Data Quality: Too much of a good thing?
Having definitively established that good DQ underpins good BI, the only practical counter argument was one of degrees – that is, how perfect does your data really need to be?
To answer this, Enterprise BI Architect (VP) at The Bank of New York, Ron van der Laan, went back to basics. Laan stated that organizational data sets should be optimized to the point where they can easily and continually generate accurate reports for accurate decision-making, and no further.
“Maximizing your DQ makes a lot of sense up to a certain level. Beyond this level, the cost often exceeds the gains. Addressing the last of the DQ issues is always the most expensive,” said Laan.
“Based on your budget constraints, increase your DQ to a level that is more than good enough for the business to make solid decisions, and use your remaining funds to add value to your BI layer. This way you will optimize your investment in your BI solution.”
This attitude is shared by data science evangelist, Mike Loukides, who made the following statement in a blog post by OpenBI co-founder, Steve Miller, on Information Management:
“Do you really care if you have 1,010 or 1,012 Twitter followers? Precision has an allure, but in most data-driven applications outside of finance, that allure is deceptive. Most data analysis is comparative: if you’re asking whether sales to Northern Europe are increasing faster than sales to Southern Europe, you aren’t concerned about the difference between 5.92 percent annual growth and 5.93 percent.”