#2: Here’s a thought…

An occasional series in which a review of recent posts on SmartData Collective reveals the following nuggets:

A recent survey of business and IT leaders carried out by Kalido shows that over two thirds of businesses have no clear data governance program, and have no plans to implement one — and 13% are unclear as to what data governance even is. The research also reveals that nobody knows who is supposed to be responsible for maintaining the accuracy of data. In 31% of the companies questioned, this role is held by the IT department – but IT organizations typically don’t have much control over the business processes that lead to poor data in the first place.

So this leads me to suggest a different metaphor for BI projects. Major elements of them are much more like archaeological digs than traditional building. The extent and importance of a dig is very difficult to ascertain before work starts and both may change during the course of a project. It is not atypical that an older site is discovered underneath an initial dig, doubling the amount of work required. So, my belief…

An occasional series in which a review of recent posts on SmartData Collective reveals the following nuggets:

A recent survey of business and IT leaders carried out by Kalido shows that over two thirds of businesses have no clear data governance program, and have no plans to implement one — and 13% are unclear as to what data governance even is. The research also reveals that nobody knows who is supposed to be responsible for maintaining the accuracy of data. In 31% of the companies questioned, this role is held by the IT department – but IT organizations typically don’t have much control over the business processes that lead to poor data in the first place.

So this leads me to suggest a different metaphor for BI projects. Major elements of them are much more like archaeological digs than traditional building. The extent and importance of a dig is very difficult to ascertain before work starts and both may change during the course of a project. It is not atypical that an older site is discovered underneath an initial dig, doubling the amount of work required. So, my belief is that BI professionals should not be likened to architects or structural engineers. Instead, the epithet of archaeologist is much more appropriate.

If you’re like me, you’ve noticed that the social media buzz is taking far more time and energy than the answer to the question, “How will sales and marketing support this year’s corporate objectives?” Many sales and marketing organizations have emphasized social media at the expense of their strategies. It’s as if strategy doesn’t matter anymore. Newsflash: It does. Moreover, the past is prologue. Companies that emerged from the last recession did so by returning to fundamentals. They understood that technology—even the coolest let’s-get-naked-and-party, Sandhill Road-backed, WTF 2.0 software—was nevertheless still a means to an end.

The scene is your company’s conference room. You have just presented your new plan outlining the data governance projects for the entire year. Each department argues persuasively for support from the data governance team. After limited discussion, the budget is approved and 95% of your team’s expenses have been committed for the current budget. This part of the meeting allocating millions of dollars takes place in about 60 minutes. At this point, the meeting leader mentions that the company has been considering the installation of a Coke machine in this section of the building. With a few minutes left in the meeting, he asks what drinks people want in the machine. For the next 45 minutes, the debate rages with a heightened level of intensity. By the time the meeting adjourns, nearly as much time has been spent on the Coke machine as has been spent on the entire data governance budget for the year.

We found out that a particular client was using THREE data mining software packages. Not statistical software packages, or the base versions, but the complete, very expensive data mining software packages. Their reason? Well, they have the opinion that some algorithms/techniques in a particular data mining software package are much better and accurate than the same algorithms/techniques in another package. Unless a particular DM software has a totally different and new algorithm for which you can’t obviously make a comparison, I haven’t come across or heard of any stark differences among model performances and results for the same algorithms offered by the reputed DM softwares. Data mining solutions and the subsequent business benefits are not solely driven by model accuracy; a lot depends on how you interpret and apply the model’s results, too.

Research from Forrester seems to suggest that IT job cuts in 2009 won’t be as steep as they were in the 2001/2002 dot com bubble burst. Forrester says that the US market for jobs in information technology will not escape the recession, with total jobs in IT occupations down by 1.2% in 2009, but the pain will be relatively mild compared with past recessions.