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?