On the Road to Montague: An African Journey towards Performance Management

While walking through the streets of Hermanas, a small town outside of Cape Town, South Africa, I wondered to myself, "How much has the PM movement infiltrated a country so far removed from the average Americans frame of reference?" (to settle those of you who have just read that statement and felt your hackles go up, let me follow this up by stating that this is usually the case, unless those Americans are from the financial, medical or telcom business sectors of corporate America – admit it…you still think there are lions, elephants and giraffes roaming the streets of South Africa, which, for those of you who do not know, is a country — yes, a country…not just a reference to the SOUTHern part of the continent itself).
 
OK, now that we have covered our geography lesson for the day, let’s get back to he question at hand…What do you think…? Do you think BI and performance management have made as much movement down here as they have in the US, especially over the course of the last few years?
 
At first, I will admit, I didn’t think so…There are still many regions in the US that wouldn’t have the 1st clue what PM and BI stand for, let alone how to employ the strategic advantage that each presents. But I was wrong…
 
Dichotomy is defined as "a division into two non-overlapping or mutually exclusive and jointly exhaustive parts. They are often contrasting and spoken of as "opposites". The term comes from dichotomos (divided): dich– ([in] two) temnein (to cut)," by wikipedia (
en.wikipedia.org/wiki/Dichotomy).
 
Business intelligence, on the other hand, is defined as  "the process of gathering information in the field of business. It can be described as the process of enhancing data into information and then into knowledge. Business intelligence is carried out to gain sustainable competitive advantage, and is a valuable core competence in some instances,"
en.wikipedia.org/wiki/Business_intelligence
 
If you want to think about the ubiquitous impact of BI in your workplace, all you need to do is ask yourself "have I ever opened or reviewed a spreadsheet," or is it. Many people think that just because numbers appear in the A3 cell of an Excel worksheet, that they are looking at data. And while in it’s most raw format, a singular number in a cell could be seen as data, how often is that the case in Excel, versus, say a formula showing up in A3, something like =A2 + B2 which results in a singular number display for the end user, or you.
OK that was a bit difficult for the non-analyst to understand. To break it down, simply take the following case:
 
If you have 2 apples and 3 oranges and you want to know how much fruit you have in total for your restaurant, you might enter the ‘data’ into a spreadsheet that looks something like this:
 
In A1, B1 and C1 cells, enter ‘Apples’, ‘Oranges’ and ‘Total’, respectively. These serve as your headers.
Underneath those headings in A2, B2 and C2, enter ‘2’,’3′ and then stop. What do you enter in cell C2?
 
You could easily eyeball 2 + 3, and enter a hard-coded ‘5’, but is that the most useful way to enter DATA into a spreadsheet? What happens if the farmer that serves up your fruit makes a mistake and sends you the 3 apples and 4 oranges? When you enter the new numbers, does the ‘Total’ column change along with the new values?
 
The answer is no. But if you were to enter into C2, the following formula: = A2 + B2, the end result is still 5 from the first example, but when you modify A2 and B2 with the new values of 3 apples and 4 oranges, it automatically recalculates the total to equal 7. This is the relativity of information; or the non-absolute transformation of data.
 
This is a simplistic example but serves an illustrative purpose. In one case, the absolute reference to 5 total pieces of fruit is useless in my eyes for most organizations that utilize long-term or big picture strategy maps to manage performance. Why?
Because the analyst man-power to continually update cells as the data changes is huge, and unproductive, when a relative reference (or using the formula rather than the hard-coded value) is a one time effort, that utilized properly, services the organization to allow analysts to actually analyze rather than just report on information. Most companies utilize their analysts mostly as reporting analysts, rather than data mining experts or statisticians, as they should be utilized because of time involved with manual ETL; (or the extraction of the data from the source systems, followed by the compilation, computation and validation of the data integrity, all of which is still not a guarantee that there won’t be calculation errors). By the time this process is done, the analyst is usually racing to meet some arbitrary or pre-determined timeline in which they haven’t the time to actually draw conclusions from the data that they have now transformed into information.
 
If you think about the two definitions that I led off with, does this dichotomy within most companies translate into analysis strength or weakness within an organization?
 
I would say the latter…
 
There are only a few companies that get "it"; and invest in performance management systems, balanced scorecarding, strategy maps tied to performance indicators, with line of sight through the business units, tied to employees and downstream, through to the end transaction and customer experiences and perceptions of how well you serve their needs. Whew…that was a mouthful…And as I always stress, one should never go down that path without having first built the system manually, and felt the pains of manual ETL and aggregations, as who better to help serve up which product / platform you should choose to RFI during the software selection process that those analysts who do the work itself? Also, if you automate without a manual framework, you have no roadmap by which to build your rules or needs from; you are left flying blind.
 
This would be like driving on a windy road at night with a blindfold on…Figuratively, it is corporate suicide.
 
Yes, there are those companies that buy PM or BI software, only to latter complain that it ‘doesn’t work’. This is usually a false statement driven by the lack of understanding of a BI product or platform, and more of a complaint about how an organization decides to implement that platform within their IT infrastructure and the subsequent processes and business rules built into the BRE (business rules engine or ‘If this, then what’).
But when you do have your manual system in place, and you do start your PM process, say with a product like Microsoft’s Business Scorecard Manager (BSM) which is currently being productized as ‘Performance Point 2007’ (don’t get me started about how excited I am about having the power of being an OLAP administrate and report designer (due to the Proclarity acquisition) all in one with a nice little scorecarding package, and oh, did I mention, integrated with the Office platform, what more could a BI dork like myself ask for?), you, the end user or analyst reading this blog, becomes the organizational fire preventor and not the fire fighter of the past, enabled by technology to do your jobs’ better.
 
When data is transformed into information systematically, you are left with a resource pool that can actually utilize those Harvard degrees, modeling out ‘what-if’ scenarios based on information served up at a click of a button. It is the difference between data and information that motivates the "cream of the crop companies" to achieve stretch goals and BHAGs
 
All in all, a dichotomy exists between the manual and systematic approaches to analysis of information and transformation of data into such information as what serves up PM for any organization. By conquering and dividing out this duplicity, you are left with the strength and knowledge that outweighs what most of your competitors will be thinking when thoughts become their calls to action.
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One thought on “On the Road to Montague: An African Journey towards Performance Management

  1. The post is so true.   So many companies have data but to get any information out of it takes tons of manual analysis.   To be most effective, much analysis should be done automatically so decisions are made real time and PM can take place at the end of a shift, day, or week, rather than after it no longer matters.I have implemented lean manufacturing in many processes, and without PM and BI, only part of the gains will be achieved, and much of it will be lost eventually.   PM is the key to lean and rarely discussed.

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