Hyperactive Data Quality

In economics, the term “flight to quality” describes the aftermath of a financial crisis (e.g. a stock market crash) when people become highly risk-averse and move their money into safer, more reliable investments.

A similar “flight to data quality” can occur in the aftermath of an event when poor data quality negatively impacted decision-critical enterprise information. Some examples include a customer service nightmare, a regulatory compliance failure or a financial reporting scandal. Whatever the triggering event, a common response is data quality suddenly becomes prioritized as a critical issue and an enterprise information initiative is launched.

Congratulations! You’ve realized (albeit the hard way) that this “data quality thing” is really important.

Now what are you going to do about it? How are you going to attempt to actually solve the problem?

In his excellent book Data Driven: Profiting from Your Most Important Business Asset, Thomas Redman uses an excellent analogy called the data quality lake:

“…a lake represents a database and the water therein the data. The stream, which adds new water, is akin to a business process that creates new data and adds them…

Congratulations! You’ve realized (albeit the hard way) that this “data quality thing” is really important.

Now what are you going to do about it? How are you going to attempt to actually solve the problem?

In his excellent book Data Driven: Profiting from Your Most Important Business Asset, Thomas Redman uses an excellent analogy called the data quality lake:

“…a lake represents a database and the water therein the data. The stream, which adds new water, is akin to a business process that creates new data and adds them to the database. The lake… is polluted, just as the data are dirty. Two factories pollute the lake. Likewise, flaws in the business process are creating errors…
One way to address the dirty lake water is to clean it up… by running the water through filters, passing it through specially designed settling tanks, and using chemicals to kill bacteria and adjust pH.
The alternative is to reduce the pollutant at the point source – the factories.
The contrast between the two approaches is stark. In the first, the focus is on the lake; in the second, it is on the stream. So too with data. Finding and fixing errors focuses on the database and data that have already been created. Preventing errors focuses on the business processes and future data.”

Reactive Data Quality

A “flight to data quality” usually prompts an approach commonly referred to as Reactive Data Quality (i.e. “cleaning the lake” to use Redman’s excellent analogy). The majority of enterprise information initiatives are reactive. The focus is typically on finding and fixing the problems with existing data in an operational data store (ODS), enterprise data warehouse (EDW) or other enterprise information repository. In other words, the focus is on fixing data after it has been extracted from its sources.

An obsessive-compulsive quest to find and fix every data quality problem is a laudable but ultimately unachievable pursuit (even for expert “lake cleaners”). Data quality problems can be very insidious and even the best “lake cleaning” process will still produce exceptions. Your process should be designed to identify and report exceptions when they occur. In fact, as a best practice, you should also include the ability to suspend incoming data that contain exceptions for manual review and correction.

However, as Redman cautions: “… the problem with being a good lake cleaner is that life never gets better. Indeed, it gets worse as more data… conspire to mean there is more work every day.” I tell my clients the only way to guarantee that reactive data quality will be successful is to unplug all the computers so that no one can add new data or modify existing data.

Proactive Data Quality

Attempting to prevent data quality problems before they happen is commonly referred to as Proactive Data Quality. The focus is on preventing errors at the sources where data is entered or received and before it is extracted for use by downstream applications (i.e. “enters the lake”). Redman describes the benefits of proactive data quality with what he calls the Rule of Ten:

“It costs ten times as much to complete a unit of work when the input data are defective (i.e. late, incorrect, missing, etc.) as it does when the input data are perfect.”

Proactive data quality advocates implementing improved edit controls on data entry screens, enforcing the data quality clause (you have one, right?) of your service level agreements with external data providers, and understanding the business needs of your enterprise information consumers before you deliver data to them.

Obviously, it is impossible to truly prevent every problem before it happens. However, the more control that can be enforced where data originates, the better the overall quality will be for enterprise information.

Too many enterprise information initiatives fail because they are launched based on a “flight to data quality” response and have the unrealistic perspective that data quality problems can be quickly and easily resolved. However, just like any complex problem, there is no fast and easy solution for data quality.

In order to be successful, you must combine aspects of both reactive and proactive data quality in order to create an enterprise-wide best practice that I call Hyperactive Data Quality, which will make the responsibility for managing data quality a daily activity for everyone in your organization.

Please share your thoughts and experiences. Is your data quality Reactive, Proactive or Hyperactive?

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