Predictive Analytics, Business Intelligence, and Strategy Management

9 12 2009

I was having a discussion with one of my clients this week and I thought he did a nice job summing up Predicative Analytics.

So in the World According to Reed (WOTR) – “queries answer questions, analytics creates questions.” My response was “and Strategy Management helps us to focus on which questions to answer.”

Reed Blalock is exactly right, traditional BI is about answering the questions we know. Analytics is really what we create with data mining – we look for nuances, things that might give us new insight into old problems. We use human intellect to explore and test. And yes, there is a little overlap. But what is really happening is that we have a different level of human interaction with the data.

BI is about history, analytics attempts to get us to think, to change, and idealistically to act.

The danger with both of these is that they can be resource intensive. Neither tool, or mindset should be left to their own devices. What is needed is a filter to identify the priority and purpose. This is where strategy management and scorecarding comes into play. We have built out massive informational assets without understanding where, when, and how to use it. We have pushed out enormous reporting structures and said “it’s all there, you can find anything you need” yet we scratch our heads when we see adoptions levels are low.

What we have typically not done all that well is build out that informational asset by how it helps us be more productive along product lines, divisions, sales region, etc. We have treated all dimensionality the same. Why, because it was easy. The BI tools are tremendous in how quickly you can add any and all dimensions.

“But because you can, doesn’t mean you should”

As we built out these data assets, we did not align them to performance themes.  We have gotten better with some key themes, like supply chain management, and human resource management, but what about customer performance?  We might look at sales performance, but that is a completely different lens than customer performance.

How do we determine which assets to start with…what assets do we need to be successful 3-5 years from now, or what are our biggest gaps to close today.  Think about customer value, or employee satisfaction (and that doesn’t mean more HR assets).  Think about your gaps in Strategy.

How often do we discuss…

  • Are our customers buying more or less frequently?
  • What are our best, and better customers doing?
  • What are the costs associated with serving our least profitable customers?
  • Where are our biggest holes in understanding?




Data Warehouse Design

24 06 2009

One of the main problems with Data Warehouses is that they are designed to answer any question.  The problem is that they usually fail to answer the one someone is asking.  DWs are usually good for referencial information – meaning I can answer questions like “how many customers do we have that have spent over $100,000” or “which customers bought the blue widget.”

There are a number of points of failure that hamper DW projects:

  • They are usually complex and very costly
  • The business changes (regions, product lines, sales heirarchies, etc) in the middle of the process
  • The end use is not well defined
  • Lack of analytical skill and knowledge of data structure in the business users to get the right data
  • The end result is too complex for the users to understand where to go to get the right information
  • No one tells the organization “thou shalt” use the data warehouse – so people get data from all different sources making a common version of the truth difficult to get to
  • There are often no rules of engagement for how to use the environment, or data in general

If organizations only use 6-10% of the data they collect, how do you design the DW for greater adoption?

For starters, understand the common business questions and the potential levers that can be pulled. For example, one of the areas that always surprises me is the lack of information around the success of marketing campaigns. Marketing campaigns and price are really the only levers we can pull in the short term to increase revenues. What we often fall back to is the sales whip – where we put more pressure on the sales team to perform. This is a strategy of hope (which is not a recognized as a successful strategy practice). We apply the pressure without providing much in the terms of support.

Instead let’s say we are ending the 3rd quarter and our numbers are a little behind and the pipeline is not as strong as we would like.  We know we have some time, but the programs have to be very tactical to find low hanging fruit. Instead of reviewing the potential marketing programs or trying something new, we cross our fingers and yell at the sales team. We could cull the DW to find large groups of customers who had not bought specific groups of products and offer incentives for them to buy.  We could identify the groups/verticals of customers with the shortest sales cycle and build a promotion and program for them as well.

Yet why do we not do this…we typically lack the information in a format we can use in a timely manner.

So if we design the data warehouse (or perhaps data marts) around specific business levers we stand a better chance of answering the one question we need. We just might trigger some very interesting questions about our business.