How to Fix a Cloud Deployment That’s Losing Money

Cloud computing will save you money. 

Ten plus years ago that was a familiar refrain when the technology hype machine touted cloud computing as the best thing since sliced bread. Today we have over a decade of cloud deployment data that often disproves or redefines the context of that statement. 

NetEnrich recently conducted a survey of 100 IT decision-makers in companies with 500 or more employees. Amongst this group, 85% claimed either moderate or extensive use of cloud computing infrastructure, while 80% stated that they have moved at least a quarter of all workloads to public cloud providers.

Now the bad news. The survey identifies security as the top cloud computing issue (68%), followed by IT spend and cost overruns (59%), day-to-day maintenance (36%), and root-cause analysis and post-mortems (22%). Some 48% also claim that the cost of recruiting cloud skills is an ongoing issue in their IT organization, with an estimated 10 open positions that chase every one qualified candidate. 

All in all, cloud computing is not the cost-savings slam-dunk once promoted. This is especially true about delivered cloud value, in terms of business agility, compression of time-to-market, and instant scalability. In some situations, enterprises see negative values for every cloud computing dollar spent rather than the business efficiencies they expected. 

Our decade of cloud implementation data indicates that enterprises often need to take operational cost savings off the table as value justifications of cloud computing, which leaves soft value savings such as agility and speed-to-deployment. If those soft savings are not there, or not valuable to a specific enterprise, the enterprise will see negative value in cloud computing. This is a concerning situation for enterprises and cloud technology vendors alike. 

So, what can be done about this problem? 

Also see: CloudOps: How Enterprise Cloud Migration Can Succeed

The Choice That Isn’t a Choice: Cloud Isn’t Optional

If cloud computing delivers a negative value, you might think the best solution is to push off future cloud migrations and/or pull cloud applications and data back to traditional systems within enterprise data centers. Unfortunately, those ideas won’t work. 

The major enterprise technology providers dedicate 80% of their research and development (R&D) budget to build technology for cloud-based platforms or to build clouds themselves. That means R&D dollars are not spent on the upkeep of existing products or development of net-new products for more traditional enterprise systems. 

Enterprises already see fewer software updates and upgrades for non-cloud products and dwindling updates and upgrades for security and database systems. The larger enterprise technology providers know their future is in the cloud, and thus, they spend their innovation dollars in the cloud. That money will naturally come from the budgets of their more traditional business software and services. 

At the end of the day, those who want to avoid value delivery issues around cloud-based projects may discover even less value by staying put or repatriating systems back into the data center from the cloud. So, you can’t avoid the pull of cloud platforms to move forward, and you can’t save any money if you move backwards. At this point, it looks like cloud is the only game in town.

For Cloud, Planning is Everything

Most negative cloud values arise when an enterprise does not correctly leverage cloud-based resources. Typically, this means someone did not pick the best platforms, tooling, and processes for the project, and those choices are not or cannot be optimized. Those incorrect choices almost always track back to a lack of planning in the initial phases of a technology project, cloud or not. 

A universal truth: If you don’t have a thorough understanding of the problem, you will only have a partial understanding of the solution required to solve the problem.

The somewhat good news? There is no immediate pain to an unoptimized cloud operation. Most of the time, moving systems to the cloud works just fine and, initially, appear to be successful migrations.

However, even though it “works,” the team will soon realize the migrated or net-new cloud-based systems do not deliver the value defined and promised in the business case. This includes value metrics in terms of how the business value of cloud computing can be measured. Here, many cloud projects get an F. 

For example, look at a generic enterprise that moved their inventory management and inventory data to the cloud over the last five years. There are about five variations of cloud technologies they could have deployed, and an exponential number of configurations within those variations. Most of these solutions and configurations work, but these enterprises will not realize their peak hard and soft savings because the defined value metrics are misaligned with the chosen cloud solution. Therefore, the enterprise will experience some degree of negative cloud value. 

In the case of our inventory management system, let’s say the enterprise defined a cloud-based database that won’t store complex data and/or can’t deal with nesting as needed for advanced predictive analytics. It turns out these features are required to create automations to optimize inventory, such as just-in-time (JIT) inventory and automation across a supply chain. 

Let’s also say the enterprise leverages a cloud-based user interface development system that does not support mobile computing platforms. To top it all off, the applications and data storage systems were defined without regard for operations, security, and governance during initial planning. Thus, the system must deal with the inefficiencies created when those parameters are added into the system just prior to deployment.

Now is the Time Improve Cloud’s Business Value

The trouble with the slam-dunk cloud conclusion – this cloud deployment is clearly a success or a failure – is that it doesn’t account for real-life variables, such as skills shortages and an almost chronic lack of planning that’s necessary to make good technology decisions.

The goal is to build better systems with better thought-out cloud technology and configurations to find the desired value. That’s a hard goal to reach if you can’t find the skills required to correctly complete the first step or if you forge ahead without those skills. 

Some would argue that cloud computing is so new we’ve yet to build up sets of best practices to cover every cloud situation, and these types of issues are just a part of the growing pains. It’s now a proven fact that we can’t go backwards. That means we must find a way forward to establish business value for every cloud project. In short, we have to learn from our mistakes and reconfigure our cloud deployment. 

If you think about it, we can almost always trace a cloud project’s negative value issues back to substandard skills at the outset of a project. Whoever made those calls with faulty information often found that their choices led to negative values, even when the system “worked.” We clearly need to fix the planning problems ASAP and define clear paths for our IT staff to gain the knowledge required to close the skills gap. 

The good news: now that we’ve been down this path, we know enough to step back and rectify our earlier decisions, to use a now-informed cloud strategy to alter key variables to move toward a profitable cloud deployment.

Here’s the Takeaway: It’s Never Too Late to Reconfigure

An optimized and high-value cloud solution is almost always possible, but most enterprises will fall short. If your new cloud-based applications and data sets lack the value the enterprise assumed was a guaranteed outcome of the move to the cloud, it’s time to go back to the beginning.

Take a hard look at the initial planning processes for current and future cloud migrations and look for opportunities to optimize existing migrations. It’s never too late. 

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AWS Re:Invent Wrap-Up: Social Issues, Networking Focus, Partner Revenue

The Amazon Web Services (AWS) Re:Invent conference was held last week in Las Vegas. This year, the 10th Re:Invent, returned to an in-person format after a year as a digital event due to the pandemic. It was also the first Re:Invent under new CEO Adam Selipsky, who takes the helm from Andy Jassy, who had arguable one of the most successful tenures of any CEO in history, leaving big shoes to fill.

Going into the event, I wasn’t sure if we would see Jassy 2.0 or a different kind of event. Different is what we got, in many positive ways. Below are my top takeaways from AWS Re:Invent 2021.

Also see: Top Cloud Service Providers 

AWS Starts Shift Toward Social Awareness

For an organization the size of AWS, the company is remarkably quiet when it comes to climate change, sustainability, social justice, and other ESG issues.

If one looks at presentations from the likes of Cisco’s Chuck Robbins or Nvidia’s Jensen Huang, their focus is on how their technology can be used to make the world a better place. In contrast, the typical Jassy keynote was filled with product announcements; and while we got a heavy dose of that, Selipsky did talk up front about the impact AWS can have on the globe; and why that’s important, as AWS can change the planet in ways others can’t because of its might and scale.

Selipsky also brought in the theme of being a “Pathfinder,” which is an individual or company that continually challenges the status quo and finds new ways to do things, even though the norm is currently acceptable. An example of this is United Airlines; its Chief Digital Officer, Linda Jojo, described how her company used the AWS cloud to digitize the international flying experience – a prime example of digital transformation.

AWS is Going “All In” on Networking

While there were many products unveiled, I thought the two most notable reflected AWS’s entry into networking.

The company announced its own private 5G offering where customers could build, provision and operate a 5G network via the AWS console in a subscription model. Start-up Celona Networks launched an infrastructure private 5G solution earlier this year, which is great for IT shops that take a “do it yourself” approach to networking. In contrast, AWS Private 5G is an excellent option for companies that prefer a turnkey managed service.

The other network announcement was the launch of Cloud WAN, which is a WAN service that is also managed through the AWS console. Customers can build a network, using the AWS global network, or from their carrier. A point and click interface reduces much of the complexity in operating a global network.

In the near term, I anticipate Cloud WAN being used to connect corporate locations to AWS clouds. But given AWS’s ability to disrupt markets, I can see them taking share from the traditional telcos, which tend to have high prices and low innovation. One of the interesting aspects of Cloud WAN is that it’s consumption-based pricing versus flat fee, which could result in significant savings.

Partner Revenue Poised to Accelerate

The Monday keynote was by AWS Head of WW Channels and Alliances, Doug Yeum, who will be moving into a new role at Amazon. Taking over is Dr. Ruba Borno, formerly SVP and GM of Cisco’s CX Centers and Managed Services. One of the interesting aspects of the hiring of Borno is that AWS has elevated the position to a VP role, so clearly the company is expecting big things from Borno – and I think she will deliver.

Although AWS is one of the biggest IT companies in the world, it’s only been selling enterprise services for a little over a decade. Because of this, its partner program is very basic in many ways. Juxtapose this with Cisco, which has one of the most advanced partner programs, and it’s easy to see why Borno was such an attractive hire.

Look for her to take the program to the next level. I clearly see increases in partner focus and revenue.

AWS Competes by Packaging New Solutions

AWS has been the de facto standard in cloud since there has been cloud with Azure and GCP continually looking up at them. In the great AWS vs. Azure vs. Google cloud competition, AWS is the leader hands down.

AWS has, by far, the broadest set of services, which includes everything from app development tools to artificial intelligence to contact center and now networking. At the event it announced updates to the second generation of its ARM-based Graviton processors, artificial intelligence security and even mainframe modernization software.

What’s notable now is that AWS is doing a better job of packaging multiple products into solutions. For example, at the event, Goldman Sachs and AWS debuted its “Financial Cloud for Data” running on AWS, which is a data management and analytics solution for financial clients. Previously, Selipsky had discussed more customized products for specific industries, making this something to watch for soon.

AWS Struggle With End User Focus

For all its success with IT pros, AWS has yet to dent making products directly for end users, despite having a handful of products in this area. For example:

  • WorkDocks is a fully managed, file storage and sharing product but has nowhere near the sophistication of a product like Box.
  • WorkMail is an email and calendar service but it’s hard to find many customers that use it.
  • Chime is a meeting product, and is actually very good, but the company is shifting it to a set of SDKs for developers to use.

The one end user focused product that does seem to be gaining traction is Connect, AWS’s contact center product, which got a boost from COVID. Its consumption-based pricing has helped customers like Hilton Hotels save money, while its AI features enable companies like Traeger Grills to provide significantly better customer service. I’d like to see AWS put a bigger focus on its apps, as I do think it could disrupt in this sector as it has done in IT services.

That’s a wrap on Re:Invent 2021 and I was glad it’s back live. A new CEO, Global Channel Chief and lots of new products should enable AWS to not only maintain its growth trajectory but accelerate it. See you in 2022.

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Top 10 Edge Computing Companies of 2022

Edge computing companies enable distributed computing throughout a network, including to the very edge – hence the name. Rather than process data in massive data centers or using large cloud providers, edge computing companies support deployments that are more far-flung, closer to consumers – even in their homes.

An edge location – which supports processing – can literally be the size of the proverbial breadbox, but they often much larger, from phone booth-sized to shipping containers. They are placed in and around major cities to gather local data or in retail locations. The related term is Internet of Things, or IoT, which typically comprises hundreds or thousands (or more) small sensors.

IoT is intended to gather data, such as telemetry, remove the unnecessary or superfluous data, and send the relevant data up to a data center for processing – or, increasingly, process the data right at the edge. It supports bringing enterprise applications closer to data sources such as IoT devices or local edge servers.

By placing the apps closer to data at its source, edge computing companies can deliver multiple business benefits, including faster insights, improved response times and better bandwidth performance. Because of this, more and more business processing is moving out of the data centers to the edge. Gartner estimates that by 2025, 75% of data will be processed outside the traditional data center or cloud.

Top 10 Edge Companies

How to Select the Best Edge Provider

The market for edge computing providers is in rapid flux – the deals and and offers of today could be markedly different tomorrow. As you settle on a couple of possible vendors, it’s good to have an extensive conversation with that company’s sales reps.

Choose a Pricing Model

Obviously this is one of the biggest considerations. There are a few different pricing models and structures offered for both hardware and edge providers.

  • Consumption-based pricing, or pay for what you use. This is usually used by IaaS and PaaS providers. Hardly any offer a flat rate. This allows the customer to scale up and down as needed, although if need stays high, your budget can be easily blown up.
  • Subscription-based pricing. This is the flat rate method and is used primarily by SaaS companies. You pay per month and have to pay a license for each user, but the trade-off is you can use unlimited resources.
  • Market-based pricing. This is less common because business runs 24/7, but market-based pricing limits when you can use resources. You might be limited to running resource-intensive programs only during off-peak hours, for example.

Evaluate Service, Scalability, Data policy 

Some edge providers may offer development platforms, while others are purely hardware providers. Additional features to ask for when speaking with the sales rep:

  • Scalability and self-service provisioning.
  • Automatic backup and restore/disaster recovery.
  • Automatic maintenance and patching.
  • Servicing remote sites vs. you have to go to the site.

Additionally, some customers have gotten a rude surprise when they went to take their data back from a remote provider. For starters, data retrieval rates can be exorbitant if you have a lot of data in a remote location. The costs and terms will be spelled out in the service agreement – which you should check very carefully.

Consider the Potential for Lock-In

No one has a single cloud provider, the draw from multiple providers. But the vendors would like you to think otherwise. This leads to a tendency for vendor lock-in, where hardware, protocols, apps, and other software are completely proprietary and don’t allow you to migrate cloud workloads from different platforms, especially from on-premises servers to the edge.

Again, in your due diligence, check the details to ensure that any vendor you work with supports interoperability standards and will not hamstring you should you decide to move or at least interoperate with another edge company.

Top Edge Computing Companies 

Our list of the top 10 major edge providers covers both hardware and services providers, since both are integral to the edge. It is in no particular order.

AWS Edge

Edge computing value proposition: As the leader in cloud computing, AWS is investing heavily in edge computing as well, which means they offer an extensive toolset. So AWS has offers for both SMB and large enterprise – but the user interface won’t be simple.

AWS may not have edge locations scattered around big cities but AWS offers a considerable amount of cloud-edge hybrid services for a uniform experience across the edge environment. AWS includes services and solutions that include hybrid cloud, IoT, AI, industrial machine learning, robotics, analytics, and compute services. If you can imagine it, AWS probably has it.

AWS claims to have more than 200 integrated device services to choose from, and sells its own: Alexa and Echo. It also provides solutions like its Connected Vehicle solution, IoT Device Simulator, and AWS IoT Camera Connector.

AWS’s interface is known to be complex, so those companies that will handle their edge infrastructure themselves will need considerable in-house expertise.


Edge computing value proposition: The emerging concept of “observability,” which refers to the ability to closely monitor a far-flung platform, is quickly becoming a must-have for enterprise customers; this is a strength for EdgeConnex.

EdgeConneX’s business model is to place data facilities where they’re needed the most for better network and IT connectivity. It works with customers to ensure tailored scalability, power, and connectivity.

Through its Far Edge services, it offers more than 4,000 points of presence outside of its hundreds of data centers worldwide. EdgeConneX’s Far Edge use cases include artificial intelligence and machine learning, fast media streaming, and (of course) IoT devices.

EdgeConneX offers EdgeOS, a self-service management OS for data center infrastructure management (DCIM) providing customers with a single, secure view into their infrastructure deployed in any location across its global footprint.

ADLINK Technology

Edge computing value proposition: ADLINK’s core focused is embedded computing; this and its international presence makes it ideal for an edge project that spans global borders.

Taiwan-based ADLINK has a network that spans from the US to Germany to China, encompassing some 40 countries in total. Unlike some companies on this list ADLINK that offer edge services in addition to cloud and other IT services, ADLINK is specifically focused on the embedded computing sector. If you’re looking for

In addition to its widespread operational presence, the company has design centers in Asia, the US, and Germany. ADLINK speciality within edge computing includes IoT hardware, software, AI software, and robotics solutions. Its product line includes computer-on-modules, industrial motherboards, data acquisition modules and complete systems, with emphasis on the aerospace, manufacturing, healthcare, networking, communications, and the military sector.

The company touts its focus on artificial intelligence, which is an exceptionally important tool for monitoring and managing the edge, which is essentially impossible using human staff alone.

Vapor IO

Edge computing value proposition: An edge computing company with a “1+1=3” strategy, meaning that they focus on interoperation with other tech firms – a particularly significant strategy in the edge world, in which cooperative networking is so essential.

Founded in 2014, and arguably a leader among the new cohort of edge startups, Vapor IO develops hardware and software, and has edge-to-edge solutions called Kinetic Grid platform and Kinetic Edge architecture, which are designed to enable customer data delivery and processing across global borders. The company is based in Austin, Texas.

Vapor IO develops a collection of edge colocation and interconnection facilities colocated with wireless network. The company is actively building fiber backbones in numerous markets.

The company builds portable data centers about the size of a shipping container, “micromodular data centers,” that are placed at wireless base stations or wherever they are needed. Vapor IO serves wireless carriers, cloud providers, web-scale companies, and other enterprises.


Edge computing value proposition: Mutable’s mission is to get edge infrastructure close to remote processors – very close. It uses “micro” data centers to support applications on its platform.

Closer to a start-up – launched in 2013 – but a compelling example of the future of edge companies. Mutable is a public edge cloud platform that patterns itself as an Airbnb for servers. If you have some underutilized servers sitting idle, you can loan them out to businesses in the area that need extra capacity, so long as they are within 40 kilometers or less, and turn your idle servers into a new revenue stream. This is done through its Mutable OS edge computing software solution.

Because of the close distance requirement, Mutable’s Public Edge Cloud ensures latency rates of less than 20 milliseconds. It offers a 5G network in addition to wired connectivity. All stacks, snapshots, containers and related services operate in an isolated environment. Mutable’s other edge computing tools include Mutable Node, Mutable Mesh and Mutable k8s Platform.

This concept of hyper-low latency is definitely a vision of where the future of edge is headed. If edge is fast and highly responsive, it grows and succeeds.

Microsoft Azure

Edge computing value proposition: With perhaps the widest infrastructure base in in the tech industry, from cloud to data to AI, Microsoft is focused on winning big marketshare in edge, and is investing accordingly.

Microsoft’s Azure IoT is second only to Amazon in terms of market size, but there is more to it than that. It offers Azure SQL Edge, an edge version of its powerful SQL Server database, offering data streaming, time series, and database machine learning. Its IoT Plug and Play lets users connect IoT edge technologies to the cloud without having to write a single line of embedded code.

Microsoft recently launched Windows 10 IoT Core, a derivative of Windows 10 designed for compact devices such as a Raspberry Pi board. Many of Microsoft’s edge computing capabilities are extensions of the Azure cloud platform and it offers Azure Stack Edge to facilitate development and migration.

Azure Stack is an on-premises version of Azure meant to be run internally in a company data center. Azure Stack Edge lets companies develop and upgrade their edge apps on-prem, and when they are ready, deploy them to the edge. Microsoft is a player to watch in the edge sector, clearly.


Edge computing value proposition: Edge is an environment that prizes managing and monitoring application workloads across regions; MobiledgeX’s cloud platform offers the cross-interface functionality with an abstraction layer; you might think of it as virtualization for the edge.

Definitely geared for large enterprise customers, MobiledgeX was launched by Deutsche Telekom AG in 2016, and offers automation and orchestration in a multicloud environment.

MobiledgeX offers a marketplace of edge computing services and resources that will connect developers with telecom operators like British Telecom, Telefonica, and Deutsche Telekom. MobiledgeX Edge-Cloud platform helps simplify deployment, management and analytics for developers of their apps to run on telco edge clouds.

MobiledgeX Edge-Cloud Platform allows developers to autonomously manage software deployment across the distributed edge network infrastructure from a number of operators, using a unified console.

Schneider Electric

Edge computing value proposition: A large player with the expertise and personnel for heavy duty edge projects, Schneider offers a extensive menu of enterprise IT services to support edge deployments.

A giant in Europe – it’s a French multinational – Schneider Electric is making a big push into the U.S. market with edge data center products, including ruggedized racks and storage units, purpose-built all-in-one units, and wall mounted units where floor space is at a premium.

It also offers EcoStruxure, a DCIM software package for managing servers remotely. Schneider owns UPS specialist American Power Conversion and APC products are frequently a part of its offerings.

Additionally, Schneider supports projects for everything for data centers to corporate headquarters to homes, and has strength in services and automation.


Edge computing value proposition: As the leading name in the colocation sector, Equinix has a deep legacy of expertise in enterprise IT – and it has the physical infrastructure to support big ambitions in edge computing.

Equinix is the largest American data center provider as well as around the world. Its primary focus is colocation, where a customer puts their compute systems in Equinix data centers, so the customer doesn’t have to maintain a data center facility.

Its edge strategy dovetails from this, in that Equinix’s goal is to help large enterprises quickly shift their IT infrastructure to colocations in major cities as needed without having to build their own infrastructure. Equinix also offers a variety of virtual network services to improve edge performance and reduce latency.


Edge computing value proposition: In a world in which many large tech companies are adding edge capability to an already large feature set, ClearBlade offers an image of the future in which companies launch to focus deeply on the edge itself.

ClearBlade is another startup purely focused on IoT and the edge. ClearBlade Edge allows customers to develop compute services and solve business problems from a single platform. It also offers real-time location and asset tracking, and its middleware platform helps build and connect systems to IoT without coding.

This no code focus is a fascinating take on the union of edge and trend within digital transformation to allow all users to build and upgrade applications. The company’s no code IoT Application edge-native computing.

Its primary products include its ClearBlade Enterprise IoT Platform, ClearBlade Edge IoT Software, and ClearBlade Secure IoT Cloud. It is particularly focused on the transportation, energy, and health care sectors.

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Tableau’s Jackie Yeaney and Data Society’s Merav Yuravlivker on Improving Data Literacy

I spoke with Jackie Yeaney, CMO of Tableau, and Merav Yuravlivker, CEO of Data Society, about what it means to be “fluent” in data – and techniques to improve this fluency.

Among the topics we discussed:

  • In terms of data literacy and effectively mining data, what is your sense of where many companies are now? Struggling? Relatively mature?
  • If a company wants to improve its data literacy, to build a culture around effective data usage, what advice would you give?
  • As they build this culture, what would you expect some natural challenges to be? How can they be addressed?
  • Your sense of the future of data literacy in organizations over the next few years? When will expertise be a default?

Listen to the podcast:

Watch the video:

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Data Mining Techniques

Data mining is the umbrella term for the process of gathering raw data and transforming it into actionable information. Due to the dramatic growth of user-friendly data visualization tools, data mining is becoming more common for the everyday user – which makes effective data mining techniques that much more important.

Additionally, data mining is a foundational element of artificial intelligence and machine learning, which is a key reason that investment in data mining is increasing at a solid clip.

There are a number of techniques business leaders and staffers should learn to hone their data mining skills – the list grows with time.

Leading Data Mining Techniques

Here are some fundamental data mining techniques that both analysts and non-analysts can apply in their operations. Remember, don’t be afraid to start small; this is a complex activity and it takes great practice.

Select The Optimal Tools

One fundamental step to make all your processes easier is selecting the right tools for data analysis. Selecting the optimal tools will not only make data mining easier to accomplish, but it assists you with maintaining larger databases. This is especially important when considering the fact that databases are growing far too large for traditional means.

Make sure you have strong data quality and data analytics tools. This ensures you have clearly presented, graphically displayed data to mine and analyze. Data quality tools in particular can help you with data cleansing, auditing, and migration.

Pattern Tracking

One of the most fundamental and easy to learn data mining techniques is pattern tracking. This is the ability to spot important trends and patterns in data sets amid a large amount of random information.

In fact, every data mining technique stems from the idea of pattern tracking. Honing your pattern tracking skills can allow you to drill down on your data with more advanced techniques. Try finding patterns without any predetermined goals to practice your pattern tracking.


Association is one of the simplest data mining techniques users can leverage – it’s one of the first data mining techniques users can leverage once they’ve practiced their pattern tracking. Association boils down to simple correlation.

It is similar to pattern tracking, but leverages dependent variables. For example, in a data set of customer purchases, you might find that users who bought milk more often than not also bought cookies in the same transaction. This is a relatively fair association to make.

Association can be helpful, but could potentially misdirect users. Users should remember that correlation does not equal causation, and outside factors should optimally be considered in any data mining technique.


Classification is the process of leveraging shared characteristics to understand groups. These classifications can include age groups, customer type, or any other factor you please.

Classification’s strength is that it can get as specific as you need it to be. You can classify customers with as much information as you’re able to extrapolate. Be sure to connect with your sales and marketing team to ensure your predetermined classes are correct.

Classification is often confused with another data mining technique, clustering. As we’ll see later on, both techniques offer stark differences for businesses.

Outlier and Anomaly Detection

Anomaly detection can serve as an effective data mining technique for any analyst and non-analyst. This is the practice of tracking your data, and specifically looking for any outliers.

Anomaly detection is very effective for training business leaders and employees on correlation and causation. This is because anomalies are not inherently a bad thing.

For example, if you notice a huge spike in sales for a product that historically hasn’t done so well, don’t jump to conclusions. Make sure you’re in contact with different facets of your business, including your sales and marketing teams. These teams could give insight into why these spikes are occurring.


Clustering is very similar to classification. It is the technique of grouping clusters of data together based on similarities you’ve tracked. The primary difference between clustering and classification is that classification works with predefined classes.

Clustering does not use pre-labeled data or training sets. And because of this, it is less complex than classification. Clustering can be a very effective way to discern objects from one another. From here, you can create customer profiles and drill down on your data.

Regression Analysis

Finally, regression analysis is the technique of analyzing the relationship among all your variables. In other words, it is the practice of making predictions based on the data you currently have.

Regression analysis is the primary way data scientists and businesses identify the likelihood of any given variable.

You select the variable you’d like to analyze, or your dependent variable and the data points you believe affect that variable, or your independent variables. From there, you could leverage regression analysis to understand the exact relationship between these two data sets. Ultimately, regression analysis is the primary way users new to data mining can gain a deeper understanding of their data sets. It’s a method that goes beyond simple causation and correlation.

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What is Data Mining?

As IT departments and businesses across all sectors handle larger quantities of raw data, processes have been created to turn this data into useful information.

Data mining is the umbrella term for this process. Data mining has a rich history, and with the advancement of technology as a whole, has had many different definitions over the years. We’ve gathered information on the history of data mining, key use cases, as well as its future. Clearly data mining is a concept that is core to today’s digital transformation efforts.

What is Data Mining?

Data mining is the process of turning raw data into usable information for business. The most common way this is done is through various data mining software solutions that look for patterns in data.

Data mining a subset of data analytics. Additionally, data mining is a foundational element of artificial intelligence and machine learning.

There are a number of techniques that have been developed over the years to practically apply and practice data mining. Each technique is built on the fundamental idea of tracking patterns in a set of data. From here, you can hone in on your data mining methodology depending on your project’s focus and the depth of your research.

For example, you could use association to simply correlate multiple dependent variables. Conversely, you could dive deeper and leverage outlier and anomaly detection to sift through large data sets and spot any anomalies. Although these techniques are widely used today, it’s important to understand the history of data mining and how these techniques have changed.

History of Data Mining

Surprisingly enough, the concept of data mining stretches back all the way to the 1700s. Bayes’s theorem and regression analysis are both early examples of identifying patterns in raw data sets. Although these methods were used for centuries, the term “data mining” first appeared in 1983, in an article published by economist Michael C. Lovell.

Originally, Lovell and a slew of other economists viewed the practice in a negative light. They believed that modern data mining could lead economists and business leaders to falsely correlating data that is not necessarily relevant. Regardless, the phenomenon grew in popularity, and by the 1990s, data warehouse vendors leveraged the term for marketing purposes.

One of the most important events in the history of data mining is the Cross-Industry Standard Process for Data Mining (CRISP-DM). This was a standard created by a number of companies in 1996 to help standardize the process and prevent the issues brought up by Lovell and his peers. The process includes six critical steps:

  1. Business understanding
  2. Data understanding
  3. Data preparation
  4. Modeling
  5. Evaluation
  6. Deployment

This model has continued to be iterated, and even received an updated version by IBM. As technological advances have been made, data mining has become a more complex and flexible process – and far more powerful as well. In today’s market, effective data mining is critical for competitive advantage.

The primary issue data mining aims to solve in the present day is analyzing the sheer abundance of data that is produced. Data mining has seen major advances – and in fact has strained the capacity of IT departments – with the growth of machine learning and artificial intelligence.

Data Mining Use Cases

Data mining has various use cases depending on the industry vertical it’s being applied in. Here are a few key examples.


Data mining can be used to uncover the relationships between diseases and treatment effectiveness. In a high-level sense, data mining can be used to identify new drugs and bring timely care for patients. One of the most effective data mining uses cases is to detect suitable treatments by comparing and contrasting symptoms.

It can also help with detecting fraud for healthcare companies. Users can leverage anomaly detection to identify outliers in medical claims, whether they’ve been made by physicians, labs, clinics, or others. You can also use outlier detection to track referrals and prescriptions that stray from the norm.

Data mining for fraud detection in the healthcare industry is frequently used. For instance, the Texas Medicaid Fraud and Abuse Detection System leveraged it to recover over $2 million in stolen funds and identify over 1,000 suspects.

Intelligence Agencies 

Data mining can be very important for intelligence agencies to determine crimes at all levels. This includes money laundering and various forms of trafficking. This is yet another vertical that relies heavily on anomaly detection for its data mining purposes. This helps intelligence agencies such as the FBI to anticipate, prevent, and respond accordingly to various crimes.

In the same vein, data mining could be leveraged for cybersecurity purposes. Applications can detect anomalies and learn from data sets to prevent further attacks. There are many AI cybersecurity firms working on developing these sorts of tools currently.


Data mining is leveraged with enormous effectiveness for sales and marketing. The main use case is detecting hidden trends inside purchasing data. This can help marketing firms and companies plan and launch tailored campaigns.

Sales teams can leverage customer data to reach out to them through their preferred methods. Generally speaking, data mining in marketing is best used for a holistically more tailored user journey. This could either relate to the purchasing journey or customer support journey. Another example would be analyzing which products are often purchases in tandem. This helps businesses gain a greater understanding of what overall packages and user carts look like.


Similar to the marketing and sales examples, data mining can be leveraged by banks and financial institutions to learn from and predict customer behavior. You will usually see financial institutions utilizing data mining to increase their overall customer loyalty.

This can allow companies to release relevant services to customers. Similar to the way intelligence agencies use data mining, financial institutions can employ data mining to identify either fraudulent or non-fraudulent actions. Again, anomaly and outlier detection is a major factor in this pursuit.

Financial analysts can also use data mining to understand purchasing and sales trends. They can pull from sales data to track peaks and dips. Advanced data mining techniques that take outside factors into account, such as holidays or seasonal promotions; and more specifically, what factors drove these purchases.

The Future of Data Mining

As customer data continues to grow, the future of data mining is continually considered and planned for by business leaders. Here are a few key directions and paths data mining could go in the upcoming years.

Data Mining in the Healthcare Industry

The healthcare industry has historically been at the forefront of data mining, leveraging it to understand patients and treatments better. One of its most effective applications has been found in signal detection, which is the process of discerning valuable pattern inside random information.

With the arrival of the pandemic, advances in data mining techniques specifically for pharmaceutical testing and clinical trial processes were made. Expect data mining to find some of its most ambitious applications in this field, with some data scientists using it to analyze DNA sequences.

Integrated Data Mining

Data mining historically has been accomplished either through proprietary software or other external means. Now, data mining features are entering a number of CRM SaaS platforms for various purposes, the main one being cybersecurity.

This has become even more important during the age of digital transformation. As key stakeholders and business leaders understand the value of modernizing their business operations, software vendors are adapting and creating low-code solutions. These solutions, alongside the ongoing data democratization movement, are opening up data mining possibilities for the everyday user.

Integrated data mining has a few benefits. The first benefit is that it introduces data mining and data as a whole in a more user-friendly way for employees. Because of this exposure, businesses can expect a general upskilling of employees. The second benefit is the fact that businesses can now gain a wider breadth of data analysis. This is because integrated data mining opens up data analysis for departments outside of your IT team.


The concept of hyperautomation will surely include data mining processes alongside it. Hyperautomation describes the approach some businesses are taking in identifying and automating as many business and IT operations as possible. This means that businesses are rapidly adopting artificial intelligence, machine learning, and robotic process automation software.

Many data mining solutions already leverage machine learning and artificial intelligence to deal with and analyze mass quantities of data. In fact, this is one of the reasons why interest in the AIOps space is rapidly growing. Hyperautomation and AIOps could be the keys to one of the main issues facing data mining today: too much data for humans to handle without assistance. Certainly, data mining aided by AIOps will play a key role in this challenge.

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AIOps Provides a Path To Fully Autonomous Networks

If you’re an IT professional, you’ve heard of AIOps or artificial intelligence for IT operations. The term, originally devised by Gartner in 2017, describes the process of managing data from an application environment using artificial intelligence. While the concept is simple, AIOps is actually quite difficult to put into practice.

AIOps combines machine learning (ML), behavioral ethics, and predictive analytics, and it involves massive amounts of telemetry information being generated by network devices. There are many automation tools out there that work well in static environments. But modern environments aren’t static and are constantly changing, which means organizations need tools that can keep up with the changes.

A new 2021 State of AIOps Study, published by ZK Research in partnership with Masergy, found IT departments spend nearly 50 percent of their time monitoring app performance and network troubleshooting. It takes IT departments on average 30 minutes to fix and resolve customer-facing issues. That’s a half hour of interrupted service.

With AIOps, critical application performance is significantly improved. The critical apps are always observed by the ML system. The traffic patterns are understood, and the network responds to those needs dynamically.

I recently spoke with Ajay Pandya, director of product management at Masergy, about the study’s key finding and how AIOps trends are paving the way for fully autonomous networks. Highlights of my ZKast interview, done in conjunction with eWEEK eSPEAKS, are below.

    • 2021 State of AIOps Study was conducted to understand where AIOps has business value for IT and where the technology is headed in the future. ZK Research surveyed 500 U.S.-based IT decision makers and C level executives across seven verticals: technology, manufacturing, retail, healthcare, finance, media and communications, and pro services. The study focused on enterprise-class companies with $250 mil. to $10 bil. in revenue.
    • A few surprising findings: 64 percent of IT leaders said they’re already using AIOps, while 55 percent said they’re using AIOps for both networking and security. The majority (more than 90 percent) believe AIOps plays an important role in managing the network.
    • The study also found organizations are benefiting from AIOps. The top use cases for respondents include cloud app analytics, performance improvement, network service optimization, and faster threat detection and response. Sixty-four percent of the respondents measure AIOps success based on IT operational efficiencies, and another 54 percent on improved network or app performance. This shows there is real business value in an AIOps investment, both making AI more efficient and improving business operations.
    • Additionally, 84 percent of IT leaders see AIOps as a path to fully autonomous networks. In fact, 86 percent expect to have a fully automated network in the next five years and 97 percent are confident that AIOps can be trusted to act alone. These predictions may not happen in the next five years, yet AIOps will be a major investment area.

Key Takeaways from the AIOps Study

  • The era of AIOps is here. Companies that haven’t started looking into it yet need to start the process now, before they’re left behind. Although AIOps can lower costs, it must be implemented for the right reasons, such as IT transformation.
  • While IT leaders recognize there is a problem, the findings don’t reflect true use of AIOps. Many solutions claim to be AIOps but are actually rules-based systems. AIOps is a closed loop system, which takes incorrect data and feeds it back into the system to become part of the training set. This is what makes AIOps smarter over time. IT buyers need to be careful and make sure they’re choosing the right solution.
  • AIOps requires both software-defined networking (SD-WAN) and secure access service edge (SASE). Companies should find providers that bring those together in a central place for automation. Most organizations trust AIOps to run their networks. However, organizations won’t be fully autonomous until they move their network and security into software, which requires both SD-WAN and SASE.
  • There is a strong link between AIOps and SD-WAN. A non-SD-WAN network is not centrally managed or centrally orchestrated since data is siloed. SD-WAN provides the centralization needed to make networks more efficient. In the study, more than two thirds of companies (73 percent) identified SD-WAN modernization as a top investment and as a prerequisite for AIOps.
  • Next-gen enterprise networks are perfect candidates for AIOps, whereas legacy networks weren’t designed for it. SASE will have a larger presence in enterprise networks in the next two to three years. That’s where AIOps will come into play. According to 70 percent of IT leaders, AIOps performs really well in a SASE architecture.
  • AIOps is a journey rather than a destination. As apps move to the cloud, a digital transformation will take place within networks. Organizations are headed toward self-healing, self-managed networks and IT leaders are putting more trust into AIOps to help with day-to-day operations.

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How to Know If Your Company Needs a Digital Experience Platform (DXP)

Modern companies must provide a comprehensive digital experience for prospective and existing customers across multiple platforms. Consumers have multiple touchpoints with digital brands, from the company’s main website to its social posts and from mobile applications to the in-store experience.

For enterprise corporations, these various points of communication and engagement with customers are managed by multiple team members across multiple departments using multiple marketing technology tools. While an essential practice in digital transformation, it can be inefficient and cumbersome to convey a cohesive message or a personalized experience when multiple platforms and strategies are at play.

Large companies are making the shift to digital experience platforms that manage all of these elements from one integrated system.

Also see: Top Digital Transformation Companies

What is a digital experience platform?

Here is Gartner‘s definition of a Digital Experience Platform: A digital experience platform (DXP) is an integrated set of core technologies that support the composition, management, delivery, and optimization of contextualized digital experiences.

The main allure of a system like this is that everything is managed from the same centralized system (or technology platform) instead of having different tools for web content management (WCM or CMS), analytics, marketing, personalization, commerce, resource management, and others.

The end goal for any digital company is to provide a seamless, connected, and personalized customer experience no matter where the customer “meets” the brand. Connecting internal operational systems to external customer experiences just makes sense. Analytics and behavior data can be shared across multiple channels for smarter customer journeys and experiences.

When should you consider moving to a DXP?

While you certainly do not need a DXP to craft a compelling digital experience across multiple channels and pull customers in, the platform does offer a set of benefits you can’t find elsewhere. Without a DXP, companies pull from multiple sources of data and analytics, manage content on multiple platforms, and have a more expansive marketing technology stack. This may serve some companies well, but they won’t get the same degree of actionable insights that are inherently included in a digital experience platform.

But choosing to implement a DXP takes significant investment, and it will only yield the best return if it suits the needs of the company. Marketing leaders should consider the overall goals and needs of their brands before adopting a digital experience platform.

Do multiple stakeholders have significant stakes in digital?

A properly implemented DXP will make company operations easier, serve the needs of multifunctional teams and stakeholders, and produce a positive customer experience overall. Companies that want to implement a platform like this should look at the variety of martech investments and tools for their digital efforts and assess if one provider would bring efficiencies and data knowledge needed to create a better customer experience.

At its most basic definition, a DXP provides an omnichannel collaboration for anyone who manages the company’s websites, pulls and consolidates information from analytics, contributes to marketing efforts, creates personalized interactions, manages e-commerce, or creates and distributes assets and resources. That list covers everything from IT to marketing.

When considering a DXP, ask if your team members need a single, integrated system or one provider that provides for multiple departments or if they can sufficiently maintain the customer experience using a variety of tools.

Do we have the resources necessary to install and maintain a DXP?

Digital experience platforms are usually selected by organizations that have in-house or external agency tech teams that can support the ongoing needs of the system. In fact, your technology access and expertise should be a part of your DXP selection consideration. Some of the leading DXP providers and technology platforms are:

  • Adobe Marketing Cloud (Adobe Experience Manager CMS) – Java
  • Sitecore Experience Platform (Sitecore CMS) – Microsoft
  • Acquia Open Digital Experience Platform (Drupal CMS) – Open Source

This is because the system has a significant number of (metaphorical) moving pieces. Implementation of a DXP is also a long-term investment. When companies choose a digital experience platform, they’re usually looking to support long-term marketing initiatives. With this in mind, it’s necessary to have accessible tech resources at the ready when working with a DXP.

Can a connected and personalized digital experience support the company’s marketing goals?

The power of a DXP is that it not only manages the company’s website but can integrate social media, in-store experiences, email campaigns, and other initiatives all from the same platform. For companies looking to develop their brand recognition and elevate their brand experience, a DXP is an incredible asset because it provides critical insights into consumer behavior and makes it easy to manage digital content from the same space.

Unlike other content management systems, which perhaps act as one part of a larger tech stack, a digital experience platform is comprehensive. The overall goal of digital marketing should be to provide a comprehensive experience, and this is why a DXP can be such a useful tool. A digital experience platform makes the most sense for companies that have multiple websites, channels, and digital experiences because it ties them all together with actionable, data-driven insights from AI and machine learning tools.

A digital experience platform can help companies achieve digital adoption and create a more cohesive customer experience. Marketing teams will benefit from cross-channel insights with a degree of precision that cannot be achieved without a comprehensive, AI-driven technology like a DXP.

For companies with the resources available to support and maintain a comprehensive system like this, digital adoption and digital success can be achieved with a digital experience platform.

 About the Author: 

Steve Ohanians, Co-Founder and CEO, WebEnertia 

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How Dell’s OEM Strategy Addresses the Current Market

Although the concept of original equipment manufacturers (OEMs) is deeply embedded in tech industry and culture, most people focus on the OEM relationships between large hardware vendors and their component partners or between commercial ISVs and PC makers.

Those interactions are akin to OEM interdependencies in every other manufacturing sector. For example, specialist subcontractors contribute everything from light bulbs, wire harnesses, paint and interior appointments to the cars and trucks that roll off automaker assembly lines.

But there is another less recognized and understood OEM dynamic where system vendors provide the digital brain power for a wide variety of compute-enabled devices, so important for edge computing. They work in collaboration with partners, in a world driven by advances in cloud computing, artificial intelligence and machine learning. These solutions range from products utilizing relatively simple embedded PC components that never see the light of day to full-fledged systems and appliances that are sold under the OEM customer’s name and brand.

Dell Technologies has been proactively involved in this latter form of business since 2000, and Dell’s Kyle Dufresne, Global SVP and GM of the company’s OEM Solutions, recently blogged about reaching this milestone. Let’s consider Dell’s OEM efforts and how their evolution addresses the demands of today’s market.

The Dell OEM Evolution

What began as a supportive response to ad hoc requests from Dell’s customers has grown into a substantial multi-billion-dollar business serving the needs of clients in over 40 vertical global industries.

Though many IT vendors pursue OEM markets and partnerships, the duration of Dell’s efforts and its continuing evolution set it apart from most vendors. In addition, some fundamental points and goals have contributed to the longevity and success of the company’s program.

As Kyle Dufresne wrote, Dell’s OEM Solutions division was formed to meet “demand for hardware that didn’t yet exist” that was “specialized to meet the requirements of projects that (customers) had in the works.”

In 2000, many companies used onboard compute features for standalone products like commercial video game machines, while others expanded on network or internet connectivity, including third party automatic teller machines (ATMs). Dufresne noted that OEM customers also seek industrial-grade solutions able to withstand harsh environmental conditions and to be capable of functioning in remote, offshore locations. To that end, Dell began by “adding some extra battery life here, ruggedizing a server there, creating unique and custom-made solutions for every customer.”

Dell’s OEM Solutions division employs more than 700 professionals who help develop, customize, design, industrialize, transform and innovate solutions that meet the essential requirements of customers in verticals, including healthcare, telco, transportation, manufacturing and public infrastructure. In addition, the company is helping customers in emerging areas and use cases, such as 5G network development, testing and deployment.

Emphasizing Dell’s “do-anything-from-anywhere-world” strategy, the OEM Solutions division also offers customers a wide range of management and support services, as well as a specialized channel partner program.

OEM Use Cases 

What sorts of companies work with Dell OEM Solutions to develop new and leading-edge products? Here are two recent customer examples to consider:

Konica Minolta 

A Japanese multinational operating in 150 countries, Konica Minolta is finding synergies between its decades of optics and imaging experience and emerging technologies, including AI. One of its focus areas is enhancing traditional digital imaging processes, such as conventional X-rays for use in applications where physical motion can contribute to accurate diagnosis. To that end, Konica Minolta developed a recording system and software called “Kinosis,” which takes a series of X-ray images at high speed and low radiation to produce cine loop sequences that enable clinicians to see the dynamic motion of anatomical structures, such as the movement of lung tissue.

To launch Kinosis commercially, the company needed a platform that would meet the high reliability and compliance standards healthcare solutions require, and also connect seamlessly to legacy X-ray and picture archiving and communications systems (PACS). Konica Minolta’s longstanding relationship with Dell led to a natural alliance with the OEM Solutions division and resulted in Kinosis offerings that run on Dell’s Precision workstations.

Along with providing the hardware foundation for Kinosis, Dell OEM Solutions also loads Konica Minolta’s OS and BIOS software onto systems at the factory, manages software updates and provides global support services. As a result of its partnership with Dell OEM Solutions, Konica Minolta’s Kinosis solutions have allowed hospitals and clinicians to effectively and cost effectively enhance diagnosis processes and patient outcomes.


VIAVI develops and delivers virtual testing, measurement and assurance solutions for global telecommunications vendors and network operators. Among those offerings is the company’s TeraVM 5G, a core emulator that enables customers to validate next generation products and scenarios. Those are vital processes when it comes to 5G, a fifth-generation high performance technology that promises to fundamentally transform wireless services and solutions.

As Amit Malhotra, VP for programs at VIAVI Solutions noted, “5G isn’t just about smartphones, tablets or consumer devices. It will ultimately enable connections with anything that has a chip in it. That requires a huge scale-up by telcos and network operators to support limitless endpoints.”

VIAVI partnered with Dell OEM Solutions to develop TeraVM into an emulation and security performance solution that network manufacturers and operators can use to stress test radio access networks with tens of thousands of base stations and millions of end-user devices under real-world conditions. A virtualized solution that can be deployed anywhere—in labs, data centers or cloud infrastructures—TeraVM runs on Dell EMC PowerEdge R740XL servers that can emulate millions of end-user devices and other endpoints and scale up to 1 Tbit/s of simulated network traffic.

Along with providing the core platform to support TeraVM, Dell OEM Solutions also works closely with VIAVI to integrate its custom designed field programmable gate arrays (FPGAs) into PowerEdge systems. As a result of its partnership with Dell, VIAVI has been able to satisfy the requirements of customers developing myriad 5G-focused solutions, including high speed wireless replacements for traditional cable services, mobile services for smart phones and the world’s first 100 percent cloud-native mobile network.

Collaboration and Adaptability 

At one level, the success of Dell’s OEM strategy demonstrates how vendors can generally create new value and solutions with the help of strategic partners. At another, it highlights the specific efforts of a company that is particularly skilled in identifying and pursuing opportunities in burgeoning commercial markets.

But a clear point is how purely adaptable the Dell OEM Solutions organization is in both technological and practical terms. In part, this reflects the sheer variety of Dell’s solutions and services portfolios, and the company’s development of sustainable new offerings for hundreds of discrete global markets.

But more fundamentally, the OEM Solutions division provides a microcosm of Dell’s continuing focus on meeting the unique requirements of tens of millions of global business customers, which has never been more important as the future of business computing – indeed, the evolution of the digital transformation – is unfolding at the edge.

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4 Benefits of Digital Transformation

The benefits of digital transformation derive from the combination of its two key building blocks, technology and people. Clearly, it’s technological advances that make digital transformation possible – big leaps in cloud computing, data analytics, edge computing, and artificial intelligence. On the other hand, you’ll often hear that “digital transformation is about people, not technology.”

However, at its most advanced, digital transformation is not precisely about people or technology, but about the relationship between people and technology. When these two powerful entities are merged with a digital transformation strategy, a business reaps major productivity benefits that neither element can deliver by itself.

Indeed, the benefits of digital transformation are myriad – there are easily a dozen or more major advantages. But let’s explore the four central benefits. It’s these four benefits of the symbiotic relationship between people and technology known as digital transformation that are transforming businesses.

Major Benefits of Digital Transformation 

One note as you explore these leading benefits: digital transformation is a highly dynamic process. Currently, the foundational technologies are data, AI, and cloud, but in the future they could be the metaverse, 5G and quantum computing. The technology shifts, but the core principle remains improved productivity.

Data Becomes a Major Competitive Advantage

While digital transformation is about the relationship between people and technology, at the very core of this relationship is data mining and data analytics. It is the constant and effective analysis of data that tracks and transforms the all-important people-technology relationship.

Companies have embraced data analytics over the last several years, yet most companies – even today’s cloud-native companies – suffer from multiple challenges in this early era of the mass adoption of analytics. As an example, experts estimate that only a limited percentage (well under 40 percent) of data is ever analyzed, meaning major insights are missed.

In contrast, a digital transformation strategy focuses intently on optimal data use, enabling the following benefits:

Data is no Longer Siloed

When data silos are dismantled, key metrics flow easily between departments, allowing dynamic interdepartmental collaboration. For instance, when the sales team understands all the relevant metrics from the business development team, it produces competitive synergy. An organization become a single collaborative entity focused on success.

Data Analytics is Set up to Scale

For many companies, data platforms are geared to service only today’s needs, with no built-in capacity to scale as needed. A well-planned digital transformation practice ensures that its data analytics infrastructure is flexible and ever-scalable; if the business grows by 5 percent or by 20 percent a year, the analytics platform scales with ease.

Decisions Based on Metrics, Not Instinct

It’s a great strength of the entrepreneurial system that businesses have been built by one or a few individuals with a natural gift for understanding markets. Yet as markets have grown more competitive, running a business by “feel” is limited. Embracing digital transformation enables a framework of regularly scheduled metrics to navigate business decisions based on actual market direction, rather than quirk or mood.

A digital transformation strategy embeds a sophisticated data analytics practice throughout the organization, in the C-suite, across the various departments, and at all levels of the org chart. The intelligent metrics derived from this practice informs smart, fast, highly flexible decision making. Companies have a Chief Data Officer (CDO); even SMBs have an expert handling this role, perhaps without the executive title.

Artificial intelligence Emerges as a Key Team Member

Though still in its infancy, artificial intelligence is rapidly becoming more practical to deploy, which provides companies that invest in digital transformation the benefit of AI’s most next-gen capability: the ability for systems to learn and develop without human assistance. The concept of a “smart machine” is a central pillar of digital transformation.

This self-learning technology means that the output of AI toolsets/platforms will – increasingly over the next several years – resemble the output of human staffers. AIOps deployments will no longer be merely support systems, but independent teams that will:

  • Create and accomplish new and important business tasks.
  • Constantly scan the business and its infrastructure for areas of improvement.
  • Dramatically increase customer engagement.

All of this AI-based activity will, paradoxically, increase the value of human staff. They will be freed up for upper-level work that requires the judgement and creativity that maximizes their value.

Customer Relationship is Strengthened

A leading pillar of digital transformation is optimal use of social media. The challenge here is that social media has no clear line between promotion and communication. Some companies use social media purely as a promotional outlet, constantly posting about sales offers. While this might offer gain, a smart digital transformation strategy turns social media into a communication system, a platform for dialoguing with customers.

Why is this important? Because the most profitable benefit of digital transformation is a better relationship with your customer. This truth is often lost amid a focus on the remarkable advanced technology that powers this emerging trend.

Social media is a compelling conduit for nurturing this customer relationship, for driving engagement and long-term brand loyalty. The greatest benefits are reaped from this practice by:

  • Focusing on building communities rather than merely mass posting.
  • Offering material that serves the audience in addition to sales offers.
  • Responding in real time to customer posts – constantly.

Social media used in the context of digital transformation turns customers from a source of analytics information – their clicks charted on graphs – to the leading stakeholder at the table. No decision is made without using analytics to look beyond the raw metrics, to delve into the intensity and true underlying intent of their voices.

Technology is Democratized

Of all the benefits offered by digital transformation, the one that will increase productivity across the widest swath of staffers is the democratization of technology. This bulky phrase refers to enabling the use of advanced technology by people at all levels of tech expertise, not just IT pros.

The concepts that define the democratization of technology are so similar to those of digital transformation that the two ideas are almost synonyms. Inherent in both ideas is improved systems throughout every level of business, across all staff and all departments.

Specifically, here are examples of how digital transformation’s focus on the democratization of technology boosts staff productivity:

Simplified User Interface

Even among sophisticated enterprise applications, the trend over the last few years has been to make the user interface as intuitive as possible. In no sector is this effort more pronounced – and more competitive among vendors – than data analytics platforms. Traditionally, analytics platforms required data science expertise, and many still do. But as technology is democratized, data analytics platforms are offering dashboards simplified to that level that any office staffer can query the database and get quick answers.

No Code / Low Code

No code / low code software development platforms allow non-tech staff to create and update software applications. By opening the door to software development to non-developers, this trend supports a quantum leap in the quantity and creativity of apps and upgrades developed. It also frees up trained developers for more advanced work, which creates yet another productivity benefit. This trend is growing quickly: Gartner projects the low code / no code market will enjoy 23 percent revenue growth between 2020 and 2021.


Robotic process automation (RPA) platforms allow non-tech staff to create automation to handle office workflow and other business tasks. This is a major break from the past, when coding automation required teams of data scientists and programmers. Better yet, with RPA automations handling the low-level busywork, human staff can focus their judgment and expertise on higher value business tasks.


Natural language processing (NLP) allows staff and customers at any level of tech training to simply speak with computer systems, instead of needing to know sophisticated coding. This is a revolutionary step in the relationship between human and computer. A few words in conversational language from, say, a business analyst can query a database, request a change in software program, or launch a new algorithm.

The net effect of all this democratization is that the power of technology is decentralized. It’s the real excitement of digital transformation: By allowing access to high tech to all employees, a company’s potential for innovation opens up exponentially. More of the staff can experiment and innovate and contribute. Bottom line benefit: as the democratization of tech drives digital transformation, your entire company is empowered to lead positive change.

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