Had a great day on Friday at work.  More acceptances poured in on Friday for the Customer Advisory Council.  Also, made a couple of significant breakthroughs on messaging for the launch on Friday and again yesterday.

Otherwise, an uneventful weekend having played an uber-painful 3-hour nine hole round yesterday.  I could write a whole 90 day blog series on “Why can’t we play a round in 3 1/2 hours like the Scottish?!?!?!” … but I won’t.  Today I was supposed to run a half marathon in support of a great cause (www.armyrun.ca), but I didn’t do much training so I bailed and ran the 5 km with friends instead.  Man, a 5 km race feels soooo much better than 21 km race.  At least 4 times easier in fact.

OK, on to Difference #1.

The amount of data it takes to run the business is significantly more than the data required to manage the business.

At one point, this was likely obvious to all, but it changed for a lot of people at some point.  Not that it matters much one way or the other, but I think this occurred when everyone generally accepted reporting as one part of business intelligence.  For a period of time, business intelligence was almost soley associated to management software (and remains a cornerstone, of course).  Operational reporting typically uses a ton of data.  The (implicit) logic statement must have ended up “operational reporting” uses “lots of data”; “operational reporting” is a type of “reporting”; “reporting” is a type of “business intelligence”; “business intelligence” is associated with “managing the business”; therefore, you must need a lot of data to manage the business.  And don’t get me started on datawarehouses!

This is not to say you don’t need a lot of data to manage your business - just orders of magnitude less – than data to run the business.

Let’s look a purely fictional retail example (as an industry that generates a lot of data).  And this might seem silly, but some people are storing this granularity of data in management systems and then wondering why time to results is so poor and why the big picture gets lost.  Let’s say we work at a large retailer.  We clearly need to record a lot of data on most every transaction for a lots of reasons, including returns, pilferage, ordering, etc.  For example, today, we might have recorded that “X-Men: First Class” sold a Bluray copy at a store in Norton, MA at 2:13 PM by salesperson Mary; another on DVD at 3:17 by John and so forth.  We might need to run an operational report tonight if the inventory is off on a random inventory check (or stock out when the POS system was showing 2 in stock).  I will stop now that the horse is glue.  Data is important for operations.

However, that level of detail cannot possibility be useful in making management decisions across the organization.  It is just too granular.

Back to management systems needing a lot of data (if a lot less data), management would still like to have the aggregration of this transactional data.  For instance, management would almost certainly like to plan – at some level – for sales of DVDs vs BluRays (and likely at the level of science fiction) in Southern MA vs other regions next year based on sales this year.  As I said, it still takes a lot of data.

In many industries the differences we are talking about are petabytes vs terabytes.

I realize that at this point, this “difference” might be raising either a “huh?”, “so what?” or “duh?” response.  It will make sense in the coming weeks (at least I certainly hope it will!).

’til the user interface/user experience … Kirk