In Search of Data Détente

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Earlier this month, I was at Gartner’s Business Intelligence & Analytics Summit, which gave me a chance to talk with customers about how they’re empowering their end-users to access, integrate, prepare and provision data. Most interesting is the growing numbers of “citizen analysts,” “citizen data scientists” and even “citizen DBAs,” who are extracting different insights from corporate data.

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The ability to give every Tom, Dick and Mary the tools to perform self-service data analytics is great because it brings together as much data as possible to solve important business problems. That’s one of the reasons that End User Data Preparation (EUDP) continues to be such a relevant topic as it enables end-users to model, prepare and combine data before analyzing it. In its recently published second annual report on end-user data prep, Dressner Advisory Services examines this burgeoning area, which the company believes demonstrates the increasing value and movement toward end-user empowerment and self-service business intelligence.

I wholeheartedly agree that having a combined view of everyone’s vision around what data looks like, where it comes from and should live, as well as how it should be used, is extremely powerful. I wrote about this in a previous blog on collective resourcing, which is the best way to unlock universal truths while finding and fixing ubiquitous business problems.

The only downside to all this idea and knowledge sharing is the resulting explosion in data complexity and conflicts. One of the issues that continues to plague customers is how to eliminate the endless finger pointing when report discrepancies arise, which seem to happen every day. When all the “citizen analysts” come together, invariably the discussion turns into a debate over who has the best data, as each report can vary wildly depending on when, how and which data was used to inform the analysis.

Too often, what’s missing is the underlying technology foundation that makes it easy to reconcile discrepancies by comparing and synchronizing data sets and sources from disparate platforms. The other thing that’s often overlooked is the importance of gaining consensus on “why” the analysis needs to be performed in the first place. Instead of just focusing on answers to “what data needs to be analyzed” and “how the analysis needs to be performed,” the collective group needs to align on “why” questions to produce effective, data-driven business outcomes.

At the Gartner event, I spoke with a pharmaceutical company focused on boosting operational efficiencies in its manufacturing operations. To identify any problems that could impact production yields, the company performs regular data samplings at different intervals. Unfortunately, they regularly run into data conflicts, which pits different teams of analysts against each other as they each vouch for the integrity of their individual analysis and results.

With each new product run, a different group of business owners performs their own analysis, which then is compared to reports prepared by the IT manager and his staff. While the IT team adheres to a standard approach to analytics and end-user data preparation, the same can’t be said for other groups. When discrepancies or conflicts occur, however, the IT team can quickly moderate and mediate questions about data efficiency and accuracy. Still, it’s common for tensions to rise as the respective teams defend their position and approach.

Now, I totally recognize that everyone does things slightly differently and most of us believe we have the right answer. What technology can and should do, however, is offer a fast and easy way to settle arguments because it enables reviewing and dissecting the analysis until you discover and then resolve the discrepancy.

For instance, maybe the business team only used three data sources while IT leveraged five to produce its report. Or perhaps one group refreshed its data every hour while the other refreshed results every 30 minutes. By peeling back the data analytics onion, layer by layer, you can eliminate the guesswork—and finger pointing—while gaining much more visibility into the overall decision-making process.

Armed with data-agnostic, cross-platform technology tools, warring factions can reach data détente to collaborate more and focus on what matters most: better, faster, more lucrative business outcomes. Using a foundation of technology that can be tracked, monitored and managed in a traceable and accountable fashion is key to empowering users. It’s also the best path for taking utmost advantage of collective resourcing to unearth universal truths that improve business outcomes.

What’s your secret to conflict resolution? Connect with me on Twitter at @joschloss to share your thoughts.

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