GOVERNANCE FRAMEWORKS

The insights that a business can extract out of data are only as good as the data itself.  Bad data can come from every area of your organization in many forms,  and can lead to difficulties in mining for insights and ultimately poor decision-making.

Data quality is a worrisome subject for many executives. According to the Forbes Insights and KPMG “2016 Global CEO Outlook” 84% of executives are concerned about the quality of the data they’re using for business intelligence. Poor data quality can be costly; an astonishing study conducted by MIT Sloan notes that bad data can cost as much as 15-25% of total revenue.

The cost of doing nothing explodes over time. Poor data quality management can be mitigated much more easily if caught before it is used — at its point of origin. If you verify or standardize data at the point of entry, before it makes it into your back-end systems, we can say that it costs about $1 to standardize it. If you cleanse that data later, going through the match and cleanse in all the different places, then it would cost $10 in comparison to the first dollar in terms of time and effort expended. And just leaving that bad quality data to sit in your system and continually give you degraded information to make decisions on, or to send out to customers, or present to your company, would cost you $100 compared to the $1 it would’ve cost to deal with that data at its entry point. The cost gets greater the longer bad data sits in the system. The goal, therefore, is to catch bad data before it ever enters your systems.