Are You Ready to Reengineer Your Decision Making?

Increasingly the organizations have implemented business intelligence and business analytics solutions, but they have been used for decision making effectively? Thomas Davenport gave a nice interview to MIT Sloan Management Review’s editor-in-chief, Michael S. Hopkins, where he told about that and other issues and what it would take to change that. Davenport said that the link between analytics and decision making needs to be relearned: “What I’ve seen a lot with the proliferation of data and data warehouses and business intelligence systems is that the tie to actual decision making has been lost. We generate a lot of data, we supply a lot of tools and we say to people, ‘Okay, go at it, have fun, play, make better decisions.’ But we never actually ensure that they do.” Below I highlight some parts of the interview:

What’s changing in the world of analytics that CEOs ought to know? Davenport answered: "There are a couple of things. One is that we used to have this distinction in organizations between transactional information systems and decision-oriented systems, and I’m starting to see that distinction break down. You have big enterprise systems vendors like SAP and Oracle saying, “We’re going to put all of our technology into memory so you can get an answer immediately out of a transaction system.” The integration of basic transaction systems, ERP systems, CRM systems, point of sale systems, web ecommerce environments – all of which generate vast amounts of data – means being able to get good answers about what’s going on in your business without having to go to nearly as much trouble as you did in the past. This is less a technology issue, I think, than a management issue. We’ll see an increased focus on decisions and how they’re made. We’ve reengineered a lot of things – our processes, our organizational structures – and I’ve been trying to persuade people that this is the time when we could really start to reengineer our decision making."

Asked to describe what he means by reengineering decision making, he said that he did a study of 57 companies that had improved their decision making in one way or another, and he asked them what they used to make those decisions better. The number one intervention tool they cited was analytics – about 85% said analytics. But right after that was change in culture or leadership, followed by better data, followed by change in business processes, then the education levels of the people doing the decision making.

The use of “decision analysts” is more the exception than the rule, Davenport said. It’s very politically difficult for people to say, “I need help making decisions.” The technical challenges of having analytics managers help executives in decision making pales compared to the cultural and political challenges.

The analytics of collaboration are another trouble spot, according him. The tools for internal collaboration are getting more sophisticated all the time, whether it’s the older knowledge management stuff or the newer Enterprise 2.0 stuff. What companies don’t do much of yet is to view collaboration as a mission-critical activity that needs to be measured and optimized. In the same way that we know how long our customers spend on our website and how many unique visitors we get, we could measure who’s collaborating with whom about what. He calls this the science of collaboration.

About the pressure that the organizations are going to be under to aggressively incorporate analytics and other predictive approaches into decision-making, Davenport thinks the speed depends on the industry. In some industries where there’s a lot of data, like financial services, retail or online, it’s already a business necessity, and getting competitive advantage will require some creative new applications. others, you can still get early competitive advantage.

Davenport said when asked if everybody knows what analytics is: The problem is that the definition is changing. “Business intelligence” was the umbrella term for looking at the past and looking at explanatory and predictive models. Now that umbrella term is “business analytics.” The whole category is just using data and analysis to understand and manage your business more effectively.

He has an acronym: DELTA model, to explain the skills and capabilities that people need in order to take advantage of the opportunities: D is for data. E is for enterprise orientation, which is viewing this not as a series of little silos but something we try to be good at as an enterprise, by getting people talking, sharing data, sharing solutions. L is for leadership. T is for targets, where you figure out where you want to apply it in your business. And then A is for analysts. You really need a lot of smart people.

The MIT Sloan Management Review mentions some interesting further reading related with the Davenport's interview:
- What People Want (and How to Predict It) - Thomas H. Davenport and Jeanne G. Harris
- Value-Creation, Experiments, And Why IT Does Matter - Michael Schrage, interviewed by Michael S. Hopkins
- The Collective Intelligence Genome - Thomas W. Malone, Robert Laubacher and Chrysanthos Dellarocas