Tactical Data Quality

I am the first one to agree that a strategic approach to data quality, should be the way to go.

Data Quality should be framed within a robust data strategy and approached systematically. However, after many years in the field, experience has been showing me that for most of the cases this is not true.

Tactics

The art or skill of employing available means to accomplish an end.

When we look at the characteristics of the implementation of a data quality strategy in an organization some characteristics are easily identified:

These are expensive initiatives; they are time and resource consuming and span through long time frames.

Also, they are deeply intrusive and disruptive, creating the natural resistance to change within the organization, creating an incredibly challenging ecosystem to work on.

Finally, we are talking of the kind of initiative that might take years to break even and deliver ROI, making it hard, even with a strong sponsorship, to keep the necessary traction to complete all the necessary changes.

These are the most frequent causes I have seen identified on “project post-mortem” reports, and I’m forced to agree, and making the option for tactical data quality initiatives more plausible and efficient, and in the end the motor from within the organization for a full Data Quality Strategy program.

Tactical Data Quality

Involving or pertaining to actions, ends, or means that are immediate or short-term in duration, and/or lesser in importance or magnitude, than those of a strategy or a larger purpose

When dealing with a tactical approach to data quality we are talking about addressing a specific problem with known consequences, so we can define a tactical initiative as having:

• Reasonable funding model.

• Targeted.

• Focused effort.

• Short timeframes.

• Increase internal engagement.

• Delivering targeted return on a short timeframe.

In large organizations, even the ones without a strong data governance framework in place, the opportunities to start these initiatives are quite abundant. Across all the business areas there are pain points related with the quality of data and identifying them is not a challenge.

We just need to find that business stakeholder that can passionately and effectively articulate the impacts of poor data quality in their processes and help him solve the source of his problems.

Minding that most of the time it is not about identifying the actions that can reach the best ROI but identifying who is the one that has a problem that needs to be targeted, assessed, and mitigated quickly.

It is easier to help someone that asks for help than persuading someone that it needs help.

A sequence of these targeted initiatives has the benign effect of increasing the awareness of the importance and impact of data quality across the organization, increasing the overall internal engagement, turning critics into evangelists, and paving the way to a more structured and strategic approach enterprise wide.