Wednesday, September 16, 2009

Stop Justifying Data Quality Programs and Do the DQ Work Already!

In a recent discussion with a good friend, I learned that they are in the middle of justifying their work in a data quality team. This being said, a few months ago they were doing it as well, and at the beginning of the year they had just wrapped up another justification project, in the beginning of the economic downturn, it was being done as well. I also know that a few years ago when I was with the team, we also had to do it.

It's a shame. A terrible shame! Some organizations understand the importance of data quality, sometimes that understanding has come at a cost:

• Lost thousands to millions;
• Faced national embarrassment;
• Or made significantly big policy screw-ups.

While other organizations, are more pro-active and have established a data quality team and program to prevent such events from happening. An activity that is considered a best practice and essential to any information technology/business intelligence structure.

However, in either case, you may have someone, traditionally a senior manager, who sees data quality as a cost, a black hole. Yes there is a cost, however the benefits outweigh the costs in a variety of ways.

• Reduction in re-work due to good data quality;
• Improved incoming data quality and data processing due to pro-active initiatives with incoming data migration and integration projects;
• Proactively preventing data quality issues from occurring;
• Improved decision making, using quality data, and more.

To my old team and senior management:

Stop with the justification exercises and begin looking at the benefits and what this dedicated group of data quality analysts have accomplished year after year.

• Recognized Finalist Best Practice by TDWI in DQ;
• Hundreds of data modelling, metadata, data processing and data corrections to incoming projects per year;
• Proactively seeks data processing improvements to improve data loads - ultimately reducing costs;
• Client support to decision makers who really don't understand the technology aspects of the data and its routines;
• Dozens of change management practices each year to improve data quality and data processing which collectively prevents lost revenues, increases sales and manages maintenance costs by reducing reruns and supporting programs such as customer profitability, and other CRM initiatives.
• The estimated benefits weigh in at an average of $1-1.5 million a year if not more.

Another justification exercise only takes the team away from doing what needs to be done, data quality.

So to the senior management in this organization and any other, yes there is a cost to any data quality program. Just remember a data quality team is your vanguard to any organization that deals heavily in data. They bring in benefit. They enable your decision makers. They protect your greatest asset - data!

A good DQ team = Great Value!

1 comment:

  1. At the Data Governance Institute, we preach the importance of stating value in a way that can be heard. We suggest an A-B-C formula.

    Because we did A, we got B results, which lead to C impact on the organization.

    The nice thing about this formula is that it clearly implies what you DON'T get if you don't do A.

    From your example above:
    Because we fielded a DQ team (A),
    we prevented many problems and provided support to those depending on data to make business decisions (B),
    which resulted in an average annual benefit between $1M and $1.5M (C).

    Gwen Thomas,
    President, the Data Governance Institute