If you are looking for a way to fund your data quality objectives, consider looking in the closets of the organization.  For example, look for issues that cost the company money that could have been avoided by better availability of data, better quality of the data or reliability of the data.

Availability of the data – Consider how fast we can get data out the of source system into the reporting or data warehousing database to be reported on. When a mistake or incident is found in the report, it may be too late to correct. We may want to consider faster updates (near real-time) into the reporting environment with automatic data profiling to find the mistake BEFORE it gets to the report. What if there are multiple source systems that need to be integrated before we can see if there are data issues?

Quality – With the organization’s customer information, how much does it cost for mailings where the same address gets multiple flyers or pamphlets? It can add up over time.  Scrubbing the customer data, hopefully with a data quality tool, will help alleviate these issues. Besides a one-time scrub, the software can also monitor the data in real-time mode making clean-up as easy as pie. While this sounds easy, a project is required with agile type turn-around.

Reliability – As the data gets cleaned up, and consumed by the business users, they will come to trust the data.  But more than trusting the data, they will trust the IT group to administer the data quality algorithms to clean up and delivery the data to them.

So, maybe it’s not about just completing a data quality initiative or making money on data quality, but more like avoiding future mishaps.