Why the Next Big Data Is End-User Data

October 19, 2015 | Post by Michael Giordano | 0 Comments

What’s the next major area where enterprises will exploit Big Data? It’s around the troves of information they already have on how their end users consume IT — and how IT supports them. In fact, if you’re not analyzing these data stores and deriving value from them today, you’re missing a tremendous opportunity to gain a competitive advantage.

Why? Because exploiting end-user data can save you significant IT costs, make your employees more productive and give you a leg up on your competitors.

Analysis of end-user data can also give you surprising new insights. As one example, the way you currently categorize end users probably doesn’t accurately reflect the way they use technology.  Leveraging data science to turn data into information can show you why, and help you understand your customers so you can tailor your engagement processes to their needs. It also provides your customers with insights into their own users and how they consume technology. 

But to take full advantage of end-user data, you need the right tools, processes and best practices. Here’s what you need to know now.

Getting to Know Users, Getting to Know All About Users
CIOs are fully — even painfully — aware of how rapidly IT in the enterprise is changing. Mobile workers in the United States will number 105 million by 2020, IDC predicts. Employee-owned smartphones and tablets will outnumber company-issued devices two to one by 2018, Gartner says. Enterprise workloads are finally beginning to move to the cloud in a big way, InformationWeek reports.

These forces have a profound effect on how technology is being used in your organization. The good news is that most companies are already amassing large quantities of data on how end users consume IT and IT support. The bad news is that most of this data remains trapped in data siloes and is not normalized in order to turn information into insights that uncover patterns you may not expect.

The solution is to focus on data and processes within your systems, and standardize data elements within and across all support systems to ensure data is captured correctly.  It’s a journey, and the process is incremental. But once you’ve achieved this, you can begin to turn static data into living data that offers valuable insights. We use a centralized Master Data Repository to get a unified view of our consumers, devices, issues and so on. We then leverage a graph database and run algorithms for predictive analysis, enabling us to discover insights and patterns on our customers and adjust our IT and support services to serve them in a way that best meets their needs and ours.

The results can be surprising. One example is that an employee’s job title may be less relevant that his or her technical sophistication when it comes to consuming IT. For example, Fred and Betty may both be salespeople, and they may both download a large number of apps onto their smartphones. But the cost to support Fred might be much higher than Betty, because Betty is tech-savvy and more adept at self-service. We can adjust our engagement channels with Betty to filter out the noise and provide her the information she needs in a consumable way. Fred may need information presented in a different way to best serve him. 

Here at CompuCom, we analyze end-user data in real time for clients in a broad range of industries and the insights are even more intriguing. For example, in some circumstances, a particular bank’s IT usage patterns might have more in common with a logistics company than other banks or similar financial verticals. That has enormous implications for how we support the bank and tailor our services to this customer. This data has potential for the bank itself, who may ask, “Why am I different?” There could be legitimate reasons for this. Maybe the bank is consuming technology appropriately and is unique. Perhaps they are innovators and are gaining competitive value. It may also may mean that they are not consuming technology appropriately, which puts them at a disadvantage compared to peer companies. 

Data Today, Differentiator Tomorrow
Big Data is a catch-all phrase, but it’s important to understand that “Big Data” is not just about types of technology; it’s more about looking at data and transforming it into useful, actionable information. Different tools can be used to achieve this goal. The important thing to be proactive with data, not reactive. It’s about turning data into insights. 

In some cases, large enterprises competing against start-ups have an advantage here, because they probably have years of historical data. This data, if applied right, can be a competitive differentiator. But even young companies or start-ups that are only beginning to capture and analyze data can benefit quickly and become disruptive to established players.

In our case, analyzing end-user data and multiple associations can deliver tremendous payoffs. Many of those benefits are around finding patterns that lend themselves to process adjustment, automation and cost savings. Other insights lead to serving our customers with more accurate, contextually relevant information that they require on demand. For example, you can identify which mobile devices are cheapest or most costly to maintain, and use that knowledge to guide your mobile-device policy. Or, you can avoid license fees for software that certain users don’t need, or provide useful applications to sets of users that didn’t previously have access to them.

You can also invest in new support services — such as walk-up IT service centers, which are rapidly gaining popularity — that solve problems for certain end users faster and cheaper. Having historical knowledge of the user, and understanding the type of user they are, enables our staff to engage with the customer better. 

Ultimately, you can provide your organization’s employees with the technology and IT support that makes them as productive as they can be. And you can exploit end-user data over time to drive continuous improvements.

The final step is predictive analysis, where you leverage current patterns of IT usage to proactively anticipate the needs of your customers. This could help position your customers to use technology to collaborate, innovate and work in entirely new ways.

I welcome your comments on leveraging Big Data to enhance business value.

The content and opinions posted on this blog and any corresponding comments are the personal opinions of the original authors, not those of CompuCom.

  • Michael Giordano's picture

    Michael Giordano

    Mike Giordano is Vice President of Digital Strategy at CompuCom. He is a strategic leader that provides vision and innovation that ignites revenues, generates operational efficiency and effective product and IT solutions.

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