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Analytic practitioners speak – an interview with Lee Feinberg of DecisionViz


Please describe your current role and title

DecisionViz is a management consultant that helps companies build leadership in the processes, people, and culture around data visualization. We work with senior management to elevate visualization from the activity of reporting and making charts to being integrated with their daily decision-making. I founded the company in 2012.

What’s your background, how did you come to be working in analytics

Our story I think is like many in the analytics field. It’s been more of an evolution and the company has been almost 20 years in the making.
I was very involved with the Internet before most people heard about the Internet. So we were dealing with how to capture and measure all this new data. I’ve always been on the lookout for how to make that a lot less painful. Most of the work was in financial services and from the beginning we baked in plans on how to understand customer behavior and if online usage was leading to purchasing products and improving retention.
Jumping ahead 10 years, in 2009 Nokia hired me to figure out how they could make better use of all the data they had on consumers using their phones. That’s quite an exciting opportunity when you consider the breadth and depth of that information. The problem wasn’t technical at all. In fact, they had an excellent data infrastructure. The problem though, was that data was only accessible by the enterprise programmers and wasn’t used in day-to-day decision-making. As I like to say, “Data has zero value sitting in a database.”
The way I saw solving our problem was to democratize decision-making — a lot of people talk about democratizing data, but we focus on the decisions. If you could enable everyone in the company to make one more decision every week, or even every day, you are creating enormous value. We feel very strongly this is the new direction of analytics.

What are the primary kinds of analytics you build at the moment

DecisionViz is focused on enabling clients to make better and faster decisions through data visualization – it’s the future of how people will communicate about data. In the next few years you are going to see the number of spreadsheets decline really fast. Visualization is so powerful because our bodies are wired to process images much better than columns and rows of numbers.
We have a three-level certification program that takes clients through the process of building visualization and analytics into the fabric of the company. It’s not about the activity of making better-looking charts or interactive reports. The program is centered around our proprietary DRAW-ON™ methodology that ties directly into the company’s decision-making process.

In your experience what are some of the top challenges for analytic professionals in terms of maximizing the business impact of what they do?

Overcoming long-standing biases is a huge challenge. For example, during an exercise we conduct, someone in IT must ask their business partner how he or she plans to use data they have requested. We usually hear, “I could never say that. We just give them what they ask for.” Now, what happens next is the business partner – or their management – stands up and says, “I like that idea. It’s perfectly OK to ask that.” You would be amazed at how that simple statement changes the dynamics in the room. It completely erases all these unchallenged assumptions about how the organization works and the expected ways for the teams to interact. The core issue is people need courage to fight through these big changes, and we give them the support to be courageous.

What have you found that helps meet these challenges? How have you evolved your approach to analytics to maximize the business impact of what you do?

The conversations needed between business and IT have a history, and somewhat an expectation, of being difficult. We believe that everyone wants to do what’s best for the company, but they don’t know how to have a conversation and are set up to be more adversarial. How we bring everyone to a more neutral position is 1) having them take our certification together which provides an open forum for discussion and 2) we remove the pressure by giving them scripts for handling different scenarios and having them practice.

How, specifically, do you develop requirements for analytic projects?

We feel very strongly about not collecting too many requirements and work in a more agile mode. There are several benefits 1) we customized commercial software to prototype rapidly, which allows users to literally see if we understood their needs, 2) we can begin delivering a live solution and get feedback that might take us in a more important direction, 3) rather than detailed requirements, building a solution, then “raising the curtain,” we get people using the analysis in real-world situations as soon as possible. This agile approach is based on the idea that being able to work with part of the solution quickly and building out is more impactful that waiting for the whole solution – and having nothing to work with. It’s a simple notion but is a tremendous change for most organizations to even test out.

There’s a growing interest in rigorously modeling decisions as part of specifying the requirements for an analytic project. How do you see this approach adding value for analytics professionals?

The heart of DecisionViz is enabling our clients to make better and faster decisions. But, they mostly think analytics is about sifting through data looking for an answer. Our approach is to be decision-driven, supported by data. We designed the DRAW-ON methodology to give our clients a framework for articulating their decisions and deconstructing those decisions into data for analysis. These two steps set the context for choosing the best visuals to tell the story behind the decisions. DRAW-ON creates a lot of innovation within organizations because it gives teams a highly repeatable process for delivering the visualizations, so it’s much easier to get the work done and have more time for analysis and making decisions.

Anything else you would like to share?

It turns out that to make the visualization (business intelligence) technology effective, there’s quite a bit of organizational change that’s required. We shift attention away from the technology and focus on the business, what decisions they are trying to make, and actions they want to take. These conversations diffuse the struggles around “who owns the data” and direct energy toward how to make the data work for the company (and by the way, the company owns the data).

Last question – what advice would you give analytic professionals to help them maximize the value they create for their organization?

Analysts need to forget their job “description,” which is figuring out what the numbers mean. That’s the work of an analyst, but not where they create value. They create value by communicating what they learn and arming decision-makers to take action. The best analysis, if ignored, is worthless.


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