[tweetmeme source=”atripathy” only_single=false]Think about the large successful organizations which are known for harnessing information for competitive advantage; P&G, Goldman Sachs, Capital One, Harrah’s, Progressive Insurance and you will find one thing in common. Their C level executives drive data driven decision making top down. And the more organizations I see, the more I get convinced that it is one of the most important factors for a company which wants to ‘compete on analytics’.
Here is my hypothesis of why it is so:
There is a fundamental Catch 22 situation in most large companies. Organizations do not have consistently good quality data (mainly due to process issues during intake) and unless the data is used to making real business decisions, it is hard to improve its quality.
This Catch 22 can only be resolved by very senior executive (read C level) who commits himself to making decisions and measuring performance based on analysis done with imperfect data (but good enough for many types of decisions/relative measurements). Once middle management understands how the data is being used, it spurs process changes to fix the quality issue which in turns increases the accuracy and reliability of analysis. The virtuous cycle is key for large companies which ‘compete on analytics’
In contrast, the middle management never wants to be in a situation to justify their decisions knowingly made using imperfect data. It is easier to justify subjective gut feel than objective decisions made with data with known quality issues.
In summary – the culture of analytics is a top down phenomenon
What do you think? Do you agree with this observation?
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Amaresh: I agree with your observation that analytics is fundamentally a business process improvement dynamic:
This Catch 22 can only be resolved by very senior executive (read C level) who commits himself to making decisions and measuring performance based on analysis done with imperfect data (but good enough for many types of decisions/relative measurements). Once middle management understands how the data is being used, it spurs process changes to fix the quality issue which in turns increases the accuracy and reliability of analysis. The virtuous cycle is key for large companies which ‘compete on analytics’
However, business process improvement can be driven at any level of the organization that needs to make decisions: it doesn’t need to come from the top. In many engineering organizations, engineers will create elaborate data collection frameworks to continually improve their decision making speed and quality. And typically, the top guys are clueless.
So, limiting yourself to C-level top-down processes to drive analytic prowess misses many business opportunities. I think it is more productive to look at the business process and see what the information content is. If that information content is high, the practitioners that manipulate the data stand to benefit from analytics, and they typically already know it.
The case I want to suggest to prove this point is the proliferation of Hadoop. The practitioners are bringing in Hadoop to do some serious data warehousing and analytics, typically completely devoid of any understanding by their C-level leaders.
What do you think?
Theo
Theo,
Thank you for the insightful comment. I agree with you that the technology advancements (especially the open source ones) are almost always driven by engineers within the company. Also agree that that the business processes can be improved by enterprising individuals without explicit approval from the senior executives. Both of them lead to productivity improvements.
However the point I am arguing relates to culture of using the data analysis to drive decisions. Capital One deciding to focus on low credit score individuals as a key market or progressive making a significant investment in their mobile claims adjudication trucks because it is not only the best thing for the customer but is more profitable for the company. These kind of decisions are possible in a company where the top management fundamentally is aligned on using data to make decisions and it percolates down to all the other parts of the organization which is when the multiplier effect of analytics is felt.
I may be wrong but unfortunately I have not seen any substantial examples otherwise. Do you have any examples?
Amaresh, you can read the book: “Secrets of Analytical Leaders: Insights from Information Insiders – Wayne Eckerson. It is very close with the information “real life” and how think the top down and bottom up challenges inside organizations. It was very useful for me and very close with the projects that I deal with.