My hypothesis is that far too many challenges in Business Intelligence are construed as technology issues. And hence very often BI programs are driven as technology initiatives - and often fail to make business impact.

Is Data Quality a Technology Issue?

Ing Vysya bank in India created a new product and asked for a set of potential customers. IT came up with a list of 5,000 target customers - a number that shocked the business - the actual number was expected to be 10 times more.

The CIO, CVG Prasad investigated and discovered that errors in date of birth entries made the list inaccurate. A drive commenced to address this problem - both corrective and preventive action was taken. As expected, the actual numbers were much higher.

This data quality issue had the potential to tilt the balance. At a target number of 5,000, the product was unviable; at a target number of around 8 times more, the product was definitely a go.

You see what I am saying? Many times business has low confidence in the quality of data (completeness, fidelity, accuracy). This implies that:

1. the decisions taken using this data could be wrong 
2. the data is not used for decision making - executives instead lapse back to relying on their intuition.

Both of the above are sub-optimal.

Viewing Data Quality as a business issue helps us drive corrective and preventive action to focus on business decision making.