DIY Data Management

I’ve just been reading Phil Simon’s Data Roundtable post, ‘Excel, Office 15 and Big Data’, in which he ponders a few questions regarding Microsoft’s updating of their most popular software offerings. Towards the end of the post Phil asks a question that sent shivers down my spine as I, like him, already knew the answer – “Is there a CIO right now who won’t invest in a sufficiently powerful application because he or she thinks that Excel will be able to handle [the management of ‘Big Data’]?”

Unfortunately the answer is yes. The bigger problem though is that it’s not just CIOs who think like this. Although they may hold the purse strings when it comes to application investment, they can be swayed as they look to their trusted advisors for guidance. But if the general opinion that Excel or some other EUC solution is ‘probably good enough’ then someone trying to push through a more strategic and robust solution could be facing an impossible task.

People are becoming more technologically savvy and as a result a new wide-spread confidence in data related tasks can be seen. In some ways this is a good thing as a better general awareness of data related issues can aide in the implementation of governance and quality initiatives. However, it can also mean that business areas are much more likely to  bypass the traditional IT development processes in order to implement their own EUC solution and especially so if they have been previously burned by IT. A new breed of DIY data solutions rise up in silos across an organisation thanks in part to the ease at which tools like Excel make basic data management accessible to the masses.

What this all means is that a strong and well implemented Data Governance programme is needed more than ever to ensure that appropriate controls, processes, people and technology are in place. EUC solutions most definitely have their place, but they must be used and controlled appropriately and be covered by the same governance as any other technology within the Enterprise or data management issues can proliferate and escalate rapidly.

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