Data-driven culture helps analytics team generate business value

Three analytics managers offer tips on engaging with business users and building strong analytical environments, as told to author Wayne Eckerson.

In this excerpt from Secrets of Analytical Leaders: Insights from Information Insiders, by business intelligence consultant and TechTarget research director Wayne Eckerson, readers will find commentary by three analytics managers, who explain to Eckerson how building a skilled analytics team and working effectively with business units on analytics applications can create tangible business value for a company.

Ken Rudin, head of analytics for Facebook, opens the second of three segments from Chapter 5 in Eckerson's book by detailing the importance of remaining focused on business goals, asking the right analytics questions and placing data analysts in the business teams they support. Timothy Leonard, an independent consultant and data scientist who previously was chief technology officer at trucking company U.S. Xpress, offers communication strategies for working with business users, including when to talk and when to listen. And Eric Colson, chief analytics officer at online clothing retailer Stitch Fix, discusses the merits of hiring top-performing analytics professionals and instituting a data-driven culture and an effective organizational structure for an analytics program.

Commentary from Analytical Leaders

Ken Rudin headshotKen Rudin

Ken Rudin: You succeed with analytics when you stay focused on the end goal. It isn't enough to find patterns in the data and highlight trends and outliers in fancy charts, or deliver insights that can potentially drive business value. Your analysts must actually create business value. If nothing changes because of their insights, then they haven't added any value to the business. They have to strive to get the business to implement their insights so that they ensure a positive business impact. That means they have to talk with business people and brainstorm ways to turn their insights into business value. The insights can impact the business in many ways. They can change product designs, pricing, or processes, among other things.

Just like a salesperson takes ownership of an account and doesn't get paid commission unless he makes a sale, it doesn't make sense to reward analysts for delivering insights that aren't implemented; you should reward them for delivering value. And you measure value just like everything else. The key is to focus on impacts, not insights.

Table of contents

Creating effective analytical processes: Tips from analytics leaders

Data-driven culture helps analytics team generate business value

Analytics program requires adaptability, collaboration -- and results

Ask the right questions. I also think it's more important to ask the right questions than to get the right answers. It's easy to get answers. We know how to do that, and we have a ton of technology to help in this area. What's hard is asking the right questions which are going to drive business impact. A lot of this is about surfacing and testing assumptions about what people think drives behavior or business metrics. For example, game design is very creative but is based on a lot of assumptions, like "We can make the game more enjoyable and get people to play longer if we add this feature or change how hard it is to get to the next level in the game." If you pose these assumptions as questions and test them, then you can prove them right or wrong. That's key to gaining understanding.

Embed analysts. Finally, it's important to embed analysts inside the business teams they support. They need to sit side-by-side with business people, participate in all their meetings, and contribute their analytical knowledge and perspective. If they're not embedded, they can't possibly master the nuances of the business they're trying to support. It will take them much longer to perform an analysis and they might miss important details. Also, if they're not embedded, it's harder for them to persuade business people to test their assumptions and act on the output to improve the business.

Timothy Leonard headshotTimothy Leonard

Timothy Leonard: To succeed with analytics, you need to put as much emphasis on the "business" as on "intelligence." I rose up through the technical ranks and learned the hard way that you can't be perceived as an IT person. You need to be perceived as a business person who uses technology to solve business problems.

So my keys to success are: 1) talk the language of business, 2) let the business do the talking, and 3) get quick wins and build on your success. Ultimately, it's all about sales. It took me some time to check my technical content and language at the door to the executive suite. I discovered that the more I discussed architectures, schemas, and tools, the less business people seemed interested in what I had to say. But if I talked about business concerns, say increasing wafer counts per square foot of factory floor at a semiconductor company, then executives paid attention.

When I join a new company, I spend a lot of time listening to people and learning how the business works. If I open my mouth too soon and expose my business ignorance, I lose credibility. So, I try to master the business quickly. As I gain knowledge and confidence, I ask fewer questions and begin engaging in conversations. At some point, I know almost as much about the business as the business people. You know you've made it when a business person says, "You know a lot about the business for an IT guy!"

Copyright info

This excerpt is from the book Secrets of Analytical Leaders: Insights from Information Insiders by Wayne Eckerson, published by Technics Publications, LLC, Westfield, N.J. ISBN 978-1-9355043-4-4. Copyright 2012, Wayne Eckerson. For more info, please visit the Technics Publications website.

I also discovered that in key situations -- like when you need executive support for a project -- it's best to shut up and let the business people do the talking. While executives appreciate a business-savvy IT person, they would rather hear a business person explain the need for a business intelligence (BI) solution. So, when it's appropriate, I ask business people to deliver the presentations about data proposals, and I sit in the back and talk only if called upon.

To deliver successful projects, it's also critical to follow a clear methodology that involves plenty of dialogue between business and the BI team. Executives need to define objectives, communicate them to everyone involved, define measures of success, and hold someone accountable for the outcome. The development team needs to hire the right people, with appropriate technical and business skills, to develop the infrastructure and applications. The business needs to assign the right business people to work with the development team to define requirements and provide continual feedback to ensure applications meet their objectives and needs.

Eric Colson headshotEric Colson

Eric Colson: The key to success starts with getting the right people. I've learned that it's far more important to hire people with the right personal qualities than the right technical skills. You want people who are curious, creative, tenacious, and passionate about what they do. People with those qualities quickly learn the technical skills they need, whether it's a new programming language, like Python, or a new analytical tool. They just do it. To them, technology is a means to an end.

It's important to pay for top talent. In a creative field like analytics, the best people perform ten times better than average people. It's much more effective to hire one "rock star" and pay him or her a big salary than hire several average performers. And, top performers want to work with other top performers, and this creates a virtuous cycle.

Culture. The right culture also matters. A data-driven culture that values empiricism keeps politics and opinions in check. People frame their ideas as hypotheses and submit them to testing and experimentation. Although decisions are evaluated scientifically, there is still room for judgment and intuition. This kind of culture values data and analytics immensely, creating a supportive environment in which data developers and analysts thrive. The right culture also minimizes rules and processes to prevent stifling innovation and learning. It continually prunes processes that don't add value and is willing to incur some risk to ensure a fluid, fast-moving environment.

Organization. To get the most value from your people and culture, you need the right organizational structure. I prefer a federated organization in which a central team supports the activities of embedded data developers and analysts while giving them ample opportunities to collaborate and share knowledge. Here, data developers sit side-by-side with the business people they support. As a result, they become immersed in the business and more effective at what they do. In a federated organization, you align first with the business, and then optimize technical functions.

Roles. In a dynamic business environment, data developers with a diversity of skills trump a collection of specialists. Specialization is a fine thing when you have well-defined requirements. But in a fast-moving company, developers need to discover requirements as they go. By developing an entire solution from requirements to testing, they can respond immediately, iterate rapidly, and deliver optimal solutions more quickly than a team of specialists that require endless meetings to coordinate their activities. The ideal data developer focuses on mastering a business domain rather than a technical specialty.

With the right people, culture, organization, and roles, you can create a high-performance analytical team.

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