I spoke with Rajiv Ramaswami, President and CEO of Nutanix, about key trends and issues in multicloud computing, including the future of this emerging technology.

Among the topics we addressed:

• We know that the Nutanix Enterprise Cloud Platform delivers the advantages of public cloud to company data centers. The Nutanix solution lives in on-prem datacenters, right?
• Nutanix is focusing on multicloud, which is a rapidly growing sector. What are some typical challenges for companies using multicloud, and what advice do you give to address them?
• Will there ever be a “single pane of glass” for a company to manage multiple clouds?
• What do you see as the future of multicloud? And how can companies prepare now?

Listen to the podcast:

Watch the video:

The post Nutanix CEO Rajiv Ramaswami on the Future of Multicloud appeared first on eWEEK.

One-off sustainability initiatives and short-term goals just don’t make the cut in today’s ESG-focused market. Consumers now demand greater transparency into business operations, supply chains, and social impacts. Additionally, they’re on the lookout for organizations making investments into the circular economy; that is, the infrastructure dedicated to recycle and reuse.

In fact, a survey found that 66 percent of US consumers believe it is an organization’s responsibility to demonstrate their ESG performance. It’s important to remember that some of these consumers are C-level executives at your customer and prospect companies.

But it’s not just consumers out on the hunt; investors are also insisting on better ESG policies from prospective organizations. Stakeholders want to see detailed plans on how organizations will commit to sustainability initiatives – and more crucially, how they intend to measure and deliver on these.

Also see: Digital Transformation: Definition, Types & Strategy 

The Race to Net Zero Has Begun

In the wake of the pandemic, organizations with flexible and transparent ESG strategies have been able to pivot with the changing landscape. However, remaining agile means demonstrating tangible progress, not just written plans. Research suggests that it is essential we achieve net zero emissions by 2050. While many organizations have vowed to make decarbonization a priority, many have yet to take action to reach the net zero goal.

The Race to Zero is a global campaign designed to gain support and action from businesses, leaders, and investors for a zero-carbon economy. This initiative was spotlighted at the UN Climate Change Conference of the Parties (COP26), an event designed to boost real change for sustainability.

Understand What ESG Means to Your Customers

Actions speak louder than words, and it’s up to organizations to prove that they are doing what they’ve stated they will. There is no ESG strategy that will suit all organizations across the board. So businesses need to ensure that their unique sustainability plans not only accommodate what they believe in, but what their customers believe in, too—which can vary from region to region.

Research from GreenBiz highlights the different focus areas each region worldwide associates most with sustainability. For example, in North America, consumers associate sustainability most with recycling. Therefore, investment into the circular economy will be vital to align strategies with local sentiment and ensure the actions of the organization resonate well with the domestic audience.

Also see: Top Digital Transformation Companies 

Tech’s Pivotal Role

Waste and pollution reduction, product and material circulation, and nature regeneration are the premise of a circular economy model,  and they are difficult to achieve without technological investment.

Climate tech solutions come in many forms, including Application Programming Interfaces (APIs), Internet of Things (IoT), cloud computing, and Software as a Service (SaaS). Investing in these technologies and harnessing their capabilities, such as enhanced product flow and traceability, has enabled organizations to increase waste management control and raise accountability.

As a result, an increasing number of investors recognize the need for climate tech. The market for climate related technology has been thriving; in the first 6 months of 2021, $14.2 billion was invested into climate tech worldwide—this is 88% of the total investments made in 2020, according to research from Pitchbook.

While there are still some businesses who have not yet adopted such a “track, trace, and think” methodology, future-focused companies have already recognized and capitalized on the supply chain traceability, company profitability, and many other benefits that are provided by disruptive technologies and a circular economy.

Everyone on Board – Including the Board

Historically, executives that empower the rest of the organization to be proactive and strategic have enjoyed the most employee engagement and have been the most successful in making an impact when it comes to sustainability. Creating a corporate-level sustainability agenda will keep the whole organization on track to reaching their sustainability goals.

Corporate goals and objectives will be pivotal in establishing an effective ESG strategy, but so will the efforts of everyone else in the business. Incorporating a sustainable culture into the organization – and taking time to celebrate and acknowledge the wins – will ensure everyone is motivated to play their own role in making a difference. Ultimately, to succeed in meeting ESG goals, everyone must be on board to collaborate across their business ecosystem—from stakeholders to customers to partners.

The move toward better sustainability practices is more urgent than ever. That’s why events such as COP26 are pivotal in raising awareness, not only within the hearts and souls of people, but within businesses, nations, and leaders around the world. The time truly is ticking by, yet the transition is doable, and the technology is already there to lend a digital hand to those that wish to pledge their commitment.

Also see: Predicting Cloud Trends for 2022: Migration, Multicloud and ESG 

About the Author: 

Cindy Jaudon is President of IFS North America

The post The Urgent Need to Align Business Strategy with ESG appeared first on eWEEK.

Multicloud is one of those topics that most cloud providers would rather not discuss. Why? Well, you’re admitting that their cloud is not the only destination for your applications and data. Also, and most important, they will be forced to work and play well with other cloud providers and do so as a stated policy.

Sadly, if you’re moving down a multicloud path, which most enterprises are these days, you’re mostly on your own in terms of how you put together an optimized multicloud architecture. It’s no real surprise that a lot of people are making a lot of mistakes right now, and this is leading to multicloud failures that cost enterprises millions of dollars.

The Flexera 2021 State of the Cloud report released states that 92% of enterprises have a multicloud strategy. This ranges from those who have begun their journey to those who are just entering the planning stages. So you’re not alone in your struggle: Managing complexity across different cloud providers is the core battle that enterprises are now facing.

Managing complexity across different cloud providers is the core battle that enterprises are now facing.

Why We Multicloud

For those of you who would consider leveraging a single cloud deployment to overcome the challenges of multicloud, you are effectively losing the core business benefits of leveraging a multicloud deployment. These benefits include:

The ability to select best-of-breed technology

AWS may have the best data analysis system for your inventory management system. However, Google may have a better AI platform for your needs, and Microsoft may have a SaaS system that you want to utilize.

We all want to find an optimized solution that leverages the best technology to fit the specific needs of the business. A multicloud deployment typically offers the lowest cost and most business-optimized solution—at least, conceptually. 

Also see: AWS vs. Azure vs. Google Cloud

Opportunities for operational cost efficacy

All public clouds have different pricing options. Either the price is higher or lower, or more likely, the way they bill for services may favor some use cases but not others.

Take data ingress and egress, for example. Cloud providers are all over the place on what they charge to simply bring data into your cloud-based data stores or transmit the data out of the cloud. The way they bill could be for data sent and received, the bandwidth used, or the time expended.

This means your configuration will have advantages with some clouds, but not with others. Given that this could be a bill that moves to well over $500k per year and is likely to increase as the business grows, these are major considerations. Therefore, it is important to consult with your cloud provider for specific policies and pricing.

Pricing analysis then moves on to storage pricing and policies, such as leveraging reserved instances to save some money. Other areas of pricing you should consider include how compute is charged and special services such as artificial intelligence and analytical systems. All are different, and all should be considered along with the business requirements. Then, consider again which cloud is the most economical to meet a specific business need.

Removing single vendor dependency

Many point to multicloud as being a way to avoid vendor lock-in. While using a single vendor can certainly have its advantages, if you write your applications to leverage whatever native cloud API is provided by only one of your cloud providers, you’re pretty much locked in. Multicloud does little to solve this problem other than allowing you to get locked into more than a single vendor, but the limitations are much the same, if not worse.

if you write your applications to leverage whatever native cloud API is provided by only one of your cloud providers, you’re pretty much locked in.

On the other hand, there is a notion that having the ability to leverage more than one vendor provides a bit of operational and business leverage. For example, the ability to have a pre-established relationship with more than one public cloud provider allows you to make better choices around the use of core cloud services, such as storage and compute. You even have options available if one of the vendors does not provide good terms or good service.

The same can be said around operational issues. For example, let’s say your primary cloud storage provider is not living up to SLAs (service-level agreements). With a multicloud deployment, you can easily move to another place, which can reduce both risk and cost.

Also see: Private Clouds Remain Central in a Multicloud World

Emerging Multicloud Best Practices

While multicloud deployments are relatively new, enterprises are already beginning to gather some best practices regarding multicloud solutions. These are best practices that most cloud providers would rather you not know about or leverage.

It’s not in cloud vendors’ best interests to spend billions of dollars to push enterprises to other vendors’ clouds. Their dollars get spent attracting enterprises to their cloud and no other. Thus, this is becoming a path to cloud where enterprises are largely on their own.

So, when it comes to cloud computing, these emerging best practices are about acting and thinking more independently than you have in the past.

And more important, you must understand that cloud is not a “one size fits all” type of technology deployment. A cloud deployment needs to be a decision that considers all aspects of your business and uses best practices to find the most optimized multicloud solution. You must weigh cost efficiencies and complexities and the cloud platform’s ability to meet the needs of your existing and future business.

Here are some of the best practices that most often drive multicloud success:

1. Consider the resulting complexity of your multicloud deployment

There are a number of technologies deployed across a multicloud deployment, and all need to be understood regarding function, correct integration, and long-term operations and support.

With cloud services such as storage, compute, AI, serverless deployment and more, it can be difficult to figure out how to configure and leverage these services optimally for your business. 

With a single cloud deployment, you limit the number of technologies you can leverage, but most of them are purpose-built to work well together. With a multicloud, multiply the number of clouds you leverage by a multiplier of the different technologies available. For example, a single cloud might have 5 different native storage solutions (e.g., block, object, file, etc.), but you could have as many as 30 when you leverage three public clouds as part of your multicloud.

a single cloud might have 5 different native storage solutions (e.g., block, object, file, etc.), but you could have as many as 30 when you leverage three public clouds as part of your multicloud.

Let’s say you leverage 10 of the 30 possible storage solutions, for use with different applications and data stores. Don’t forget that you’ll need to hire and train for the additional technologies, as well as operate them using CloudOps staffers who understand how each works, as well as expertise for more specialized ops tooling. You can count on spending as much as twice or three times the budget for operations than when deploying on a single cloud provider.

Most enterprises won’t spend that kind of money to deal with multicloud complexity, which means complexity must be mediated during the design phase of your multicloud. You have to plan ahead for operations.

You can also mediate much of the complexity by using advanced operational tools, such as AIOps, where you can leverage abstraction and automation as ways to deal with complex operations with fewer humans. While this can lead to a reduced cost compared to traditional methods of handling complexity, operational tools like AIOps typically operate best as a built-in feature and will cost much more if integrated later. 

Also see: The Future of CloudOps: Big Challenges and Possible Solutions

2. Consider multicloud cost governance

Just as too much architectural complexity leads to cost overages, not having a solid grip on the consumption of cloud services across a multicloud deployment will lead to cloud bills that are higher than any benefits multicloud can provide.

Cost governance has other names, such as FinOps, which basically leverages cost monitoring technology to monitor what cloud services are being consumed by what or who as well as how much they cost over time. The trouble is that many who deploy multicloud often rely on the cloud native cost monitoring tools from each provider. While these are fine when you leverage a single cloud solution, they are overly complex to deal with if you leverage two or more clouds – and often lead to costly mistakes.

The trouble is that many who deploy multicloud often rely on the cloud native cost monitoring tools from each provider.

The new generations of cloud governance tools should have the ability to monitor the ongoing usage of cloud resources as well as set limits on usage, provide charge-back and show-backs to budget per organization. They should provide deep data analytics to determine how the spending will likely change over time. This includes what-if analysis to consider the cost of leveraging different cloud services, including more complex usage billing, such as ingress and egress charges, and leveraging discounted services that are purchased ahead of need.

3. Consider what’s between the clouds rather than what’s in them

What’s important about multicloud is not what runs within each cloud, at least conceptually; it’s about what runs between them. This includes all common services that should span the entirety of the multicloud, including security, governance, operations, etc. This includes all services that should be the same between clouds and that will operate as “cloud agnostic” or “cross-cloud” functioning technology.

This can be a bit confusing because these technologies may operate within a specific cloud provider as a third-party application. However, they are purpose-built to deal with all the clouds within your multicloud deployment as if they were functionally operating between them on an independent platform.

These third-party technologies that can span clouds may include cross-cloud security managers that leverage a common identity directory, cost governance as covered above, and operational tools such as AIOps that span all clouds. And any other services that are common to all clouds and should run above all clouds rather than as a specific tool native to a single cloud.

Also see: AIOps Trends

Learning Multicloud by Trial and Error

Core to the best practices presented here is the ability for enterprises to create their own path to multicloud. Yes, it would be nice if there were a single solution pattern that fit all enterprises when it comes to the use of multicloud deployments. In the real world, each architecture needs to be purpose-built for the enterprise that leverages it.

The good news? Some common best practices are beginning to appear. It’s important that you learn as much as you can from the trials and errors of those who came before you, and then, come up with your own specific solutions. Those who follow the best practices mentioned in this article are more likely to find multicloud success.

The post Best Practices for Multicloud (that Cloud Providers Prefer You Not Know) appeared first on eWEEK.

5G is taking the world by storm for its notable speed and bandwidth strength. It supersedes 3G, which gained popularity in the early 2000s after the iPhone 3G was released, followed by 4G in 2011. 4G and 4G LTE are still heavily relied upon, but 5G is becoming more available for enterprises, small businesses and everyday consumer use.

Although 5G is hailed for its low power consumption and increased interconnectivity, adopting a 5G network does come with some degree of risk. Businesses should be aware of the potential increased risk and cybersecurity concerns that the 5G network may introduce.

Also see: Cybersecurity Risks of 5G: How to Control Them 

Faster Networks Provide Ideal Attack Conditions

The perfect scenario for a hacker is a vulnerable device connected to a low latency, high-speed network because it responds faster, thus improving the speed at which security weaknesses are identified and exploited. Traditionally, this type of threat was limited primarily to devices connected via terrestrial networks. The new generation of 5G connectivity aims to provide a similar latency and throughput speed as fiber optic internet connectivity.

While 5G itself is not a security threat, a device connected to a 5G network is not offered additional security. In fact, the higher throughput speed offered by 5G acts as a double-edged sword.

5G is theoretically 10 to 100 times faster than its 4G predecessor, meaning any 5G connected device will be far more appealing to a cybercriminal in terms of an attack target. Cybercriminals want quick wins; therefore any device that responds rapidly is a more attractive target over a device that originates from a high latency, low throughput network.

On systems where a large cache of data exists, the ideal situation for an attacker is to find it connected to a high bandwidth network capable of significant data throughput, enabling data exfiltration to occur faster. The alternative method more frequently used by attackers is to upload ransomware onto the target, which then proceeds to encrypt all valuable data in a manner that enables the attacker to extort a ransom in return for a decryption password.

More Bandwidth Means More Vulnerable Devices and Data

As IoT technology and edge computing continues to spread exponentially into both consumer and industrial devices, their dependency on agile connectivity will also increase. 5G offers increased speed and greater concurrency making it possible for a larger scale of critical IoT devices to stay connected at one time.

With 5G enabling heavier loads of devices to be connected, it naturally leads to higher volumes of data being transmitted, shared, and potentially compromised through undiscovered device vulnerabilities. Where such devices are involved in the collection and processing of personal information, any subsequent security breach may result in the exposure of health care files, banking transactions and sensitive customer data.

Consumer privacy will be an ongoing concern for businesses. Staying aligned with global data privacy laws allows businesses to avoid increasing financial penalties issued by regulators. Moreover, protecting sensitive information bolsters consumer trust and creates a strong, long term customer relationship that enables organizations to use data privacy as a competitive advantage. 

A Future Enabled by 5G: Risk vs. Reward 

5G offers near-instantaneous communications for current and next-generation devices such as smart cars, drones and endless other applications that drive our society towards a modern future.

While this new level of connectivity may introduce additional risks, it also offers substantially more rewards where risks can be managed with proactive mitigation and awareness of the data on every connected device. Ultimately, the purpose of an attacker looking for devices connected to high-speed, low latency networks is to find weaknesses to steal or leverage the valuable data within these devices.

The world’s thirst for high connectivity will never stop and as the world migrates towards 5G technology, the next conversation will migrate towards what the next technology successor will be. However, the associated security risk themes will always remain similar in that these risks exist and require controls to mitigate them.

Guard Your Data Against 5G’s Threat

Despite whether your organization’s computing devices are in the field connected via 5G, via a local area corporate network or if they exist remotely within an employee’s home office environment, the approach to tracking and mitigating associated risk remains the same. Regardless of your industry, a fundamental approach to tracking an organization’s risk from 5G starts with the data being stored, transmitted or processed.

Establishing the types of data being handled and where that data resides, whether within on-premise devices, cloud providers, or remote employee laptops provides a key baseline to data risk awareness.

A standard technology utilized to establish awareness of data within modern security-aware organizations is data discovery. This technology crawls across every type of data within servers, laptops, cloud and email systems looking for every hidden instance of personal, sensitive and confidential data.

Irrespective of your chosen technology approach, the key principle is establishing an awareness of data, including where it is, what it is, and establishing an ongoing plan to regularly review and remediate any new findings.

About the Author: 

Stephen Cavey, Co-founder and Chief Evangelist, Ground Labs

The post An Overlooked Cybersecurity Threat: 5G appeared first on eWEEK.

If we track the recent progress of the Chief Information Security Officer (CISO), there’s good reason to wonder if they are headed toward the visibility once reserved for CEOs, given how today’s dramatic security challenges have boosted their profile.

In a relatively short time, we’ve seen cybersecurity move from being an afterthought to become central to business operations. It really wasn’t until the very end of the millennium when the Melissa virus, coupled with the fear of Y2K disasters, launched “hacking” and data security into the public perception.

Since that time—a mere 20 years ago—we’ve seen a rapid evolution of the role of the CISO from a back-office controls and risk mitigation function to one of the most influential voices in the boardroom. CISOs are responsible for guarding against attacks that are not only costly in terms of revenue but also brand reputation.

In an era of rapid digital transformation, the role of the CISO has shifted to that of an “enabler,” helping companies securely move at the speed of the market. It’s not a stretch to assume that as the significance of the role continues to increase, so too will the public interest in the people holding these roles.

In fact, we’re already starting to see this shift as CISOs are increasingly being called upon to serve as thought leaders and experts in the eyes of external stakeholders.

Taking Center Stage: A Challenging Balance

Just as many brands have benefitted from the robust personalities of their CEOs, there is a corresponding argument to be made that putting the CISO front and center can be beneficial.

Data security remains a polarizing topic. According to a recent survey from KPMG, 67% of the U.S. general population say they want more transparency around how their personal data is being used by companies. And 40% say they would willingly share their personal data if they knew exactly how it would be used—and by whom. 

Similarly, in a “show, don’t tell” era, consumers may place more trust in an organization if they feel they know the person behind ensuring their data-safety. Humanizing the function by putting a name, face, and personality behind security and privacy measures can help convince consumers that the organization is truly, personally invested in securing their information.

But such exposure comes with its own set of risks. Elevating and celebrating the CISO could give cybercriminals an extra incentive to target the company—looking to specifically take down that figure.

Also see: 5 Cloud Security Trends in 2022

Best Practices for Today’s CISO: Earning Trust

Whether a good or bad idea, CISOs and aspiring CISOs would do well to prepare for the eventuality of life in the public eye.

Use your Personal Brand for Good

The most important aspect of building your personal brand is understanding its purpose. Why are you building your brand? What are you hoping to accomplish? Almost invariably, the answer is to build stakeholder trust.

Always Work Through the Lens of Trust

Trust is earned in drips but lost in buckets. The unavoidable truth is that—if you’re a public figure—there is no such thing as off the record. You have to proceed under the assumption that the mic is always hot, and the camera app is always on “record.”

Before you speak, post, or act, ask yourself: Will this inspire trust or erode it? By the same token, remember that if the goal is to build trust, you need to maintain an open and honest approach with your audience.

Choose your Platform

Even though it’s called a “personal brand,” the lion’s share of your content will center around your professional expertise. As you endeavor to stand apart from the pack of fellow CISOs and would-be-CISOs, you’ll want to focus on educating a wider audience on a topic you feel is very important and yet not understood by many.

Set your Own Boundaries

If you find yourself asked to be a public figure on behalf of your company, remember that “showing your whole self” is a sliding scale. It does not mean you need to tweet that back-to-school picture of your fourth grader.

It might mean sharing some snaps of your new puppy if you’re comfortable with that. Or it might mean sharing a hobby that you’re passionate about. Remember, the goal is to help your audience understand the real you—but you decide where to let them in.

You Can’t Fake It

Creating a persona that is not true to you is a recipe for failure. It is not sustainable, and the world has become too interconnected with too many people having a microphone for you to successfully present a lie.

All it takes is one viral post from a friend or acquaintance who truly knows you to blow your cover, and in doing so destroy any trust you’ve cultivated.

Seek Expert Help

You’re a CISO because you are an expert in information security—and that is where your focus can and should remain. When it comes to building and maintaining your brand, seek out the experts. If your company is pushing you to be “more public-facing,” ask what resources are available to you to help create content, maintain social media engagement, and secure (and prepare for) traditional media opportunities.

According to a report from Grand View Research, the global cybersecurity services market size is expected to reach USD 192.70 billion by 2028. As the field continues to expand, we may well see the day when it’s commonplace for CISOs to be Twitter verified.

Get ahead of the game by taking steps today to ready your personal brand—but never forget that the goal is not to get famous. Rather, it is to further business objectives and results by building, maintaining, and growing stakeholder trust.

Also see: Cybersecurity in 2022: Solving the Skills Gap 

About the Author: 

Prasad Jayaraman is a Principal in KPMG’s Advisory Services

The post The Successful CISO: How to Build Stakeholder Trust appeared first on eWEEK.

I spoke with Michael Liebow, Global Head of Atos OneCloud, about how legacy architecture plays such a core role even in today’s newest cloud deployments, and provides advice on optimizing cloud strategy

• Your new position with Atos OneCloud? What will you be doing?
•  Let’s contrast the past with what’s currently happening. Cloud in years past could be one data center connecting with one cloud provider, a simple hybrid cloud. Now, it’s a multicloud scenario, with microservices and container management systems, with the cloud merged with an edge deployment. Complexity rules. How can companies cope with all of this?
• What’s the most typical pain point for companies and cloud? It seems like it might be planning – they’ve set up deployments without quite knowing what they were doing, and now they’re stuck with architecture that’s problematic. Agree, disagree?
• Your take on the future of multicloud?

Listen to the podcast:

Watch the video:

The post Atos OneCloud’s Michael Liebow on Cloud Computing Challenges and Solutions appeared first on eWEEK.

The networking landscape has shifted dramatically over the past two years as remote work, cloud migration, and container-based architectures have augmented network infrastructure. This digital transformation has put added pressure on NetOps teams to gain visibility into on-premises, cloud, and hybrid environments to ensure performance of the entire network and applications, regardless of location. 

As a result, traditional network performance monitoring (NPM) tools are no longer enough for organizations that want to proactively plan, monitor, and optimize their network services or that want to find and fix network performance problems quickly.

In fact, according to Gartner’s Market Guide for Network Performance Monitoring 2021, “by 2025, 60% of organizations will have seen a reduction in traditional network monitoring tool needs due to increases in remote work and cloud migration, as compared to 2021.” 

In essence, organizations can no longer afford to have visibility gaps across infrastructure. To overcome this challenge, they need to ensure their infrastructure is equipped to handle issues both on and off premises as well as leverage a combination of data sources to provide a holistic end-to-end view of the entire network. 

How is this done? Let’s dive into more details on the state of NPM and how it’s adapting to offer cloud and hybrid visibility.

Also see: The New Focus on CloudOps: How Enterprise Cloud Migration Can Succeed 

Combining Data Sources to Identify Problems

NPM tools leverage a combination of data sources, including network-device-generated traffic data; raw network packets; and network-device-generated health metrics and events to monitor, diagnose, and find performance issues. This includes giving NetOps teams forensic data to identify the root cause of performance issues and insights into the end-user experience.

Traditional NPM tools focus on the core network and data center, capturing information from within the traditional network perimeter. The traffic types, flow rates, and traffic patterns are almost a known factor except for network anomalies that happen.

Customers with on-premises network designs have a robust, scalable, and stable environment and, with the help of firewalls, reliable WAN edge devices and other network components. NPM tools are 99% aware of the possible issues that could happen, and they monitor and act based on this analysis.

With customers migrating to cloud and container-based architectures (and other technologies like SD-WAN and microservices), it’s become more difficult to capture traffic and isolate problems. Today, organizations need tools that can monitor LAN, WAN, and into the cloud, so let’s dive into each area that’s impacting this shift around visibility.

Redesigning Networks for Remote Work

It’s no secret that the remote workforce has resulted in organizations redesigning network infrastructure, but these changes often don’t account for monitoring or visibility.

For example, with the increase in the remote workforce, the number of connections coming in through the VPN concentrators or firewall has increased tremendously for most large enterprises. These devices and network designs must be redesigned to accommodate this increase in scale, throughput, and number of user access licenses. 

With more remote workers, enterprises are looking for NPM tools that can monitor and analyze traffic patterns, utilization, and application monitoring from the VPN concentrators. NPM tools are now available to read useful data (Flow, SNMP, API, etc.) from these devices to help analyze and monitor remote user traffic.

Cloud Migration and Cloud Native Computing

Cloud migration has been happening for years, but due to recent events, has been accelerating at a staggering pace. The drive toward more cloud-enabled applications and services, which may not be owned by the organizations, further complicates monitoring and troubleshooting.

For example, there are cloud solutions offered by multiple cloud companies (like Google, Amazon, Azure) that also provide services and applications hosted on their portal or managed services by other vendors.

With this mix of vendors, the type of useful readable data for NPM tools to understand and monitor can be challenging. But NPM tools are now able to capture raw cloud data and convert to readable IPFix data, or via API to create useful reports for monitoring and analysis.

Some NPM tools have global or private agents deployed in the cloud at various sites, which make use of synthetic traffic for monitoring and analyzing network SLAs. Cloud vendors are also trying to add more ways (advanced API, service tags, etc.) for raw data to be easily accessible for NPM tools.

Container-Based Architectures

Containerization and microservices allow an organization to package software and its dependencies in an isolated unit, either on-premises or in the cloud. Having visibility into these services is crucial for managing performance across a user base, but it’s fundamentally different due to changes in traffic flow. 

NPM tools need to be able to access the raw data, read this information, and analyze useful information from these containers and microservices to export this data into useful user reportable format. Each vendor has their own way to implementing how the containers or services are hosted on-premises or in the cloud, and the format in which raw data can be accessed. Most vendors have APIs to get access to this data; it’s just a matter of NPM tools to implement this API format to fetch this data.

Different cloud solutions have different ways the end-to-end solution is hosted. For example, deploying an NPM tool and reading raw data from Amazon AWS is completely different from hosting the tool on a Google GCP platform. The data formats provided by different vendors have different data variables, service names, region formats, etc. There are also large new enterprise private cloud solutions coming up (like SAP or Salesforce private cloud solutions), which add to the complexity of read data from hybrid cloud-hosted enterprises.

The shift to cloud and the changing network architecture is putting a premium on NPM solutions that work across all these technologies. This means capturing data that includes streaming telemetry, flow data, packet data, and SNMP, which can be used for such outputs as predictive analysis, real-time monitoring, AI/ML-assisted analytics, and historical analysis. 

Enterprises now need to think about how NPM tools are planning to address new hosted technologies and migration from an on-premises design to a complete cloud solution.

About the Author: 

Jubil Mathew is a Technical Engineer at LiveAction

The post Why NPM Tools Need to Work Across On-Prem, Cloud, and Hybrid Environments appeared first on eWEEK.

As we move into 2022, there’s little doubt that we’ll see the growth in intelligent automation (IA) continue to gather momentum as more decision makers realize the transformative power of this emerging technology. Building on this, a key shift we’ll see is the realization that only a truly unified workforce can deliver a business’s full potential. A unified workforce is one where humans and IA – intelligent digital robots – work as a single cohesive construct.

We’ve now moved past the fear paradigm of seeing digital labour as a risk and instead see it as a partner, or collaborator, and as a means to excel. As mindsets around digital transformation and the future of work progress, so will the technology itself. There has been an impressive growth in the capabilities of intelligent automation in recent years, boosted by advances in artificial intelligence (AI) and complementary technologies.

Moreover, robotic process automation (RPA) has become a key platform and business enabler for AI as businesses leverage AI to increase the sophistication of what can be automated and to support more complex automated interactions between digital workers and human workers and customers. 

Throughout 2022, these developments will accelerate even further, continually adding to digital robots’ skill sets. The embedding of AI and machine learning (ML) into digital robots will enable businesses to make automations faster and cheaper to program and execute. This will increase the scope over which they can work, and ease the path toward being able to orchestrate digital robots as they expand across the enterprise. This, in turn, will lead to rapid scaling of automation programs to play increasingly strategic roles in businesses.

Autonomous Intelligent Automation: Business Uses

As intelligent automation becomes more “intelligent,” we’ll gradually be able to see other changes in the way the technology is used. For example, vendors will move toward developments such as autonomous IA, a form of intelligent automation that can be self-defining, self-managing, and self-healing. 

The lines between digital process automation (DPA), iPaaS, no code, robotic process automation, and other forms of automation are blurring too. So we will most likely see a future trend toward IA platforms that provide the means to create, manage, and control operations that leverage multi-modal automation across both humans and digital robots.

Shifting mindsets and advancing technology might be heading this way, but what does this mean to businesses? Greater use of intelligent automation will increasingly lead to improved business outcomes for specific target markets and industries – see below. Solutions will become more tailored toward particular industries to deliver their own strategic business outcomes. These might include:

• Reduced labour costs
• Increased quality and compliance
• Improved customer and employee experience
• Stronger business resilience
• Greater agility in competitive markets

Intelligent automation will continue to become increasingly strategic—more focused on key corporate goals such as competitiveness, revenue growth, customer service, and market growth.

Also see: What is AIOps?

Select Industries on the Forefront

It’s become clear that intelligent automation is applicable to almost all sectors but, heading into 2022, growth is expected to be particularly strong in a handful of industries.

Retail has only scratched the surface when it comes to intelligent automation, which offers a hugely efficient way to boost sales, increase customer engagement, and reduce costs.

Manufacturing will leverage intelligent automation to improve customer experience – a trend well under way – and help to swiftly adapt to regulatory changes, and manage complex supply chains. 

Healthcare is clearly seeing accelerated usage of intelligent automation. Organizations have recognized its strength in improving interoperability and back-office efficiencies; providing financial stability; enhancing workforce satisfaction; and, most importantly, elevating patient experience.

Utilities are primed for strong growth in IA. Higher customer expectations, adaptations to tackle climate change, meeting regulatory compliance, aging infrastructure, and workforce are all factors that will drive utility businesses towards the use of intelligent automation throughout 2022.

Also see: Top Digital Transformation Companies 

A Digital-First Strategy

More than anything, businesses will realize the need to make intelligent automation a strategic priority. Already, tactical and quick-win automations are a thing of the past, so businesses need to go further than they currently are in order to achieve the maximum ROI.

Organizations will approach their challenges at the highest level, orchestrating and rearranging the future management of work, leveraging all business application interfaces – be they legacy applications, modern apps or APIs – to work for their strategic business goals. 

However, the deployment and integration of digital robots into the existing workforce is only half the story here. Organizations will give more consideration to how IA can augment their human counterparts’ workloads, enabling them to do more. As intelligent automation’s presence grows within organizations, companies will put greater emphasis on how this new unified workforce is orchestrated for competitive advantage. 

In sum, businesses will feel less limited by organizational constructs by completely reimagining processes with a digital-first mindset.

Also see: DevOps, Low Code and RPA: Pros and Cons

About the Author: 

Eric Tyree is the head of AI and research at Blue Prism

The post What Does 2022 Hold for Intelligent Automation? appeared first on eWEEK.

At the core of digital transformation lies data democratization, the practice of making data accessible throughout all departments of a business, not simply the C-suite and IT team.

Datasets are growing fast. Forget data analytics – business leaders are struggling just to visualize and map out data to begin with. Data visualization works to fix this.

Also see: Top Data Visualization Tools

What is Data Visualization?

Data visualization is the graphical representation of datasets and information. Data visualization is an umbrella term for visualizing all types of data through charts, graphs, and maps.

The ultimate goal is to visually represent your data in an accessible and easy-to-understand manner. Visualizing data is a fundamental step in understanding trends, uncovering patterns, and tracking outliers.

Experts predict that the data visualization market will expand greatly within the next five years, with compound annual growth rates of over 10%, growing to a $19.2 billion market valuation by 2027. Although the rise of data democratization and digital transformation are causes of this rise, much of this growth is due to growing interest in business intelligence.

What are the Benefits of Data Visualization?

There are three key benefits to data visualization:

1. Making Big Data Digestible

There is no question that datasets are growing tenfold. Combine this with the growing advancement of IT systems that are increasingly more advanced, and you have a colossal pain point for businesses.

The fact that data is becoming so overwhelming for organizations has spurred the rise of AIOps. AIOps helps businesses with various use cases such as:

• Predictive alerting
• Root cause analysis
• Prioritizing events
• Predictive outages
• Service desk ticketing

How does this relate to data visualization? Data visualization tools are an effective way to map out data in an automated manner – and this automation is core to AIOps. This hands-off approach makes approaching large datasets rather easy to gather, and then in turn to digest using data visualization techniques.

2. Greater Accessibility

We mentioned how large datasets are now accessible for a greater number of users. This demonstrates that data visualization is a key factor in data democratization.

Data visualization tools help simplify complex robust data points and present them in highly digestible ways. This growing accessibility can help upskill employees and make businesses more efficient.

3. Greater Efficiency & Understanding

Traditional means of sifting through data were meticulous and time-consuming. Data visualization helps businesses discover insights at a much faster rate than prior to the advent of visualization tools.

Speed is central here. This growing scalability means that business leaders have more room to be granular in their analysis. If data is mapped out more quickly, IT teams and data scientists have more time to draw more complex insights from their well-organized databases.

Prior to data democratization, gaps in communication were all too common for enterprises and businesses alike. Boiling down and explaining advanced insights can be difficult without a common understanding of what the datasets behind these insights mean. With modern data visualization software, such as Tableau and Microsoft Power BI, data analysis is broadened to virtually any department within your organization.

Also see: Best Business Intelligence Tools 

What are the Different Types of Data Visualization?

Here are examples of various forms of data visualization and uses cases:

Bar graphs: These types of graphs are best utilized to compare aspects of different groups or to track those aspects over time. Bar graphs are best used when changes are rather large.

Line graphs: One of the most popular and fundamental forms of data visualization are line graphs, which are used to track changes over short and long periods of time. Line graphs are particularly useful to highlight smaller changes.

Graphs are, for the most part, are rather modular. Above is an example of a line graph tracking bounce rates; this is in contrast to a bar graph that represents page load times.

Pie Charts: Another fundamental form of data visualization, pie charts are effective for comparing parts of a whole. Because they are not placed on an X-Y plot, tracking data over time is not possible with a pie chart.

These charts are very basic examples of data visualization. Many modern tools are designed to open up complex methods to the everyday user. For instance, here’s one way a management trainee in the finance industry used data visualization software for their needs:

“Power BI is a widely used software in our organization where we deal with a huge amount of raw data and process it to gather actionable insights. It helps us to visualize scattered and unfiltered information efficiently and easy to understand manner…Overall, I would say that this is a must-have software for any enterprise that directly witnesses a lot of data being gathered to formulate strategies and plan of actions” – Management Trainee in finance industry, review of Microsoft Power BI at Gartner Peer Insights.

Scatter Plots: A slightly more advanced data visualization method is scatter plotting. Scatter plots are an effective way to explore the relationship between two variables and multiple sets of data. Below is an example of a scatter plot mapping out profitability in various American cities. Note how cities with larger profitability have larger circles.

As you can see above, data visualization tools can map out different graphs on the same page to help present holistic insights more clearly. Advanced software solutions will let users build interactive graphs that help stakeholders understand insights even more deeply.

Also see: Top Data Mining Techniques 

Data Visualization Best Practices

Keep in mind that the main goal behind data visualization is to present data in a more interesting, relevant, and accessible manner to diverse audiences. Each best practice is specifically designed ultimately for this outcome.

Best practices include:

Choose the Optimal Chart and Graph: Make sure to consider your needs before selecting the proper way to visualize your data. Ponder whether you want to track data over time or how many variables you intend on analyzing. These questions can help your decision making process.

Use Templates: Many data visualization tools offer templates that businesses can “plug and play” with. You want your data to be interesting, but don’t stray away too much from the norm. Templates are an effective way to get a jumpstart on well-presented data and information. Don’t overthink it.

Leverage Color: Color is a subtle, yet highly effective way to map data. For example: you could choose to make an anomalous point in your dataset red to immediately differentiate it from other points. For instance, use red to indicate heat and blue to indicate cold, if you’re tracking weather.

Don’t Distort Data: If users go too overboard with their designs and data visualization charts, they could end up inadvertently distorting insights. For example, pie charts should always be 2D. 3D pie charts might look more interesting and even draw more attention, but the unfortunate result makes each proportion of the chart harder to decipher. An effective pie chart, or any graph for that matter, shouldn’t be a puzzle.

Also see: Top Data Analytics Tools 

The post What Is Data Visualization? Benefits, Types & Best Practices appeared first on eWEEK.

The e-commerce platform Shopify has successfully leveraged machine learning through a methodological, five step strategy. Below we’ll examine each step and detail how it’s helped the company thrive.

Shopify’s growth is unparalleled in the e-commerce industry. Collectively, the platform’s merchants make up the 7th largest company in the world in terms of revenue. That puts them above conglomerates like Apple and Volkswagen.

The company has been at the forefront of ML adoption for anomaly detection and forecasting. That’s particularly impressive when considering that up to 85% of machine learning projects ultimately fail to deliver on initial promises and goals.

At the Cognilytica “Data for the AI Community” seminar, head of data science at Shopify Dr. Ella Hilal spoke on how the e-commerce titan has scaled so effectively using machine learning (ML). Let’s take a look.

Step 1: Be Clear on Your Forecasting Needs

The fundamental step of leveraging ML is simple enough: Gain a top-level understanding of your forecasting needs.

Dr. Hilal suggests a few questions business leaders can ask to understand their forecasting needs:

• What decisions will be informed by this information?
• When are you making these decisions?
• How far into the future are you looking to forecast?
• How often are you making these decisions?

Realize that this holistic approach to creating a machine learning model will be applied in each step of the process.

Also see: Top Data Analytics Tools 

Step 2: Understand Your Data

Dr. Hilal framed it clearly: “What data sources are available and what are their properties?”

Understanding the properties of your data sets is a key step to forecasting. Ask yourself foundational questions, such as whether data is univariate or multivariate. It’s also important to detect and eliminate non-stationary behavior in your time-series data.

“Nobody ever regrets spending the first few cycles of their effort toward a big model that informs big decisions by double-clicking on what data they have.”

In other words: It’s in your best interest to stay curious. It’s just as important to see which data sources are not available as the ones that are.

Understanding your data requires a top-level view of not only your operations but the market you operate in. This foundational knowledge is how data scientists can parse out anomalies and the specific reasons behind them.

Step 3: Choose the Right Forecasting Method

This is where the actionable steps behind forecasting begin. Dr. Hilal suggested several key points that forecasting models must have:

• Be easy to interpret.
• Possess the ability to be tuned easily.
• Be relatively fast.
• Provide automated forecasts.
• Manage multiple types of seasonality and recurring events.
• Manage missing observations or large outliers.
• Manage historical trend changes.

Shopify used Facebook Prophet, an open-source additive regression model. They specifically chose this model due to its scalability and its ability to generate forecasts quickly over millions of data points.

Another key decision to make when selecting the right forecasting model is whether to take a top-down or bottom-up approach. Top-down approaches look at top-line metrics first and analyze the drivers behind them, while bottom-up approaches analyze and track drivers first. Other questions to consider when selecting the proper model include:

• Single ML model or an ensemble approach?
• How often do you plan to retrain your model?
• Did you spend enough time tuning? Did you overfit?

Also see: Best Business Intelligence Tools 

Step 4: Manage Anomaly Bias

“Anomalies are not all bad,” said Dr. Hilal. In fact, if it is effectively explained and perhaps even recurring, data scientists may want to amplify the anomaly to learn from it.

A large part of managing your anomaly bias is studying your data across time and analyzing which timeframes are indicative of future behavior. Shopify had a massive amount of data to select from, with very clear annual cycles of data in place. However, the team decided that the last three months were most indicative of their next performance.

Dr. Hilal also mentions that 2020 was, clearly, an anomalous year. However, businesses and data scientists should consider that anomalies and unique trends will always exist. This is why Shopify took 2021’s unique trends into account as well.

Step 5: Automate the Model

Finally, just like any other ML model in production, forecasting is truly effective when automated.

This automation should address a few points:

• How is your training data stored? Raw or transformed?
• How will you retrieve the data for training?
• How will you retrieve data for prediction?
• How do we get feedback from a model in production? Any systematic alerts?
• How are you tracking drift?

Remember, you ultimately want your model to be scalable and to work with little intervention – automation is valuable for independent operation.

Also see: Top Data Mining Techniques 

Conclusion: Understand Your Decisions

It’s clear that the process behind leveraging ML for forecasting and anomaly detection at scale starts with a top-level understanding of your operations, needs, and industry.

In each step, Dr. Hilal emphasized the importance of stepping back and understanding the “what,” “how,” and “when” behind each decision.

The post 5 Steps to Leveraging Machine Learning: The Shopify Use Case appeared first on eWEEK.

Qlik and Tableau are among the most highly regarded business intelligence and data analytics tools available. In this article, we will compare these two data leaders, and note the ideal user for each.

To compete in the business intelligence (BI) and data analytics market, vendors must provide functions such as data preparation, data mining, visual exploration, insight generation, and self-service analytics. The marketplace is also demanding cloud-based tools that offer data visualization capabilities and interactive key performance indicator (KPI) dashboards; these must offer easy to read charts. Tools must also be able to gather data from a wide range of sources.

Also see: Best Data Mining Tools & Software 

Qlik vs. Tableau: Key Feature Comparison

Qlik is an end-to-end data integration and data analytics solution for modern BI. It delivers real-time insights and automation through its Active Intelligence Platform. Qlik’s cloud analytics leverages an associative engine, artificial intelligence, and the cloud.

Unlike many other BI platforms on the market that rely on SQL, Qlik’s engine can work with any data source in its existing format, allowing users to execute analytics on all their data within Qlik. The company makes it available in several ways. In addition to SaaS, there is a choice of multi-cloud and on-premises options.

The Qlik Sense data analytics platform includes an associative analytics engine, AI capabilities, and operates in a high-performance cloud platform. It empowers executives, decision-makers, analysts, and staffers who need BI that users can use to freely search and explore insights to help promote digital transformation.

Tableau helps people see and understand data. It offers visual analytics with AI, data management, and collaboration. At the heart of Tableau is a proprietary technology called VizQL that makes interactive data visualization an integral part of understanding data. A traditional analysis tool forces you to analyze data in rows and columns, choose a subset of the data to present, organize that data into a table, then create a chart from that table. VizQL skips those steps and creates a visual representation right away, providing visual feedback as you analyze.

Tableau offers an integrated data analytics and AI suite. AI-powered analytics lowers the barrier to data science techniques, enabling business users and analysts to make smarter decisions faster. It goes beyond creating charts through a series of templates and wizards to offer control and customization around insights. Full functionality can be deployed on any cloud (private or public cloud) or on premises, used on Mac or PC.

Qlik Sense’s AI assistant, called Insight Advisor, offers insight generation, task automation, and search and natural-language interaction. Users can combine and load data, create smart visualizations, and drag and drop to build analytics apps. It also offers interactive mobile analytics, embedded analytics; its Associative Engine allows people to explore in any direction.

Qlik’s AI-infused cloud analytics, then, are all about moving from passive backward-looking data consumption to activating relevant and timely data for real-time decision making. Its open cloud architecture means IT and users can leverage analytics with their cloud platform of choice such as AWS, Google Cloud Platform or Microsoft.

Tableau has over 80 native data connectors, which allows users to access data from a great many sources. Web Data connectors allow you to connect to other data sources. Users have the option of either ingesting data into the data engine or pushing queries to their databases.

Tableau is now owned by Salesforce. This has positive implications in terms of integration with Salesforce and opening the door to a large new customer base. However, it remains to be seen if Salesforce will allow Tableau to grow or will be more inclined to view it as a value add to its own primary offerings.

Also see: Data Mining Techniques

Qlik vs. Tableau: Comparing Analyst Comments

Qlik has been rated a Leader by Gartner for many years. The latest Gartner Magic Quadrant for Analytics and Business Intelligence Platforms again places it in that category. Qlik was also named by Gartner as a Challenger in the 2021 MQ for Data Integration Tools. A recent BARC survey had Qlik earning six number one rankings and 45 leading positions in six peer groups in the annual survey of some 2,500 BI and analytics practitioners.

Overall, Gartner praises Qlik’s strong product vision for ML- and AI-driven augmentation, and its deployment flexibility that doesn’t limit customers to any particular cloud. Acquisitions of RoxAI, Knarr Analytics, and Blender.io are enhancing capabilities for alerting, continuous intelligence, and SaaS platform integration. This is serving to expand its capabilities across the data and analytics pipeline. In addition, its Data Literacy Program is designed to educate users on BI and analytics and eliminate the need for data science degrees in analytics. Gartner notes, however, that Tableau has greater market momentum based on Gartner search and client inquiry data.

Tableau is another Leader in the Gartner Magic Quadrant, graded a little higher than Qlik. The analyst firm gave it high marks for having a sleek user experience that enables them to perform analysis seamlessly. It also does well on advanced visualization capabilities, courtesy of its VizQL engine. On the marketing front, Gartner believes Tableau has stronger momentum than Qlik. With more than 145,000 people attending its online user conference, and its free Tableau Public platform including more than 3 million data visualizations, Gartner also gives Tableau praise for usability. Finally, the company has put a lot of time and effort into its partnership with Salesforce. Tableau Viz Lightning, for example, was developed to simplify integration of Tableau visualizations into Salesforce.

Qlik vs. Tableau: Pricing

Although Qlik Sense offers core analytic and BI platform capabilities in a single license, additional licensing is needed for add-on capabilities, such as Qlik Catalog, Qlik Insight Advisor Chat, and Qlik NPrinting for Mode 1 reporting, if deployed on-premises, according to Gartner. However, Qlik’s SaaS platform simplifies everything into a standard subscription. Yet product pricing complexity is a complaint that is sometimes leveled at Qlik.

Tableau is a little more open than Qlik on pricing. It offers enterprise subscription plans with set prices. Still, Gartner commented on premium pricing concerns raised among its customers. For example, the company has been introducing Tableau CRM priced at up to $150 per user per month.

Qlik vs. Tableau: Comparing Similarities and Differences

Both Qlik and Tableau are highly capable BI and analytics solutions that are among the best in the business. Users can’t go far wrong choosing either one.

• Both companies enable customers to create interactive dashboards.
• Both offer the flexibility to deploy on-premises or with any major cloud provider.
• Both have added augmented analytics, using artificial intelligence and machine learning technologies to make data analysis easier on their platforms.
• Both offer data management capabilities to help organizations curate and govern the data analyzed by their products.

Tableau claims that it works better than Qlik with modern databases. This is because Tableau can connect to data live (performing queries in-database and returning results in real-time) or in-memory (ingesting data from source systems into Tableau). This allows customers to control performance, cost, and data freshness more easily than with Qlik. While both Tableau and Qlik can create dashboards, Tableau believes VizQL allows non-technical people to explore data without the help of IT. Qlik, at times, may require technical expertise to analyze data.

Another way to compare them is to look at their user communities. Qlik’s user community is mostly focused on IT developers and systems integrators. This indicates a strong focus on back-end integration and IT smarts. Tableau’s massive fanbase, on the other hand, is more focused on helping customers overcome roadblocks that slow the adoption of analytics products. While highly generalized, that might be a good way to contrast the platforms. One speaks well to developers and integrators. The other talks to broader, non-technical audience.

On the other side of the coin, Tableau is less capable as a cloud platform. With its heritage being on-premises deployments, its cloud-hosted solutions are limited mainly to Tableau Online and Tableau CRM. Gartner stated that Tableau lacks a cloud-native architecture for its large on-premises customer base to embrace the cloud’s full benefits.

Forrester, too, commented that the Qlik Associative Engine stands apart from the competition. Most of the competition relies on search-based BI platforms to provide guided analysis and data exploration. Qlik Sense search, however, is based on data values and relationships, which enables a user to select data points directly from a visualization. Forrester said this, “supports almost unrestricted data exploration.”

Qlik vs. Tableau: Who is the Ideal User for Each? 

Tableau targets business users who need an intuitive platform to stay more informed with data. Ideal users are non-technical business people who are interested in making better decisions through better understanding of data. And with its Salesforce ownership and greater Salesforce integration, users of Salesforce would be obviously inclined more towards it than Tableau.

Qlik could be characterized as more of a platform for IT personnel and data analysts who respond to business requests for data and analysis. However, its cloud architecture is more suited to organizations that are heavily invested in the cloud already. As such, Qlik offers greater scalability and flexibility in the cloud.

Also see: Guide to Data Pipelines

The post Qlik vs. Tableau: 2022 Software Comparison appeared first on eWEEK.

The amount of data at the Edge is already a challenge for IT leaders. Yet it’s becoming even more of a problem for managers of IT infrastructure as data levels expand exponentially.

Indeed, the overwhelming edge computing trend is expansion of data levels. IDC predicts that by 2025 there will be a total of 55.7 billion IoT (Internet of Things) devices, generating 73.1 zettabytes (ZB) of data worldwide. IDC projects that worldwide Edge revenues will grow to $250.6 billion in 2024, with a compound annual growth rate (CAGR) of 12.5% during that period.

As a result, businesses around the world will “feel the burn” of uncontrolled data transfers to the Cloud and the core data centers this year. Clearly, distributed data generated at the Edge cannot – and should not – be sent to a corporate headquarters for storing and processing. There’s simply too much of it. Now data management must extend to the Edge.

Decisions must be made about where, and how, to store and manage all that data. Considerations include a strategy about managing data from multiple data silos: IT data, operational data, network data and sensor-based IoT data. Plus, the data-types vary, by applications – including block data, file data and object data – with a variety of data that reflects changing computing technologies from the 1960s to the 2020s.

Adding to the challenge, there are myriad edge data categories – and some companies have more than one. They include:

• Enterprise Edge: bank branches, retail stores
• Industrial Edge: factories, automated equipment, robots
• Smart City Edge: self-driving vehicles, in-the-road embedded sensors
• Telco Edge: telecom equipment, 5G data, cell towers
• Tactical Edge: military, cybersecurity defense, ruggedized system uses

Let’s take a look at how we can best manage the explosive growth of data from edge computing.

Also see: Why the Future of Computing is at the Edge

Manage Edge Data with Multiple Partners

The Edge is not a cookie-cutter, repeatable landscape for data storage and data management. That’s another challenge for IT and business leaders planning their 2022 data strategy.

Because the data-management challenge is so complex, multiple partners will be needed to get the job done. Furthermore, leveraging these providers to assist with artificial intelligence (AI) and machine learning (ML) will support automated data discovery and software updates. Manual methods will not be fast enough to keep up with the sheer scale of Edge data changes.

We can see that CSPs and ISVs are already forming more partnerships to address end-to-end data management that extend to Edge locations. Notable partnerships include:

• IBM and Cloudera (IBM Cloud Pak software with Cloudera CDP)
• Equinix and Microsoft Azure
• AWS Outposts and VMware
• Google Distributed Cloud (Anthos) with HPE and NetApp storage
• HPE GreenLake and VMware

Data security and data protection must take place – at multiple tiers of the on-premises infrastructure – and must now extend to the Cloud and the Edge. Not a simple task. For most companies, scaling up data-management requires an end-to-end cohesive strategy, along with a set of consistent best practices, consistent security software deployments and consistent data policies. Without smart management and advanced software tools, distributing data to multiple clouds and all Edge sites would be chaotic.

Simplify Edge Data For Better Management

Data simplification at the Edge is key to avoiding data logjams that would slow down the network. When data transfers are slow – and data-transfers are large – the traffic snarls caused by network “latency” slow the pace of business considerably. That’s why data simplification, and local analytics of Edge data, is critical to good outcomes for data-management at the Edge.

Inventory the data. The first step is data discovery, making sure that all the data silos associated with different types of Edge use are inventoried, and understood, and made available for corporate tracking/monitoring and data management.

Given the spectrum of Edge data scenarios, IT alone cannot do everything for every piece of data – no matter where it lives, including data-at rest, data-in-flight and data being processed. Fortunately, IT organizations have many options for scaling up their ability to do data management at the Edge.

Get unified visibility. IT needs to be in-control, with software tools that can, together, provide unified visibility to data throughout the organization. To do so, they’ll need to have a unified dashboard to show data-monitoring logs across Edge locations – and a customer security “key” that preserves data privacy for remote data – and data traveling to and from the Edge. Many of these security keys are, in fact, “private” keys controlled by the IT customers, rather than software vendors, to ensure end-customer control and enforcement of corporate data-privacy policies.

Also see: Will Edge Computing Devour the Cloud? 

Scaling Up Data Management: Security, Protection, Management

Rapid edge data growth is giving rise to a new wave of data-management challenges that IT organizations need to address now. That’s why data simplification, data analytics, data management, data security and data protection at the Edge are going to be so important in 2022.

These criteria were voiced by Intel CEO Pat Gelsinger in a year-end video sent to Intel employees, saying of the Edge, “the top requirements are AI, low-latency, security.” He also acknowledged the emergence of zettascale computing and zettabytes of data in the 2020s.

So, here are three core areas for IT managers to consider for global Edge data management:

Data Security at the Edge. In the Edge, there is no traditional security “perimeter” to protect, challenging IT to find new ways to secure remote data. End-to-end security, enforced by data encryption, protects against attacks – cybersecurity and ransomware attacks – that brought some companies to a stand-still in 2021. If data security is breached, it may take hours or days for data to be restored – and business continuity to be achieved.

Data Protection at the Edge. There is a mind-set for consistent data protection, requiring an over-arching strategy and a solid implementation to protect data from the Core to the Cloud to the Edge of the network. IT managers will have to ensure careful data monitoring for Edge locations and have multiple scenarios for replicating business-critical data – and envision multiple paths for data to be recovered, if needed.

Data Management Strategies for the Edge. To extend an organization’s management of distributed data, reaching into the Edge, hybrid clouds, multi-clouds and Core locations, IT managers will see many data management software offered by a range of vendors. The specific features will be different, but it’s the IT strategy that matters most when building a data-management plan for the organization. Increasingly, organizations are looking to work with partners in the co-lo and CSP spaces, to extend their reach – and to become more efficient at data management. This is especially true for SMBs with limited IT resources at Edge locations. 

IT’s To-Do List Expands for Edge Management

So, let’s take the long view of where the rapid rise of distributed data is leading us – and how IT leaders can cope with fast-growing data resources at the Edge.

IT leaders must communicate with business leaders. We all have the point-products we need to create the next-generation of our corporate data infrastructure. It’s critical that business leaders and IT leaders will be on the same page – so that major initiatives can be funded in a timely manner, to keep up with our organization’s rapid data growth in 2022. We should do this with an eye to checking the boxes for compliance with data-privacy regulations, across geographies (e.g., GDPR in the European Union and CCPA in California).

“Mind the gaps” in data infrastructure. Look for gaps in your end-to-end data management strategy – and address them, one by one. Speak with business units, data scientists and end-users throughout your organization to identify aspects of distributed data management that simply aren’t working well right now. They will know where gaps are hurting their ability to track data quality – and to analyze the growth of your business.

IT leaders must create a unified strategy. Shop for partners to help with data discovery, data security, data protection and data management. Look to multiple data partners – including software vendors, cloud providers, co-lo operators, telecoms companies and services companies. Their solutions are already useful – as currently implemented – but it’s up to IT leaders to pull together a unified, consistent strategy for data expansion, data management and data security.

What’s Next for Managing Edge Data?

There’s no time to waste. IT managers cannot afford to wait until Q2 to map out its 2022 data-management moves; they must do so now.

Those data-planning meetings – whether virtual or in-person – need to happen right away. Managers should identify all the data that should be logged and monitored at the Edge, and they should gather and analyze all the metadata – the data about the data – to build a more unified, cohesive, and consistent plan for managing the entire corporate data portfolio from end to end.

The post Guide to Managing Edge Computing’s Explosive Data Growth appeared first on eWEEK.

Business intelligence (BI) software tools enable companies to analyze events, trends and market changes to gain competitive advantage.

These BI tools collect, manage and interpret mountains of structured and unstructured data. They construct models that deliver answers and insights through reports, charts, graphics and other data representation methods. BI software is the foundation of digital transformation. 

BI systems incorporate or use various data sources, including spreadsheets, querying software, reporting tools, online analytical processing (OLAP), data mining, data warehouses, and data marts. They usually encompass data extraction, data cleansing, data governance and dashboards or other mechanisms that allow users to view reports and visualizations.

The objective is to support business processes across an organization. These can range from past events, such as how a product or service performed, to predicting future events and scenarios. They can span various departments, including finance, human resources and operations. Increasingly, BI platforms intersect with machine learning (ML) and other artificial intelligence (AI) tools, such as natural language querying. They support advanced data mining techniques.

Not surprisingly, business intelligence—which dates back to the 1970s—continues to advance. Today’s systems span clouds and tap on-premises data sources, and work alongside data mining tools. They also rely on APIs to extend analytics capabilities across business partners and supply chains. Some platforms are also designed to pull data from edge computing deployments, and many platforms also include open-source tools and components that make them more flexible and adaptable.

• • How to Choose BI Software
  • Domo
  • Microsoft Power BI
  • MicroStrategy
  • Oracle Analytics Cloud
  • Qlik Sense
  • SAS Enterprise Guide
  • Sisense Fusion Analytics
  • Tableau Desktop
  • SAP BusinessObjects
  • Business Intelligence Software: Additional Market Leaders
  • BI Software Tools: Vendor Comparison Chart

How to Choose a BI Software Tool

As you focus on selecting your BI software tools, it helps to focus on five crucial steps:

• Analyze your existing data sources. Obviously, any new solution must ingest your organization’s existing data. Either it will do this directly or you will have map data structures and perform conversions manually. Without knowing what data currently exists and where it resides, it is next-to-impossible to determine what BI platform to use and what’s involved with the transition.
• Identify your objectives. If you’re looking to take your business intelligence and data analytics to the next level, it’s critical to identify the features you need in order to find a good match. For instance, do you want to expand the use of BI to different groups or departments? What types of reporting and data visualizations do you require?
• Determine whether other sources of data can improve business results. A primary reason to adopt a more advanced BI platform is to add features and capabilities. Are you looking to tie together cloud repositories or various enterprise systems? Are you hoping to build out a framework with edge computing companies to expand data collection across a supply chain? These are fundamental questions to ask before selecting a vendor.
• Survey the vendor landscape. Of course, vendors and software solutions approach BI in fundamentally different ways. Is the vendor geared toward on-prem, or are they cloud only? As a result, a critical element in selecting a solution is matching requirements with vendor features, its support framework and its roadmap. It’s important to determine which vendor can unlock the greatest value from your data, support business transformation and continue to add value in the future.
• Understand what changes are required. Any large initiative, including BI, requires changes to both IT infrastructure and business processes. Consequently, it’s essential to match your organization’s needs, desires and requirements with the right BI solution—and ensure that it delivers the ability, flexibility and scalability you need to navigate today’s fast changing business environment.

Top BI Software Tools

Here are 10 of the top BI software tool providers:

Domo

Key Insight: Domo focuses is on tying together data from a variety of sources and building insights through rich reporting and visualizations. The solution delivers sophisticated tools for handling data integration—including through visual ETL—as well as dashboards and mobile BI platforms that use intelligent apps to display analytic insights.

Pros

• Connects to a wide array of data sources and systems and offers powerful data importing and ingestion tools.
• Delivers real-time monitoring and results through robust alerts and notifications.
• Offers a rich and powerful feature set that can be used by data scientists as well as non-technical line-of-business users.
• Powerful dashboard capabilities.

Cons

• Additional apps and other tools and add-ons can increase the price tag significantly.
• Some users find the platform confusing and overwhelming due to a large array of options and choices.
• Lacks some embedded advanced analytics capabilities.

Microsoft Power BI

Key Insight:  Power BI is a sophisticated but affordable platform for organizations of all sizes. It supports self-service analytics for line of business users and data scientists, incorporating AI and ML. Microsoft includes strong collaboration features along with rich reporting and data visualizations. Power BI delivers powerful automation and includes tools to align setup and operation with enterprise scorecards.

Pros

• Powerful data ingestion and connection capabilities combine on-premises and cloud data seamlessly.
• Offers a wide array of pre-built dashboards that connect to various applications, products and services.
• Delivers rich interactive capabilities, including real-time reports and visualizations.
• Provides strong data governance features.

Cons

• The learning curve for advanced analysis can be complex and difficult.
• Graphics and visualizations aren’t as robust as competing BI solutions.
• May be difficult to use with non-Microsoft products and technologies.
• Doesn’t support IOS and other Apple devices.

MicroStrategy

Key Insight: The vendor’s focus is “intelligence everywhere.” MicroStrategy offers tools to build powerful and attractive dashboards that pull data from more than 200 data sources. The solution works on both PCs and Macs. It can deliver services based on roles, devices and numerous other factors. MicroStrategy supports automation and embeds analytics functions directly into applications and websites.

Pros

• Includes powerful features, including a “hyperintelligence” function with strong automation capabilities.
• Provides a strong feature set and extremely flexible BI framework.
• Offers powerful data governance features along with strong security.
• Generates highly rated visualizations.

Cons

• Many users find the platform difficult to set up and use, particularly when handling extremely large data sets.
• The solution can be pricey.
• Lags other platforms with collaboration and social BI features.

Oracle Analytics Cloud

Key Insight: The cloud native BI platform is designed to handle the full spectrum of analytics requirements, from data ingestion and modeling to data preparation, enrichment, visualization and collaboration. It includes embedded ML and AI, including natural language search functionality. Open-source data connectivity with built-in connectors simplify data aggregation tasks.

Pros

• Provides more than 50 out-of-the-box connectors, including Java Database Connectivity.
• Delivers a scalable single source of truth data framework for line of business users as well as data scientists.
• Offers strong ML capabilities through a Python-based framework.
• Provides strong support for mobile devices and includes a powerful data governance and security framework.

Cons

• Users complain that the framework can break, particularly on upgrades. Error tracing can be difficult.
• The interface could be modernized and improved.
• User ratings for technical support and end-user training rank below competitors.

Qlik Sense

Key Insight: The vendor delivers robust BI through a cloud analytics platform. Qlik Sense dashboards connect to hundreds of data sources and support complex and varied use cases. This includes self-service visualizations, interactive dashboards, natural language search, and powerful mobility functions. AI and machine learning support augmented and predictive analytics.

Pros

• Comes with built-in connectors for hundreds of data sources and services, including all major cloud platforms.
• Strong support for both structured and unstructured data.
• Highly rated for ease-of-use, including analytics dashboards and interactive visual exploration features.
• Provides rich and varied reporting and visualizations.

Cons

• Lags behind competitors for collaboration and social BI features.
• Users rate governance and metadata management below other BI solutions.
• The software can present a steep learning curve. Training may be required to use the platform effectively.

SAS Enterprise Guide

Key Insight: SAS Enterprise Guide is a Windows .NET client application that offers a self-service framework for collecting, managing, manipulating and formatting data into reports and visualizations. It relies on a point-and-click, menu-and-wizard-driven framework that includes the ability to write code and use custom macros. Thus, developers and other coders can use existing programs to create stored processes, and then run them on demand or port them to external apps, such as Microsoft Excel.

Pros

• Delivers a highly customizable BI framework through a well-developed coding platform and macros.
• Strong IT-managed role-based security along with powerful data governance features.
• Handles huge data sets while delivering fast performance.
• Strong support for Microsoft Office, including Excel.

Cons

• Overall complexity can present challenges for non-technical users and those with limited coding knowledge.
• Expensive and sometimes confusing price structure.
• No support for Python or R tools.

Sisense Fusion Analytics

Key Insight: The platform delivers a highly scalable and flexible self-service framework for conducting enterprise grade BI. The vendor’s focus is on infusing “intelligence everywhere” through extended data discovery in the cloud and on-premises, dashboards and integration APIs. Sisense offers strong AI and ML support, and powerful data visualization capabilities.

Pros

• Offers data connectors for almost every major service and data source.
• Delivers a code-free experience for non-technical users, though the platform also supports Python, R, and SQL.
• Provides a wide array of templates that automate BI and embed actions into applications and processes.

Cons

• Can present a steep learning curve.
• Users complain that errors and problems can be difficult to trace.
• Some complaints about bugs and stability issues.
• Mobile device support not as strong as others in the BI space.

Tableau Desktop

Key Insight: Tableau has emerged as a leader in BI and analytics with a framework that supports rich visualizations and storytelling. It offers appealing and highly customizable dashboards, and it can accommodate huge data sets. Tableau Desktop connects to hundreds of data sources, both on-premises and in the cloud. It includes drag-and-drop functionality along with support for AI and ML.

Pros

• Delivers outstanding visuals.
• Strong data integration and automation features.
• Includes powerful data management and presentation features.
• Supports most types of devices and operating systems, including all major mobile platforms.
• Offers 24/7 support.

Cons

• Steep learning curve for setting up data frameworks.
• Expensive, with complex and inflexible pricing model.
• ETL capabilities are not as advanced as other BI and analytics platforms.

SAP BusinessObjects

Key Insight:  SAP offers sophisticated business intelligence and analytics capabilities through its BusinessObjects intelligence suite. The platform serves as an on-premises BI layer for SAP’s technology platform, including SAP Business Warehouse and SAP HANA. It delivers a centralized suite for real-time data reporting, visualization and sharing. It also supports enterprise performance management features such as planning, reporting, query and analysis, and enterprise information management.

Pros

• Offers powerful features and capabilities, including data ingestion and automated reporting.
• Integrates well with numerous applications and cloud platforms.
• Strong Excel spreadsheet and Microsoft Office integration.
• SAP offers solid service and support.

 Cons 

• Some users say that the interface and dashboards could be more user-friendly and more customizable.
• Collaboration and social BI not as developed as competitors.
• Expensive with somewhat complex pricing model.

Tibco Spotfire

Key Insight: The self-service BI and data visualization platform incorporates a visually appealing interface along with strong AI features to deliver advanced data search capabilities. This includes natural language search, AI-driven recommendations and direct manipulation of data. The platform is designed for line of business users and data scientists. It includes more than 60 native connectors and extensive support for APIs.

Pros

• Extremely flexible and highly customizable.
• Supports extremely large data sets.
• Offers excellent data visualization tools and options.
• Highly rates customer and technical support.

Cons

• Lags behind competitors in mobile exploration and authoring.
• Collaboration and social BI tools are not as advanced as others in the BI space.
• Presents a steep learning curve. 

BI Software Tools: Additional Market Leaders

Birst

The analytics provider focuses on pre-built industry and role-specific content and metrics to power BI throughout the enterprise.

Google Charts

Google’s free data visualization tool works with JavaScript to assemble data and generate presentations and reports.

HubSpot

The CRM data platform addresses marketing through customer insights. It features an appealing interface and robust analytics tools.

IBM Cognos Analytics

IBM delivers rich BI insights through reporting and data visualizations. The platform taps Watson AI and machine learning for predictive analytics and other features.

KNIME

The free, open-source data analytics solution supports data integration, modeling and visualization capabilities.

RapidMiner

The platform generates a wide variety of data insights through AI, including predictive analytics.

ThoughtSpot

Delivers AI-driven analytics through powerful data ingestion along with dashboards and visuals.

Xplenty

The platform supports ETL and reverse-ETL functionality within a highly scalable BI and analytics platform. It delivers strong compliance and security features.

Zoho

The self-service BI tool delivers robust uses powerful connectors and intuitive dashboards to deliver reports and visualizations.

 Business Intelligence Software Tools: Vendor Comparison Chart

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The pandemic focus on work from home in 2021 put the spurs to cloud migration for many businesses, which had to migrate to the cloud almost overnight to support remote workforces. As a result, teleworking has become cloud computing’s killer app during the lockdown – but cybercriminals know this, too.

Clearly, hackers will keep targeting cloud environments as long as they see profit potential in those attacks, so they will be doubling down on targeting cloud infrastructures in 2022.

With that in mind, here are five cloud security developments to expect in the year ahead, and a few suggestions to tackle the challenges they bring.

Also see: Tech Predictions for 2022: Cloud, Data, Cybersecurity, AI, and More

Cloud Security Trends in 2022

1. Supply Chain Attacks Will Rise

The now year-old Solarwinds attack, where cybercriminals breached thousands of customers by inserting malicious code into software updates, was the canary in the coal mine. Expect hackers to target weak links in the supply chain as a way to breach the cloud and attack better-protected assets.

Tip: Cybersecurity defenders should focus on cutting off attackers’ ability to move laterally by reducing entitlements and privileges for all internal and external identities (including machine identities) that are allowed to access cloud resources

Also see: Top Cybersecurity Companies

2. Cloud Breaches Will Be a Fact of Life

McKinsey & Co. has noted that the pandemic has forced many enterprises to leapfrog years in their use of the cloud during the lockdown period. But it hasn’t been a smooth transition, and security has suffered in some cases; 40% of companies have suffered a cloud-based data breach. In 2022, expect every organization to endure at least one cloud breach.

Tip: Businesses should focus on reducing their cloud attack surface and containing the blast radius if a hacker breaches their security controls. They should invest in tools that provide visibility into cloud identities and give defenders a clear picture of the potential damage that could follow a breach so they can respond quickly and connect with all the stakeholders.

3. Cloud Maturity Will Make a Difference

Companies that are in the early stages of their cloud migration are at a greater risk than those that are optimizing their existing infrastructure. Expect those organizations in the building stages of cloud development to suffer more breaches.

Cloud-native companies have an advantage here, since they typically have a better understanding of the complexities associated with managing the security posture of cloud infrastructures to protect their data and assets, rather than relying on platform providers to step in.

Tip: Cybersecurity professionals should focus on building security controls for identities, access and configurations into their cloud migration roadmap at every stage.

4. Zero Trust Picks Up Speed

“Zero trust” is quickly becoming a buzzword that threatens to lose its meaning, but it hasn’t lost its importance. Having a consistent, uninterrupted security policy guarding who can access data and resources and enforcing least-privilege access is still the key principle for protecting cloud infrastructure. So expect initiatives to leverage zero trust architecture to keep catching on with enterprises.

Tip: Organizations that want to enforce zero trust and least privilege in the cloud need to manage network policy, identity and access permissions, and resource configurations. They need to put practices into place that provide comprehensive visibility into all three to better manage the threat.

5. Machine Identities Will be an Achilles Heel

Organizations have become wiser about improving security among their users by leveraging multi factor authentication (MFA) and single sign-on (SSO) to prevent credential abuse and mishandling. As more organizations improve security on the human front, cybercriminals will look to open new fronts by targeting machine and service identities. These have already been exploited in almost every cloud breach to help attackers access data or move laterally within systems. This is primarily because machine and service identities are created by developers to enable other functions and that are outside the wheelhouse of cybersecurity.

Tip: This needs to change. Faced with a shortage of cyber security talent, organizations need to leverage tools that can serve as a force multiplier for security staff. Automation can help with cybersecurity by identifying and prioritizing the risks connected to machine identities and their entitlements, as well as automating the remediation of those risks.

The remote work genie is refusing to go back in the bottle. Even after the pandemic becomes a memory, employers expect a number of their staff will remain remote in some manner. Meanwhile, cloud usage will continue to accelerate and so will attacks against service provider platforms and the data they contain. The year 2022 needs to be the year for getting cloud security basics right.

About the Author: 

Shai Morag is the CEO of Ermetic

The post 5 Cloud Security Trends in 2022 appeared first on eWEEK.

I spoke with Mark Porter, CTO, MongoDB, about trends in the database market, and how the so-called ‘Innovation Tax’ hinders the ability to produce outstanding tech solutions.

Among the topics we covered: 

• Let’s take a look at the database market as we enter 2022. There’s a lot going on in the data and data warehouse market: Snowflake, Amazon competing aggressively, etc. What do you see as a couple of trends defining the database market in 2022?
• The Innovation Tax: The idea that clunky processes and outdated technologies make it harder for engineering teams to produce excellent tech that delights customers. How can companies strategize for this?
• You’ve written an interesting blog post about engineering teams, and how there’s a “Goldilocks Gauge” of just the right size. Please explain.
• The future of databases over the next several years? What do you see?

Listen to the podcast:

Watch the video:

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On Tuesday, January 18, at 11 AM PT, @eWEEKNews will host its monthly #eWEEKChat. The topic will be “Digital Transformation Trends,” and it will be moderated by James Maguire, eWEEK’s Editor-in-Chief.

We’ll discuss – using Twitter – the trends that will shape digital transformation in 2022. On a related note, how will data, AI, cloud computing and security be affected by digital transformation?

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

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

Special Guests, Digital Transformation Trends

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

• Llanor Alleyne, Managing Editor, IT Business Edge 
• Chris Ehrlich, Managing Editor, Datamation
• James Maguire, Editor-in-Chief, eWeek [moderator]

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

Questions for the Tweetchat

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

1. Digital Transformation as we start 2022? Are companies actively planning, or mostly riding the continuous move toward tech integration?
2. If there’s one technology/concept in Digital Transformation that has staying power, what is it?
3. What’s a key trend (or two) in Digital Transformation that’s shaping the near-term future?
4. Challenges in Digital transformation? Where do companies struggle?
5. Worse, where are the money pits? Where do companies waste money chasing digital transformation Nirvana?
6. The future of Digital Transformation? Where will we be in 3-5 years?
7. What else is important about Digital Transformation – what else should companies be aware of?

Go here for CrowdChat information.

#eWEEKchat Tentative Schedule for 2022*

Jan. 18: Trends in Digital Transformation
Feb. 15: Navigating Multicloud Computing
March 15: Low Code / No Code Trends
April 12: Edge Computing: Monitoring, Observability and More
May 17: Data Analytics: Optimizing Your Practice
June 14: Expanding Your AI Deployment

*all topics subjects to change

The post eWEEK TweetChat, Jan. 18: Digital Transformation Trends appeared first on eWEEK.

Cisco is bringing its Webex technology to the final frontier: space. Webex will be a part of the Callisto payload on the Orion spacecraft, which launches later this year on a mission called Artemis I.

Formerly known as Exploration Mission-1, this launch will be the first in a series of several complex missions focused on human exploration to the Moon and Mars.

Also see: Cisco Launches New Hardware and Software at WebexOne

Orion Will be Voice and Video Enabled

The mission is a partnership between NASA and three key vendors: Lockheed Martin, Cisco, and Amazon.

Lockheed Martin is the main contractor for NASA; it built the Orion crew module, crew module adaptor, and launch abort system. The Callisto payload aboard the Orion will contain an iPad equipped with Cisco’s Webex to enable voice and video in deep space. It will also include Amazon’s Alexa cloud-based voice service to provide astronauts with companionship and information.

The project has been in the works since 2018, and is expected to finally make its debut this spring or early summer. This effort will allow NASA and its tech partners to test technology already used on Earth in space, to improve the lives of astronauts, said Aruna Ravichandran, VP and CMO for Cisco Webex Collaboration, in an interview with ZK Research.

Artemis I Will Test the Limits of Collaboration in Space

Artemis I will be a 40-day space mission, focused on simulation. The Orion won’t have humans onboard during the first mission, but rather a simulation of astronauts using Webex to communicate with the Mission Control Center at NASA’s Johnson Space Center in Houston.

A VIP room at the center will house technology for this mission, including WebEx Board Pro and the WebEx DeskPro. Cisco’s goal is to test two uses cases for Webex in space: videoconferencing and whiteboarding.

“Testing these technologies will ensure that when real astronauts go to the Moon, this will make it easier for them and it will be a successful mission,” said Ravichandran.

First, Cisco will facilitate communication between DeskPro and an iPad on Callisto, running Webex video meetings. Cisco expects latency to be a major challenge during the mission, given that the spacecraft will be more than 240,000 miles away from Earth. To address this challenge, Cisco built a proprietary blend of next-generation adaptation and resilience technology, which builds in buffering to deal with delays in deep space.

Additionally, Cisco will demonstrate how astronauts can create, collaborate, and brainstorm when they’re in space using WebEx Board Pro. Astronauts will be able to whiteboard on the Orion and relay it back to the Mission Control Center.

Also see: Compliance vs. Security in Enterprise Networking: the Cisco Approach

Video Collaboration Pushed to the Next Level

“Collaboration on Earth has become table stakes. We couldn’t have survived without collaboration technologies during the pandemic,” said Ravichandran. “We’re taking it to the next level with NASA. The first mission is all about testing the technology and mastering latency.”

In addition to helping astronauts perform their jobs, Cisco wants to enhance their quality of life. Astronauts on missions often experience loneliness and isolation. Equipping astronauts with videoconferencing tools will allow them to stay connected to family and friends.

Video communication in space has always been problematic due to distance and the challenging nature of the environment. It’s common for communication systems to lose data after just milliseconds of delay. That’s why testing buffering technology developed by Webex engineers will be an integral part of the mission.

A Custom AV1 Codec: The Ultimate Video Transmission Technology

Cisco will also address constrained bandwidth in space using a customized version of the AV1 codec to greatly improve video quality. This will ensure transmission of the highest resolution video at 128 kilobits per second (kbps). While AV1 is not widely available in Cisco products yet, the vendor is customizing its technology to implement AV1 on the Orion.

This might be one small step for collaboration but it has the potential to be one giant leap for videoconferencing. Traditional video systems do not deal well with latency. They either drop packets, causing parts of the picture to go missing, or drop the connection completely. Video transmission was never designed for an environment like space.

Cisco is using AI technology to reconstruct video and audio to make decisions around what to send or not send. For example, instead of sending the full image, use lower quality for the background and maintain higher quality for the person’s face.

Lessons learned during the mission could be applied to use cases here on Earth, such as boosting bandwidth utilized by Internet of Things (IoT) devices in the field across industries. Ultimately, these advancements could remove the boundaries for hybrid work, said Ravichandran.

AV1 has been in the works since 2015 but is nearing standardization and could see deployments in areas where bandwidth isn’t plentiful. An example use case is what Cisco is currently working on: bringing video to low bandwidth, high latency environments. And this effort would also allow HD video to be delivered to places where it’s not possible with the use of standard codecs.

“This is a big area of focus for us at Cisco. We want to be the leaders in defining hybrid work,” Ravichandran concluded. “Now we can take it a step further to the final frontier by enabling video communication and collaboration in space.”

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

The post Cisco, Lockheed Martin and Amazon Partner to Bring Collaboration to Space appeared first on eWEEK.

The new solutions Dell Technologies introduced at CES 2022, along with three new workplace Concepts announced in mid-December, are good examples of products that help employers and employees address business and marketplace shifts. That is especially important for products used in the workplace, including endpoint PCs and laptops.

Let’s take a look at what’s new and improved among these endpoint products.

Also see: Top Edge Computing Companies 

CES 2022: Dell Reimagines XPS 13 and Collaboration

Launched originally at CES 2012, Dell’s XPS 13 has since become one of the company’s most best selling laptops, and for good reason.

That first generation (L321X) was Dell’s initial Ultrabook, a class of laptops that utilized a host of new technologies and features to deliver significantly better compute, battery and display performance. The XPS 13 also fundamentally altered the company’s reputation for product design and aesthetics. The XPS 13 wasn’t just a new laptop line—it was a signpost indicating Dell Technologies’ future direction.

Since then, next gen XPS 13 solutions have steadily highlighted new features and technologies developed by Dell and strategic partners, like Intel, and the 10^th anniversary XPS 13 Plus introduced this week at CES 2022 continues that progression. This is the first XPS 13 to incorporate 12^th gen Intel Core processors.

However, new Dell fan technologies (increasing airflow by 55 percent without raising noise or temperature) enable the XPS 13 Plus to use 28W Intel Core chips (up from 15W in prior gen systems), thus substantially improving overall performance.

Along with streamlined design changes that reinforce the XPS 13 line as a solid choice for use in the office or home, the new XPS 13 Plus offers practical enhancements, including Express Charge 2.0 (which brings the laptop up to 80 percent battery life in under an hour), larger (zero-lattice) keycaps, a glass touchpad, improved InfinityEdge display and redesigned quad speaker system. The XPS 13 Plus also meets the social impact plans in Dell 2030 Goals.

Dell’s new UltraSharp 32 4K Conferencing Monitor also offers new and improved features, as well as a sophisticated design aesthetic that fits both home and workplace use cases. The monitor includes Dell’s UltraSharp Webcam (with SafeShutter), echo-cancelling dual array microphones and 14W speakers, enhancing both collaboration and visual experiences.

The UltraSharp’s IPS Black technology and VESA DisplayHDR 400 features enables content to be viewed in 4K resolution, contrast, and color, and ComfortView Plus reduces harmful blue-light emissions without affecting color accuracy. Like other Dell video conferencing monitors, the new UltraSharp is certified for Microsoft Teams and comes with privacy and productivity features.

Both the XPS 13 Plus and UltraSharp 32 4K Conferencing Monitor will be available for purchase worldwide in spring 2022.

Also see: 5 Edge Computing Trends in 2022

Dell Concepts for Seamless Work Experiences

While the new solutions launched at CES 2022 address current issues and needs, Dell’s vision of the workplace of the future is apparent in the Seamless Work Experience Concepts outlined last month in a blog by Glen Robson, CTO of Dell’s Client Solutions Group:

• Concept Flow – is designed to remove the productivity-sapping friction workers can experience as they transition from location to location. To address that, Dell engineers are marrying devices, industry standard wireless charging technology, intelligent software applications and Wi-Fi 6E docking technology to create a singular, seamless proximity-sensitive user experience. As a result, workers will be able to get up and running faster and with fewer steps and headaches than they currently do.
• Concept Pari – Teams and team members often face communication and collaboration barriers when using video conferencing tools. To improve that situation, Dell engineers studied the impact of cameras and camera placement on communication and developed a small (~ 1 ounce/30 grams), simple, moveable wireless webcam with built-in microphone that can be placed and positioned virtually anywhere. Using the webcam, users can more easily maintain direct eye contact with those they are addressing and share offscreen content and images. While designed mainly for people who regularly engage in video calls, Concept Pari could be a worthwhile asset for anyone who regularly transitions between various work locations.
• Concept Stanza – Finally, traditional work activities like notetaking can also be improved with new digital tools. Concept Stanza aims to do that by providing workers a thin, light, port-less, 11-inch companion device for PCs. Designed to reduce distractions, using Concept Stanza is similar to handwriting notes but double tapping a note instantly converts it to text that can be saved, retrieved, searched and shared across multiple devices. Designed to be used with either a stylus or microphone, Concept aims to blend analog and digital processes, thus enhancing overall workplace productivity

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

Addressing Business Needs, Planning Innovation

The new Dell XPS 13 Plus and UltraSharp 32 4K Conferencing Monitor launched at CES 2022, along with the Seamless Work Experience Concepts announced last month, highlight how the company is effectively addressing business customers’ current requirements while planning innovative future products and tools.

What is clear from examining these new and potential offerings is Dell Technologies’ astuteness in balancing the market’s thirst for valuable new solutions and the need to continually evolve its portfolio of workplace endpoints.

The post Dell Technologies at CES: The Importance of Endpoint Evolution appeared first on eWEEK.

As companies increasingly move to cloud architectures, they’re looking for ways to increase security while also reducing complexity and operating costs. And of course the move to remote work – which boosts the need for hybrid cloud and Software as a Service – only accelerates the need for these requirements.

All of these requirements can be addressed by an emerging tech that’s high on the current hype cycle for enterprise security: Secure Access Service Edge, or SASE. The benefits are outstanding: SASE is an architecture that combines networking concepts like VPN and SD-WAN, along with security concepts like Zero Trust (another compelling but often over-hyped concept) and contextual access.

But at this point, SASE represents more of a goal or concept for most organizations than a product. Nevertheless nearly all the major vendors have seized on this concept (e.g., Cisco, Palo Alto Networks, Zscaler, Akamai, McAfee, etc.) and labeled many of their components as critical to achieving SASE.

But as attractive as the concept sounds, in practice there are many hurdles companies need to overcome in order to turn SASE hopes into reality.

Challenges of SASE Deployment

In broad terms, SASE is the integration of network services with security services to make user access, SaaS and multicloud functions fully secure. It includes capabilities inherent in SDWAN deployments, like path resiliency and redundancy. It also offers app routing, visibility and reporting, vendor-specific software defined capabilities, and VPN.

SD-WAN, which helps virtualize networks and their operations, is deployed widely, but does require that enterprises replace older single function switches and routers with new equipment. This virtualization of networking has been going on for several years, and the majority of enterprises have virtualized most of their networks, or at least a major portion.

This is a critical step, as SASE requires a virtualized network in order to be implemented. Further, the public cloud networks are all virtualized so heavy users of these services have a built-in advantage.

The more difficult part of realizing SASE is the need for a security architecture that can be fully integrated and managed, much like software-defined networks are. But the majority of enterprises currently have a hodge-podge of security products in place that are nearly always stand-alone.

In fact, some organizations have dozens, if not hundreds, of unique security apps running on everything from their data centers or cloud instances, to networks, to hardware endpoints, and to individual apps. Even the ubiquitous VPN necessary to securely connect over networks may not be cross-compatible with all the devices and servers within the organization. And with an increasing number of network options available (e.g., 5G, WiFi 6, broadband), this represents its own compatibility challenges.

SASE requires a uniform method of policy management, secure access, threat protection and device management in order to be fully implemented. And with so many security components in place that generally do not play well tougher, this is a daunting task.

One final obstacle to overcome is organizational. In most enterprises, networking and security operations are unique groups, not necessarily always in close contact. For SASE to be fully implemented, both network ops and security ops must be on the same page and working together. Without this interaction, implementing SASE isn’t possible.

Implementing SASE Requires Major Integration

Despite what many vendors promote when selling their products, achieving SASE is really a major integration challenge. And many smaller organizations do not have the resources to make this happen, even if they do have some components already in place (e.g., SD-WAN, cloud access gateways, etc.).

Even larger firms will have difficulty making sure their entire network and security tools and cloud management products are able to share information and be managed through a single interface. While it’s possible to have a SASE implementation without a universal control plane or single pane of glass console, the cost of such a solution is much higher in skills required, people required, and time spend compared to a single management interface. It’s also problematic in that it’s much easier for problems to arise due to a lack of visibility when non-compatible systems are manually managed.

For many companies, the best path forward to SASE is to find a systems integrator that can not only integrate the necessary tools but also can manage the day-to-day operations of their SASE architecture implementation. However, enterprises should be aware that SASE is a “moving target,” in that few companies have all the components in place, and even ones that do will likely face upgrades and changes to infrastructure over time as capabilities mature.

Network operators (e.g., Verizon) have created SASE practices that can provide “SASE as a Service” on top of their connectivity and security service operations, but enterprises will still need to manage the relationship and devote resources to ensure the latest updates in network and security are fully implemented as business needs and infrastructure changes take place.

Further, service providers will have their own set of preferred partners that may not be compatible with the vendor products the enterprise currently has in place. Still, this may be an advantageous path, as these operators can leverage both their ability to influence vendor apps, as well as their learned experience in making SASE work effectively.

Market Consolidation Needed before SASE Maturity

SASE as a concept has a lot of merit in that it can significantly improve the security posture of organizations, particularly in a cloud-heavy world in which we now reside. But achieving the realization of SASE is not as straight forward as many vendors’ hype makes it sound.

Indeed, we expect it will take at least 3-5 years before the SASE market settles on what the architecture really looks like and which vendors will be at the forefront (with market consolidation removing many players from the field in the interim).

Enterprises should definitely be evaluating the SASE architecture as a way to increase security, but must also be aware that current products may not be the end products in place in the future, and therefore deployment flexibility will be required.

The post Secure Access Service Edge: Big Benefits, Big Challenges appeared first on eWEEK.

I spoke with Krishna Subramanian, COO at Komprise, about the potential benefits from optimal management of unstructured data.

Among the topics we covered:

• Let’s talk about the management of unstructured data. Why is it challenging?
• If you were to give companies advice about optimizing their use of unstructured data, what would it be?
• You’ve mentioned the importance of “enabling machine learning with file data from data lakes.” Why is this so advantageous?
• What are some key trends in the future of unstructured data management?

Listen to the podcast:

Watch the video:

The post Komprise’s Krishna Subramanian: Tips for Managing Unstructured Data appeared first on eWEEK.