<p>If some one asked you to explain the reporting/analytics/scorecarding solutions you have developed so far for your organization, how would you respond? Here are some of the common answers</p><p> </p><p>1. This gives me the list of all <sales orders/POs/Invoices> for a set of selection parameters - and we can see our biggest and smallest values, we can see a nice graph of the trends over time, and it will high light all exceptions outside the thresholds we have defined</p><p>2. This compares how we performed against <plan/forecast/budget> over a period of time</p><p>3. This shows us how we are doing on our SLAs - like how long each step of a process took (took 5 days for a Purchase request to become a Purchase order). </p><p> </p><p>Next question. Why do we need these solutions ? Each question has specific answers, but the common theme is - We want to know this so that we can do "course corrections" and try to do better in future. </p><p> </p><p>And now a short simple question to round up the discussion " oh yeah? REALLY? and how exactly would you do that?" </p><p> </p><p>How much can we rely on past performance to predict future in business? By no means am I the first to ask this question. And there are a lot of good answers on why/how historical information gives clues on future performance. </p><p> </p><p>Can you compare data across time horizon in "apples to apples" fashion ? sure - as long as we have common characteristics to compare them. This is not the difficult part - this can be achieved some how in most cases. The crux of the matter is - can you explain the variance in data that these solutions throw at us? </p><p> </p><p>Let us take an example - last year, it took 5 days for a PR to become a PO on an average. And year before that it took only 4 days on an average. Why? Does the report tell you the reason why this happened? If it does not give this answer - is there any justification in calling this business intelligence? where is the "intelligence" here?</p><p> </p><p>This is not an easy question to answer - there are several reasons possible as to why the PR took a day longer to become a PO this year. Let me throw a few out there - totally arbiatary figments of my imagination.</p><p>1. Approvers don't understand the cost of waiting - indicating maybe a change management issue or a training issue</p><p>2. Lack of streamlined approval process - indicating an inefficient process design</p><p>3. A new manager took over procurement department this year, and he was not as good in following up as his predecessor was. </p><p> </p><p>Let us say that after some analysis - we identified that the reason for this as "1. Approvers don't understand the cost of waiting". So we fix that - and get every one trained, and hang banners all over the place urging them to do better and all that :)</p><p> </p><p>A year later - we find that the average time is now 6 days. We are back into analysis mode - and this time we found that it is because a large number of PR got raised in holiday season, when approvers were on vacation. </p><p> </p><p>You get the idea...this approach has a lot of pit falls.</p><p>1. We re-invent the wheel every time we see a variance. We are not managing by exceptions - the report does not break down the data by potential causes we identified in last iteration.</p><p>2. There is no systematic way of storing the result of past analysis. </p><p>3. We don't even know if it is worth analyzing this variance . Is it such a big deal that POs took a day longer to get created on an average? is the cost of analysis more than the cost of delaying POs by a day? Why was the decision made that 4 days was the acceptable limit for time a PR needs before some one creates a PO out of it? why not change it to 5? - and then there is no longer a need to waste time on complex analysis !!</p><p> </p><p>In most organizations that I am familiar with - there are pretty good reporting solutions that show them WHAT happened. They have (or can have) the ability to see the data and slice and dice it in any way they care to. But the moment, the data shows a variance - they don't have a good process of handling it. As a result, they do the same analysis repeatedly. It is not easy to answer "WHY" questions. </p><p> </p><p>Is it even possible to have a solution that answers the "WHY" questions? My answer is "yes - to a certain extend". Does this mean our existing reporting solutions are useless? Not at all - they are the starting point. With good design, we are already able to show clearly as to "WHAT" happened. </p><p> </p><p>So, what can we do to extend our existing solutions to answer the "WHY" questions? </p><p> </p><p>First of all - we need to realize that the idea is NOT to automate all such problem solving completely. It is impossible to identify all the causes for a given effect. All we can do is to see if we could identify a good number of possible causes upfront so that a systematic solution can attempt to correlate the effect to these causes. If no correlation exists - then it makes sense to identify this as a valid exception, and find a solution manually. But it does not stop there - the moment we identify the cause, we should be able to identify this as one of the probable causes for next time - so that we don't waste resources redundantly on the same issues repeatedly. </p><p> </p><p>Let us talk some more next time and see if we can come up with an approach to keep "Intelligence" from taking the divorce route :)</p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
