Analytics Process

23 11 2009

Over the last couple of months I have been writing about a handful of US Economic Indicators.  While I have reviewed these over the last few years of my life, I had not done so on a regular basis.  This inconsistent and let’s call it a casual curiosity lead to never really understanding the implications behind the numbers.  Sure I could talk about them, but I could not leverage them.  While not an expert by any means, I can see a lot more now than I did when I started this blog series.

This is similar to ad-hoc analysis without purpose.  We do something once and create a little hype.  When we don’t have any vehicle to take advantage of the newly found ideas, the idea dies as does the learning.

Think about the process of how you handle ad-hoc analytics within your organization:

  • Do you have the right minds constantly looking for new issues?
  • Or, do you put the right minds on solving issues when they arise?
  • Can you name your best analytical minds?  Are they assigned to thought leadership and problem solving?
  • Do you use your analytical minds to challenge the knowledge levels of others?
  • How do you foster new thinking?

 

Consistency breeds familiarity, and familiarity breeds knowledge





Design for Information

4 08 2009

All to often reports are designed to provide data, not information.  There are charts and tables with little intrepretation, or description.  While I am not great fan of PowerPoint, it can often make up for Enterprise BI limitations.  We can call out certain areas within the charts and graphs, as well as add the commentary to help us communicate our point.

A safe assumption is that the person reading the report will not have the same understanding of the material as the report designer, or analyst.  It is then our job to make sure that the report communicates the point clearly.  The last thing you want is to hear “what are you trying to show me?”

Below is a good example of presenting data, while not telling us much.  Here we see that he/she has a few fans that are frequent contributors, and that tweet volume picks up around the lunch hour.  There is not much variation for the days of the week, with a little drop off for the weekend.  August is also the most popular month.

twitter2008-1

What would be helpful to know is why this data is important to us.  What perhaps would be the most important is to know the subject material, so we could do things like tweet just before lunch as that seems to be the most popular time to inspire reaction.  Or that August tweets were up due to an embarassing grammatical error.

As we are designing reports, make sure that the information has a purpose.  Most specifically, know the audience and know the potential actions the information is going to inspire.





Analytics & Actionable Information

5 03 2009

I work on many projects where the outcome is  “just provide us actionable information.” While this is always the goal, I find most people use the term quite loosely, as if it were merely an additional option. In reality this is quite difficult to create. Many things need to come together to create action, and it is far more than just information or a report.

To create effective actionable information, we need to integrate people, information, and tools. We also need to have the right skills at different times. All too often, the expectation is for IT to write a single report that will answer all questions. Yet, what typically happens is the report creates more questions as IT cannot predict every need. All this has done is create more activity for IT and delayed action.

Let’s view this from a process point of view…how would it look:

First, we have a tremendous amount of data. And it would be easy to argue way too much data, hence the need to create layers of relevance. How often do we get lost looking for what we need, or recreate something because we don’t understand the business rules of the data we find? This wasted effort costs the business money and time.

We have the information, now we need a good analytical mind to review the data to create analytical models or what-if scenarios. What typically happens here is a finance or IT analyst runs a few numbers. This is probably OK for many instances, but the best option would be both a mind of the business as well as a statistical curiosity (though at this stage we need more of a statistician). IT and Finance often lack both of these to some degree – as their primarily skill is data or fiscal governance.

Now we have some level of analytical information, but still have work to do. In general, the statistical mind tries to cram in too much detail and wants to discuss the process of discovery, instead of the finding. To transform analytical information into action, we need the business to present the finding in executive terms – value created. The presentation is more than likely to include multiple reports, synthesized into a couple charts. The next step is to foster a discussion of the recommendations and potential options. The discussion will focus on gathering feedback and coalescing them into an agreed upon plan.

It is common here for people not to feel comfortable with the information and ask for additional information and analysis, but we need to fight the urge to delay and put the best foot forward. There will be times when the need for rework is great, but if the discussion includes the right people and the facts then there should be enough to make a decision and move forward. Otherwise, the risk is creating a culture of endless analysis.








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