Analytics center of excellence. Business intelligence competency center. Decision analytics. No matter what you call it, a "center of excellence" serves an important role: It promotes collaboration and defines best-practices for a specific focus area (in this article's context, analytics) to improve business results. But how does it really work?
Director of the Business Intelligence Competency Center The Cincinnati Insurance Companies
In pursuit of a real-world perspective, SAS Editor Kristine Seawell and I spoke with Brett Starr, Director of the Business Intelligence Competency Center for The Cincinnati Insurance Companies, an Ohio-based insurer that offers personal and commercial lines sold through independent agents. Starr's group is responsible for documenting and enforcing data standards, creating scorecards and dashboards, prototyping and evangelizing new advanced operational metrics, developing analytic use cases, and helping units understand and use analytics more effectively.
We asked him about developing an analytical culture; in particular, how to encourage business units to use the center for analytical help, rather than requiring them to.
What's the toughest hurdle to building an analytical culture?
I don't think it's that people don't understand analytics like you read in such books as Super Crunchers or The Numerati. They do understand. I feel the biggest hurdle is to get people to effectively collaborate, to get over the fear of relinquishing control, to reach the point where they all want to chip in and build that better mousetrap so we can evaluate performance and make better decisions.
In addition, I don't think the organization needs to have a mature data acquisition strategy to have a competency center. The center just needs to be at the point where it can provide a service. Whether you have a 100 TB warehouse or a series of small databases, a competency center can create common process and framework for intra- and extra-departmental analysis.
How do you get people at your company to come to you with analytical problems?
Make them happy. Show them that analytics is cool, and make sure they feel like they're calling the shots. If I go to them and say, "Hey, I want to standardize your approach to measuring loss ratio," or, "You need a common measure of account retention," they will tell me to go away. But if I show them a cool graph and it's got these neat little widgets where they can put real-time filters on, it helps them understand their data.
This understanding encourages them to seek our team out for discussions on analysis. And that might lead to the informal creation of a Data Stewardship Group, which just happens to agree on a common measure of account retention. It's organic. It is very difficult to effectively mandate change within corporate cultures, creating true wins for everyone involved is what gets the company on board.
It sounds kind of sneaky, but really effective.
Our philosophy: Cooler is better. If I show you something cooler, you're going to use it and come ask for more.
Some of what you do has been traditionally housed in IT. How is that relationship?
There's that old adage -- you go to an IT person and they say, "The business doesn't know what they want," and you go to a business person and they say, "IT doesn't know anything." So the business goes off and tries to do its own work in Excel and massive Access databases -- and IT creates reports and different databases that no one uses.
Our team does our best to bridge the gap and provide a service to IT by taking the ideas from the business unit and doing a lot of quality assurance and prototype work. We'll assess new data sources, for instance, and make sure they conform to the dimensions of the data warehouse. It takes a lot of tedium off the business unit's plate.
You've talked about how you bridge the gap from the IT side. How do you bridge it from the business side?
One of the frustrations from the business side is that IT might show them some data that would immediately spark a question from business. But from IT's perspective, they don't want to redo the analysis, they want to put the report into production. We streamline the gap through effective communication and effective prototyping.
What would you say has been the No. 1 success for the competency center thus far?
Helping to steer the company toward common metrics. If you have a siloed or fragmented organization, internal metrics are often not consistent. As the varying metrics bubble up to the highest levels, it becomes harder to figure out where to put your resources. You can inadvertently make strategic decisions based on biased information. Overall, through a broader acceptance of common measures, we have become a more data-driven organization.
At a midsize company where I implemented their first analytics platform, the formation of the Business Intelligence Competency Center (BICC) was critical to its eventual acceptance and success.
As mentioned in the article, there was the goal of getting the folks from the line of business to set the project goals, deciding what was in/out, and basically shape the analytics platform course. This was not only good business (in my view, IT should never be the lead or project owner in these types of endeavors) but it allowed IT to stay out of the hard sell side and work on what they know, which is applying techonology to the benefit of the business. The business folks on the BICC did the selling back within their teams, and since they believed in it, were able to get "their own" to buy in much more easily than a scary IT type could.....and I say that with utmost admiration for IT types, since I resemble one!
@annabrown: Absolutely agree. Excellence isn't about quantity, it is about quality. I try to engrain this in my students: in nScale computing, n is any number -- ten or ten million. Ten crappy observations are no better or worse than ten million crappy observations.
I'm glad Brett's perspective resonated with you, Pierre DeBois and kicheko. I particularly like his notion that it doesn't matter how big your data is to have a Center of Excellence. When any amount of data and analytics is involved, a center that provides standards and a unified framework is needed. Everyone needs to speak the same language - to a degree at least.
I second Nancy's comments - I like how preventing IT and management silo roles has been mentioned here. Sometimes people really don't respect the expertise at the other end of the table, and try to go too far around the capability instead of working alongside. Bravo Brett.
Great interview and indeed a lot of problems are solved the wrong way because the organization did not use analytics...for example we'll have some course majors always attracting less than quorum. First response is usually to remove those subjects but then a little surveying reveals that it is simply the personalities teaching those subjects that cause the problem. A little reshuffling in the faculty solves the problem.
Once a problem like this has been solved, the next time there's a new problem the management will think of analytics first.
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