Can We Actually Confront Data Quality with Business Intelligence?

Similar to years past, data quality may yet again win the superlative award for the most discussed topic in the business intelligence industry. Unfortunately, it's becoming about as boring to read as the ubiquitous "Top X Things NOT to do in BI Projects" lists. Yet, for the same reason those articles are written over and over again, and we have to continuously discuss data quality because we're still seeing the same mistakes from years ago.

You might be wondering: "haven't we corrected this by now?" Despite the emergence of tools to assuage our data quality concerns and provide assurances, the topic keeps itself blissfully relevant. Our subtle inconsistencies, poor data collection methods, and our overall lack of time and resources sustain the problems, preventing many organizations squashing the issues at their origin.

I still want to believe there's an organization out there with pristine and perfect data, but after many business intelligence implementations across organizations of different sizes, industries, and locations, I've learned that this is nothing more than wishful thinking. Data quality initiatives have been around for a long time (mainframes and address data, anyone?), and with the current explosion of data output from systems and devices, we can be sure that data quality will remain a topic of discussion for years to come.

So, how exactly does business intelligence fit into this massive data quality picture? Funny (and sadly) enough, many organizations only find out about their data quality issues once they actually start using their business intelligence solution. The problems only become apparent when the nice dashboards they expected don't look as nice or useful. In some scenarios, certain types of analysis aren't available because the poor condition of the data quality renders them incomplete and/or impossible.