Continuing with the ancient teachings of Sun Tzu and the Art of War, we can gain further insight in the actions needed for building a solid foundation for the Art of Data Quality.
Sometimes it's about timing.
"The quality of decision is like the well-timed swoop of a falcon which enables it to strike and destroy its victim."
Place the data quality analyst in the position of a falcon and your enemy is that lovely data error that needs to be utterly removed from the system. Ultimately, the data will ony be removed by a decision made by the data quality analyst or promoted to higher-ups to decide upon, again identified and promoted by the data quality analyst. The quality of your decision and your analysis has significant impact on how your data repository is viewed by others.
"Rouse him, and learn the principle of his activity or inactivity, Force him to reveal himself, so as to find out his vulnerable spots."
Taking this teaching into account, we can state that Sun Tzu may have been telling his military students to be proactive. A proactive approach to seeking out and removing bad error to improve your data quality is essential.
Taking into account the definition of proactive, "Taking the initiative by acting rather than reacting to events."
Jim Harris, talks about a hyperactive data quality and mentions a proactive approach to handling problems from coming in. That approach looks at prevention. Prevention is a significant key in proactive data quality. Incorporate Sun Tzu's teachings in proactive data quality and you will be vigorously looking for errors, you'll be data profiling like you've never profiled before. Nothing wrong with that either, it's may lead to unwanted discoveries or great opportunities for data cleansing.
"Do not repeat the tactics which have gained you one victory, but let your methods be regulated by the infinite variety of circumstances."
I'm all for standards, governance, policies and processes to get the job down. The way the job should be down. BUT, you also need to be flexible and adaptable. You may have just saved the company a great deal of money with some fancy DQ manoeuvre. However, just remember the next problem that comes along does not necessarily follow the same trail in. So, you must know your systems, processes and be prepared to make adjustments to your tactics to clean that data quickly and effectively.
"We cannot enter into alliances until we are acquainted with the designs of our neighbours."
Not so much about timing, but an important message nevertheless. When developing a service agreement with internal client groups, external customers, vendors, reporting teams, or anyone that needs your data. Be prepared to get 'acquainted' with them. Wine and dine them, understand their needs, desires and limitations. What is it that they want and why they want it? If they are providing provisioning statistics to regulators why do they need marketing campaign statistics. Why do they want the data on a weekly basis when they only analyze it monthly. Get to know them, so you can better serve them. Remember data quality is not just about bad data or good data, it's also about relevant data.
In conclusion for this post, I'd like to say,
"Ponder and deliberate before you make a move."
Remember "haste makes waste", plan and time yourself carefully and know your facts.
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