Concluding a three-part series of several Sun Tzu teachings in the Art of War and how they may apply to the Art of Data Quality.
Sometimes it’s all about the troops in your camp.
“To begin by bluster, but afterwards to take fright at the enemy’s numbers, shows a supreme lack of intelligence.”
Are you the type of data quality analyst who wants everything. Do you take all challenges on so you look good, or don’t trust others to do the job right; well perhaps you should think before you take it on. Because if you ever ask for it, and can’t deliver the goods you will look like a fool, and be considered unreliable, a definite career shaker.
“He who exercises no forethought but makes light of his opponents is sure to be captured by them.”
Closely linked to the previously mentioned teaching. In this one we learn that if you do not plan (use forethought), you may find yourself in dire situation. Remember the general rule of thumb. Eighty percent planning-twenty percent execution.
“Hence the experienced soldier, once in motion is to set up one standard of courage which all must reach.”
It always pays well to have experienced staff in your data quality competency center. They are active, they know the data, they know the business, they’ve helped developed the standards required to make your data a high quality asset. Another aspect of an experienced analyst, they are the ones to establish the governance that can be applied to almost any situation. In their absence, bad data can be targeted and the result will be a corrected situation with quality data at your disposal; all based on their governance input. A lot of people won’t do this, for fear of job security, it’s those rare individuals that do it, that will move your organization forward. So to you I say, keep the courageous.
“Success in warfare is gained by carefully accommodating ourselves to the enemy’s purpose.”
Data in general always has a purpose. Bad data has no purpose, it is created because someone, or something, somewhere ‘screwed up’. You cannot accommodate yourself to thepurpose of bad data, but you can accommodate yourself to ‘high-traffic’ areas, areas that are most likely to see bad data come-in. To sum it up, be prepared and recognize those areas in your model where bad data will rear it’s ugly head and be ready to chop it off.
In conclusion, I’d like to quote Jim Harris’s comment from the first Sun Tzu article. A paraphrase from Sun Tzu.
“Hence it is only the enlightened executive and the wise leader who will use the highest intelligence of the enterprise for purposes of data quality, and thereby they achieve great results. Quality is an important element in data, because upon it depends an enterprise’s ability to succeed.”