data Lab, about why “code first” is so effective, and his view on the future of data science.

Among the topics we discussed: 

• What does it mean to take a “code first” approach to data science? What’s the difference between this and a low-code approach?
• What are the most common obstacles that data scientists encounter as they use AI/ML tools? How do you recommend they address these issues?
• The Domino Data Labs offering? What’s the Domino advantage in the data sector?
• The future of code first and/or low-code? What do you see in the years ahead?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

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Generative AI is a type of artificial intelligence that uses unstructured deep learning models to produce content based on user input. As part of this process, generative AI uses a foundation of machine learning and deep learning algorithms. The content it creates includes written materials, images, video, audio and music and computer code.

For example, when a human types a question or statement into ChatGPT – a pioneering example of generative AI – it delivers a brief but reasonably detailed written response. A user can also enter follow-up questions and engage in an ongoing conversation with the chatbot, which can remember details from earlier in the conversation.

Generative AI has recently garnered attention because major breakthroughs in the field are accelerating. For example, OpenAI’s ChatGPT can generate grammatically correct text that appears to be written by humans, and its DALL-E tool can produce photorealistic images based on word input. Others companies, including Google, Facebook and Baidu, have also developed sophisticated generative AI tools that can produce authentic-looking text, images or computer code.

For more information, also see: Understanding the ChatGPT AI Chatbot

How Does Generative AI Work? 

Generative AI creates new content based on a training set. Researchers feed enormous volumes of data—words, pictures, music or other content—into a deep learning system called a Generative Adversarial Network(GAN) framework. The supervised neural network sifts through the data and uses a system that rewards successes and penalizes errors, mistakes and failures, advances. Over time and with human oversight, it learns how to identify and understand complex relationships.

The original OpenAI Codex used for ChatGPT, for instance, is derived from more than 700 gigabytes of data collected from the Web and other sources. This includes content from books, magazines articles, websites, technical manuals, emails, song lyrics, stage plays, scripts and other publicly available sources. Natural language models like ChatGPT typically rely on mathematical models called word vectors to weight and rank phrases. They also rely on a technique called Recognizing Textual Entitlement (RTE) to better understand word relationships, such as they’re, their and there or whether there are strong winds or the road winds.

Entailment, Contradiction, or Neutrality

As researchers add data to a natural language model like ChatGPT or LaMDA and additional training takes place, the system continues to compare and contrast words through a lens of entailment, contradiction, or neutrality.

For instance, the premise “A dog has legs” entails “legs have feet,” but contradicts “dogs swim under the sea,” while remaining neutral to a statement like “all dogs are good.” As the system runs through millions of combinations, it learns how to build an accurate and contextually correct predictive model. 

According to OpenAI, researchers fed more than 300 billion words into the actual ChatGPT model. Initially, human AI trainers provided input for both sides—as a user and as an AI assistant (generator and discriminator). Humans then reviewed randomly selected model-written messages, ranked various completions from the model, and fed them back into the GAN to further train the reward model. The result was a reasonably accurate reinforcement learning algorithm that, with additional training and user input, continues to improve over time.

Researchers use similar techniques to classify pictures of birds, clouds, trees, faces, cars, and millions of other objects. Over time, the reward model is updated and refined—and it continues to produce more realistic details. In fact, models like DALL-E and Google’s MiP-NeRF produce highly detailed effects, including shadows, color gradients and textures. This makes things such as a stone surface or water shimmering on a lake look remarkably realistic.

For more information, also see: AI and Deep Learning

What Does Generative AI Do?

Generative AI and other foundational AI models are dramatically influencing the development of AI, boosting assistive technology and enabling powerful capabilities for nontechnical users. This includes content creation from text to code to images to music.

Text 

OpenAI is far from the only company to develop natural language chat capabilities. Google’s LaMDA and Bard, Apple’s Siri, Microsoft’s Cortana, and Amazon’s Alexa all generate written or spoken words through the use of Generative AI models.

Images

 Other Generative AI tools, such as DALL-E and Google’s MiP-NeRF, can generate photorealistic images based on word input. For instance, a web designer might type the words “classic Spanish plaza” into the DALL-E engine and view an image that looks incredibly real—though it doesn’t represent any actual place. Likewise, a person might ask DALL-E to produce an image of a woman sitting at a café in the style of Monet and nearly instantly view an image that looks like it was produced by the artist.

Music 

Generative AI is also used to produce audio and music—including full-fledged compositions and specialized sound effects. Several companies, including Amper Music, Aiva, Amadeus Code, Google Magenta and MuseNet are capable of generating original music with multiple realistic-sounding instruments. A user can request a genre, artist or style—say jazz, Mozart, the Rolling Stones or upbeat—and listen to the resulting AI generated composition.

Software Development

 Another burgeoning use case for Generative AI is software development. Platforms such as Amazon’s CodeWhisperer and GitHub’s CoPilot introduce natural language-based low-code and no-code platforms for developers. With Generative AI, a software developer can speak or write a request into a platform and view actual lines of software code in Python, R, Java or other major languages. This allows developers to work faster and create reusable modules more easily.

Story and Game Development

More advanced use cases revolve around things like story and game development, robotic designs and even debugging products or operational methods by asking questions and probing a topic. What’s more, by asking the Generative AI tool to provide ideas and concepts, it’s possible to explore themes and even develop new and different digital and physical objects. 

How Can Businesses Use Generative AI?

The history of AI and business is filled with innovation, disruption and profound changes. Generative AI promises to lead organizations down the same path. Some of the leading use cases for generative AI in the business world include:

• Marketing and sales. Generative AI systems can produce a variety of written content for emails, website text and images, brochures, eBooks and product guides, product labels and internal documents. Organizations can also use the technology to analyze customer feedback, identify risks and opportunities, and deploy highly usable and functional chatbots.
• Human resources. HR departments can tap generative AI to write an enterprise handbook, job descriptions and interview questions. A chatbot can deliver information and self-help for employees. This might include automating on-boarding or providing options and advice for choosing healthcare insurance or a retirement savings strategy.
• Operations. Customer service chatbots can help companies manage inquiries and direct people to the right information—and hand them off to an agent when it’s beneficial. Generative AI can also identify errors, defects and other problems through comparative images. For example, a company might use generative AI to create an ideal image of a highly technical component and then capture images during manufacturing to ensure they adhere to quality control standards.
• IT and software development. Generative IT can write code in modern languages such as Python, Perl, Go, PHP and JavaScript. Development teams can plug these snippets and blocks into software—and store them in libraries. Likewise, Generative AI can autocomplete data tables and produce synthetic data that can improve the accuracy of machine learning models. The technology can also simulate cyberattack methods.
• Other business uses: Numerous other business use cases exist. These include modeling systems used for research and development (R&D); reviewing text in documents to ensure they meet legal and regulatory standards; and optimizing and improving general employee communication, including emails and business presentations.

For more information, also see: What is AI? 

Generative AI: A Brief History

Artificial intelligence research began to take shape during the 1950s. Alan Turing and other scientists began to explore ways to create computing frameworks that could duplicate human thinking.

By the 1960s, so-called Markov models began to appear. These probability-based algorithms could generate speech or text based on basic mathematical models—though with limited success. By the 1990s, more sophisticated generative models began to appear. Over the last decade, GPUs and advances in deep learning have ushered in far more advanced AI. Today, these recurrent neural networks can generate content in a way that approximates—and in some cases exceeds—human artists, musicians and writers.

At this point, artificial intelligence – particularly generative AI – is fundamentally reshaping the way people and businesses act, interact and process information. 

Market research firm Grandview Research projects that the Generative AI market will grow by 34.4% annually through 2030. It says that the technology has value across a wide swath of industries, including finance, healthcare, automotive and transportation, information technology, telecommunications, and media and entertainment. Generative AI can transform tasks as wide ranging as marketing, image classification and quality control.

In fact, Gartner has proclaimed that Generative AI technology will revolutionize digital product development. The consulting firm reports that by 2025 about 10% of all digital content will derive from these algorithms. No less important, McKinsey & Company reported, Generative AI will fundamentally change job roles along with the way people work. It noted: “The rise of generative AI has the potential to be a major game-changer for businesses.”

On a related topic: The Future of Artificial Intelligence 

What Ethical Concerns Exist About Generative AI? 

Not surprisingly, the rise of Generative AI has unleashed concerns. One fear is that the technology will replace humans for many job functions. However, the technology—at least for the next several years—will more likely serve as a complement to humans.

Lack of Accuracy

For instance, ChatGPT text requires human review because it isn’t always complete and accurate. Blindly plugging in text could lead to a variety of problems, ranging from accusations of bias to legal issues.

Legal Issues and Plagiarism 

Likewise, an enterprise must take caution about what types of music, images or other materials derive from Generative AI. Because these models are built from actual content produced by writers, musicians and painters, they can raise questions about ownership, control and copyright.

For this reason, generating a photorealistic image that’s similar to the specific style of an artist could raise questions—and even lead to a lawsuit or public backlash. There are also growing concerns about the technology being used for deepfakes and as a way for students to avoid writing essays and papers.

Privacy and Security

Privacy and security concerns are also at the forefront of Generative AI. Some data used to build models may inadvertently contain private data and information that might later be exposed. Equally concerning: cybergangs and other criminals have already begun to use Generative AI to produce highly convincing documents, software and images that become part of social engineering campaigns.

Societal Impact 

Another issue is the overall societal impact of Generative AI, particularly tools like ChatGPT and Bing’s AI chat feature (which is built on the ChatGPT framework). Some observers, such as New York Times technology columnist Kevin Roose, have raised concerns about the technology being used to manipulate humans—including in harmful and destructive ways. In addition, they have voiced concerns about the technology carrying out its own dangerous acts. 

For more information, also see: AI Software and Tools 

Bottom Line: What is the Future of Generative AI?

While nobody can predict the exact trajectory of generative AI, it’s clear that it will make a profound impact on businesses—and society. Clearly, as the technology advances, the capabilities will expand. Within a few years, the technology may well be capable of writing full-fledged reports and scientific papers as well as producing mockups for websites and other design materials. 

Years from now, it’s possible that Generative AI will produce better final drafts than professional writers and generate better art and design elements than professional human artists and graphic designers. More advanced Generative AI may also be able to entire computer applications, video games, movies and other complex elements with little or no human supervision. It’s likely that voice assistants like Siri and Alexa will be capable of handling far more sophisticated functions—such as planning a vacation or buying birthday gifts for a family member.

If Generative AI can match or exceed human performance for many tasks, the nature of work—and roles within organizations—will change dramatically. Some roles and job functions will disappear, while new roles will likely replace them. However, this displacement could rival or even exceed past events, such as the Industrial Revolution. In addition, society will have to sort through a variety of issues—including ethical and legal concerns—in order to fully benefit from the technology. 

In the end, one thing is certain: Generative AI is here to stay. As neural nets and GPUs continue to advance and AI algorithms become more refined, the ability of machines to perform human tasks will increase. Whether Generative AI will lead to singularity, the hypothetical point where AI exceeds human intelligence, remains to be seen. However, it’s entirely clear that generative AI is poised to change the nature of business—and the world around us.

For more information, also see: The AI Market: An Overview  

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I spoke with David Reber, CSO of Nvidia, about how the modern cybersecurity sector is defined by “AI vs. AI.”

Among the topics we discussed:

• In the world of cybersecurity, it appears that AI is a tool used by both sides – it’s similar to an escalating arms race. Can you talk about how AI is shaping the security sector?
• If both sides have AI, how can companies get the better hand in protecting themselves? What are your recommendations?
• How do Nvidia’s AI offerings serve the cybersecurity sector?
• The future of AI and cybersecurity? What do you foresee and how can companies get ready now?

Listen to the podcast:

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Data governance refers to the people, processes, and policies that are in place to effectively create, use, store, and secure company data. Data governance is critical for any organization harnessing the power of big data.

Data governance is essential for business because simple, semi-organized data management is no longer enough in today’s marketplace. Instead, organizations must put strict processes into place to ensure complete control over their data, including how it’s used, stored, accessed, and maintained.

What are the Elements of Data Governance?

The elements of data governance comprise the people, process and policies that allow an organization to effectively manage all of its data flow. 

• People: Data governance begins by defining which team members are responsible for managing and championing governance policies and processes. In addition to managing the governance software, these governance professionals ensure that policies are defined, procedures are sound, and data is protected. Data governance experts identify these data professionals as the most important element of the governance process.
• Processes: Data governance processes outline the proper actions necessary to create, store, secure, and access data throughout an organization. These processes include everything from data collection and analysis to the specific steps employees must take to access critical data remotely. For proper data governance, each process must be documented and followed by those involved with the data.
• Policies: While processes are the actionable steps organizations must take, policies are the standards developed for an organization’s data. Specific policies are required for all aspects of data management. For example, an organization should have policies that discuss the appropriate use of data as well as policies that detail overarching security standards.

For more information, also see: The Role of Data Governance in Effective Data Management

The Goals of Data Governance

Data governance goals may differ between organizations. However, some goals are universal. According to the Data Governance Institute, the most important objectives include:

• Build standard, repeatable data processes.
• Enable better decision-making.
• Train management and staff to adopt common approaches to data issues.

Other goals of data governance include improving compliance with data security standards, ensuring consistent data across the organization, and securing data to minimize risks.

For more information, also see: The Data Security Market: Key Strategies

What are the Benefits of Data Governance? 

While the global data governance market was valued at $1.8 billion in 2020, it’s expected to grow at a CAGR of 22% to reach $7.2 billion by 2027.

This growth is not only driven by organizations’ need to prioritize data but also by the many benefits data governance provides, including helping to improve decision making, creating better strategic plans, and improving data security.

Improves decision-making

One of the primary reasons organizations are inspired to use data is for visibility into everything from their daily operations and finances to their marketing campaigns. This visibility helps them make the educated business decisions required to scale.

However, a lack of data processes and procedures often results in low-quality data that can’t be trusted or easily accessed in real time. Data governance eliminates these challenges, so stakeholders can make decisions that push the business forward.

Simplifies strategic planning

For data-driven organizations, data plays a key role in business strategic planning. For example, data enables stakeholders to see current business performance and forecast future performance.

Unfortunately, many organizations struggle with data silos or a repository of data that’s inaccessible to all teams and departments. And in many cases, there is no single source of truth. 

This results in stakeholders making plans without seeing the whole picture. Data governance ensures data silos are nonexistent and that data is visible to all. The result is a clear image of an organization’s past and current performance that can be used for effective planning.

Improves data security

The average cost of a data breach within the US is $9.44 million. The global average is $4.35 million. To avoid these serious consequences, organizations must take data security seriously, and that means ensuring policies are in place for a fast response.

Effective data governance includes policies and procedures that dictate what should be done in the event of a breach or data loss. With these policies documented, organizations can move quickly to secure their data. Plus, proper data security policies can help prevent risks in the first place.

Enables compliance

Regulations such as the US Federal Trade Commission Act and the EU’s GDPR require organizations to protect the personal data of their customers. However, when data is not governed, this can be difficult.

Data governance helps organizations remain compliant with these regulations by enabling data transparency and minimization, among other requirements.

For more information, also see: Why Organizations Need to be Data-Centric

The Challenges of Data Governance

Even though data governance simplifies planning and decision-making while ensuring data is secure and compliant, it isn’t without its challenges, some of which include volume of data, data silos, and establishing organizational buy-in. 

Data Volume

Each day, quintillions of bytes of data are created across the globe. Organizations generate data from a wide range of sources, such as their CRM platforms, financial transactions, and customer communications. The sheer volume of data can feel overwhelming to manage, let alone properly govern.

Data Silos

As mentioned above, organizations just beginning their data journey often deal with data silos. For example, an HR department may have data that the finance department doesn’t have access to. These silos make governing data impossible by threatening data integrity and limiting visibility.

Organizational Buy-in

Just like any business initiative, data governance requires buy-in from all stakeholders and teams to be successful. But governance isn’t easy and may require culture change. It also requires all departments to collaborate and be willing to share information. It’s common for organizations to experience growing pains at the start.

5 Steps to Build a Data Governance Strategy

The above challenges can be avoided through the development and use of a solid data governance strategy. Although each organization’s strategy will differ depending on its needs and goals, there are five critical steps any business should take to get started.

1. Choose a Data Leader

Any business initiative requires a champion to lead the charge. A data  leader ensures new policies and procedures are followed. This individual should be knowledgeable in data management and governance and passionate about ensuring all data is handled with care.

2. Take Inventory of Your Data

To properly manage and govern data, organizations must know what data they have and where it comes from. Organizations should begin by completing an inventory of all data and its sources. This process involves many steps, such as cataloging data assets and assigning metadata for easier organization.

3. Document Data Processes

Organizations should also document processes to guide the correct use of data. This includes everything from data analysis to access and beyond. For example, what processes should be in place to ensure data is only shared with those who need it? What processes are in place to safeguard data in the event of a breach?

4. Adopt a Data Governance Framework

Organizations can choose between various data governance frameworks to jumpstart their efforts. These frameworks act as guidelines to support organizations in developing effective data governance strategies. 

Some examples of common frameworks include the Eckerson framework and the DGI Data Governance Framework. Organizations should choose a framework that fits their unique needs and goals.

5. Develop Policies & Continuously Revisit Them

Finally, organizations must develop data governance policies. These policies are standards created to communicate the key processes and team responsibilities required to govern data. Some examples of policies may include data usage or access.

Policies should be revisited often to ensure they reflect any changes within the organization, such as updates to data processes or job role changes.

Also see: Four Pillars of a Successful Data Strategy: Making Better Business Decisions

Examples of Data Governance Tools

Although data governance is complex, there are tools and platforms available that can support businesses in their efforts. Here are three of the top options for organizations seeking to establish effective governance in their operations: 

SAP Master Data Governance

SAP is a leader in enterprise software. The SAP Master Data Governance software provides a unified view of an organization’s data. SAP’s platform features key capabilities such as data consolidation, central governance, and data quality analytics.

Alation

Alation offers robust data governance solutions that enable visibility into all data assets. Alation is unique in its ability to automate certain data governance tasks such as data stewardship, classification, and more.

Ataccama One

Ataccama provides a suite of tools made to improve the process of governing organizational data. For example, through the data discovery solution, organizations can classify data into a single catalog for easy visibility. Other tools include data profiling and automated data quality monitoring.

Bottom Line: Implementing Data Governance for Better Operations

Launching a data governance strategy will almost certainly be challenging for many companies. Processes and data repositories that were once managed on an informal basis will require sustained effort – and a clearly documented plan – if they are to conform to an optimal data governance strategy. 

Yet however challenging, a well-constructed and strategic data governance policy is crucial for competitive advantage because disorganized, unstructured data management will clearly lead to business losses. Therefore, each enterprise must create a data governance strategy to control all elements of company data, including how it’s managed, mined, analyzed, protected, gathered, and stored. 

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Mobile World Congress (MWC) kicks off this week in Barcelona. The theme of the event is telco transformation – as it has been for some time.

Clearly, the evolution of telcos is long overdue. Most communication and network service providers have networks that rely too heavily on people, spend too much on infrastructure and deliver services too slowly to compete with the cloud companies. This leads to their net promotor and customer service scores being abhorrently low. This is why this group of companies is often relegated to being the “plumbing” of company infrastructure.

On a related topic: The Future of Artificial Intelligence 

Modernization Critical to Long Term Success

Digital transformation has given telcos an excellent opportunity to change the way they are perceived. The building blocks of digitization are technologies like cloud, IoT and mobility and these all have one thing in common – they are network-centric, making the network more valuable.

In a recent survey of mine, I asked business leaders their opinion of the value of the network today versus pre-pandemic and two-third believe the network to have increased in business value, putting the telcos in a strong position to help their customers transform.

To accomplish this though, telco modernization must happen. At MWC, this opinion was shared with everyone I spoke with. However, it is a fact that we have been talking about the need to evolve for over a decade. This large a shift requires new hardware, software and a completely different operating model. While the vendor community has done a nice job of evolving equipment, it’s also true that labor and skill shortage has remained a problem. But the rise of artificial intelligence (AI) could change that.

AI: Core Component of Telco Modernization

AI can revolutionize the telecom industry by transforming all aspects of network planning and deployment, operations, and customer engagement. Although the technology is still in the early implementation stage, telcos are starting to invest significantly in AI, according to a new report released by NVIDIA titled State of AI in Telecommunications: 2023 Trends.

NVIDIA surveyed more than 400 telecom professionals globally to evaluate the opportunities and challenges of implementing AI in this industry. The respondents included C-suite leaders, managers, developers, and IT architects from mobile, fixed, and cable companies. The survey was conducted between mid-November 2022 and mid-January 2023.

The report uncovered that most (95 percent) telecom professionals are engaged with AI, which is even higher among industry decision-makers (100 percent). Despite the high interest in AI, however, many companies still haven’t widely adopted AI. Only 34 percent of the respondents said they’re using AI for more than six months. Meanwhile, 23 percent are trying to figure out different options for AI, and 18 percent are in the pilot phase.

For more information, also see: Top AI Software 

AI’s Impact on Optimizing Operational Costs

Operational optimization could be the biggest opportunity for AI in the telecom industry. Approximately 60 percent of the respondents believe AI automation that replaces or augments human effort will drive efficiency. Additionally, 44 percent expect AI to reduce the cost of operations and 35 percent expect AI to enhance customer engagement.

For companies that are already implementing AI, it has had a positive impact on both revenue and cost.

AI Leads to a Boost in Revenue

In the last year, 73 percent of the respondents saw their revenue increase after implementing AI and 17 percent reported revenue gains of more than 10 percent in specific parts of their business.

Furthermore, 80 percent of the respondents said their annual costs decreased in the last year and a smaller number of the respondents (15 percent) reported a reduction above 10 percent in parts of their business.

A leading challenge for widespread adoption of AI in the telecom industry is the inability to quantify its return on investment (ROI), according to 44 percent of the respondents. Despite AI having a positive impact on revenue and cost, there seems to be a disparity between expectations and reality when implementing AI-based solutions.

Other challenges cited by telecom professionals include a lack of skilled personnel, poor infrastructure, and not enough funds for AI. For instance, 34 percent of the respondents said their company has an insufficient number of data scientists, while 33 percent said they don’t have a budget for AI projects.

For more information, also see: Digital Transformation Guide

AI Investment Needs to Increase

Given the challenges, investment in AI appears to be low, where 50 percent of telcos are spending less than $1 million annually. In comparison, telcos typically spend at least a billion dollars annually on capex.

The report’s findings show that the level of investment in AI doesn’t match the level of enthusiasm and engagement. Only 42 percent of the respondents agreed that their current investment is at the right level.

However, the outlook for 2023 is positive, with 47 percent of telecom professionals planning to increase their AI investments.

Although nearly half (49 percent) are still in the learning, assessment, or trial/pilot phase, telcos are determined to move toward implementing AI. Data shows telcos are testing various AI-enabled solutions for network operations, base station site planning, truck-routing optimization, and machine learning data analytics. Many are also focused on improving the customer experience by implementing engines, virtual assistants, and digital avatars.

In the next six to 12 months, telcos plan to increase investment in predictive maintenance and use AI to tackle problems in their hardware and software infrastructure, including customer on-premises equipment like mobile devices and set-top boxes. Machine learning (ML) and high-performance computing platforms seem to be the leading AI tools of choice. Telcos are also investing in deep learning, digital twins, and simulation for the purpose of testing infrastructure upgrades in the virtual world before rolling them to customers.

Today, AI is deployed mostly in hybrid environments (cloud/on-premises) and telcos are happy with this model. But there is a shift happening to all-cloud for companies that are seeking improved performance of cloud solutions. The biggest concerns for companies that are worried about moving to all-cloud include information security (35 percent) and network stability (32 percent). Cost wasn’t named as a priority when choosing a deployment environment for AI, suggesting that telcos are taking more of a strategic approach rather than a cost-driven one.

Going forward, partnerships will play a critical role for telcos implementing AI. Telcos will need reliable, knowledgeable partners for supplies, support, and expertise to accelerate adoption. In fact, 83 percent of the respondents said they use third-party consultants or software vendors for AI projects. Additionally, partners will help develop new services, and as a result, allow telcos to deliver better customer experiences.

Also see: Top Digital Transformation Companies

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