Data In, Decision Out
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This recent blog post by Seth Godin made me think about the data quality adage garbage in, garbage out (aka GIGO).
Since we live in the era of data deluge and information overload, Godin’s question about how much time and effort should be spent on absorbing data and how much time and effort should be invested in producing output is an important one, especially for enterprise data management, where it boils down to how much data should be taken in before a business decision can come out.
In other words, it’s about how much time and effort is invested in the organization’s data in, decision out (i.e., DIDO) process.
And, of course, quality is an important aspect of the DIDO process—both data quality and decision quality. But, oftentimes, it is an organization’s overwhelming concerns about its GIGO that lead to inefficiencies and ineffectiveness around its DIDO.
How much data is necessary to make an effective business decision? Having complete (i.e., all available) data seems obviously preferable to incomplete data. However, with data volumes always burgeoning, the unavoidable fact is that sometimes having more data only adds confusion instead of clarity, thereby becoming a distraction instead of helping you make a better decision.
Although accurate data is obviously preferable to inaccurate data, less than perfect data quality can not be used as an excuse to delay making a business decision. Even large amounts of high quality data will not guarantee high quality business decisions, just as high quality business decisions will not guarantee high quality business results.
In other words, overcoming GIGO will not guarantee DIDO success.
When it comes to the amount and quality of the data used to make business decisions, you can’t always get the data you want, and while you should always be data-driven, never only intuition-driven, eventually it has to become: Time to start deciding.
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