What Gets in the Way of Good Analytics?

Today at Bank Systems and Technology, there’s an article on the increasing importance of analytics to the banking industry. The story is fairly typical in the genre – “we used to manage by gut, but better information about our customers can help us in so many ways!”

What caught my attention was that quite a few of the contributed quotes came from places on the org chart that just don't exist at most organizations – the “Director of Statistics and Modeling” and the “Department of Insight and Innovation” to name two. These references were threaded alongside a frequent comparison of “mature” analytics areas, such as credit card predictive modeling and “growing” areas, such as customer attrition modeling. This might suggest that organizations who create a dedicated function related to analytics and related disciplines are more successful at spreading the competency internally than those organizations that leave it to chance. This is certainly the position put forth by Thomas Davenport in Competing on Analytics, and is certainly intuitive in some respects.

It’s easy to envision a success story for such a group – evangelizing the power of analytics, introducing new skills to functions without a historical strength in analysis, etc. But what are the likely barriers and points of failure? How can an organization considering such an investment get ahead of the curve and mitigate the risk?

I’d speculate there are a handful of key reasons for struggle or failure:

1. Lack of a starting point / quick win “pilot” - Perhaps it is difficult for a Center of Excellence-type structure to get off the ground without one demonstrated benefit within the first year or so
2. Insufficient data trail - For businesses or domains without a solid trail of transactional information, it might be tougher to get started (there goes my idea for a chain of cash-only restaurants with no POS system)
3. Lack of data architecture / infrastructure investment - If a new analytics team’s first report includes a request for $5 million just to organize the data, rough roads may be ahead
4. Active resistance to the scientific approach - If a CEO is commonly heard to say “you guys think too much,” is that an organization likely to be hospitable to analytics?

What do you think is the biggest barrier? One I didn’t identify? What are the keys to success in building an organization's overall competency in analytics?

Who’s going to the Big Dance?

The NCAA Men’s Basketball Tournament tips off this weekend with the announcement of the 65-team field. The tournament, affectionately referred to as “The Big Dance,” is a yearly highlight for college basketball fans and culminates in the crowning of the national champion after 64 games over three weeks of March Madness.

Two of these fans, who also happen to be business professors, have developed an analytical model using SAS® software to predict “at-large” teams – those schools who do not receive an automatic bid to the tournament.

Jay Coleman, an operations management professor at the University of North Florida in Jacksonville, and Allen Lynch, an economics professor at Mercer University in Macon, Georgia, built a model that has achieved an impressive 94% accuracy rate in predicting tournament teams.

The actual selections are made by the NCAA Tournament Selection Committee, and will be announced this weekend. Coleman and Lynch used historical results from this Committee, along with 42 pieces of information to build their model. Interestingly, they found that only 6 items are significant in determining whether a team gets an at-large bid:

1) RPI (Ratings Percentage Index) Rank
2) Conference RPI Rank
3) Number of wins against teams ranked from 1-25 in RPI
4) Difference in number of wins and losses in the conference
5) Difference in number of wins and losses against teams ranked 26-50 in RPI
6) Difference in number of wins and losses against teams ranked 51-100 in RPI

Here a link to their website; and a 2-minute video about their model.

As they mention in the video, predictive models have many applications in the business world. These models can be difficult to build (the DanceCard model has been refined over 14 years) and validate (we don’t have the equivalent of a 10-member committee announcing their results live on CBS). But simplifications may exist (of 42 drivers in the DanceCard model, only 6 are significant) so don’t be afraid of the complexity.

Advances in analytical software, coupled with the increased availability of data, make predictive models a powerful tool to use in optimizing your business. And we have the benefit of a real-life market to test our ability to predict the future.

Today’s burning hoops question: Will Ohio State, who lost to Florida in last year’s championship game, even make it into this year’s field with a 19-12 record and an RPI of 48? DanceCard will have their final prediction later this week.

What’s your burning business question? Have you tried to build a predictive model to answer this question? How well did you do?