It's understood by many in business that it should be a key goal to look out for data quality. In a corporate world that's growing ever bigger and unwieldier, it's harder now than ever to keep a good handle on your customers, your competitors and the world around you. Accurate data is necessary in order to paint a complete picture.
It's easy to say all of that in theory, but enforcing it becomes a much more difficult matter. Data quality is a complicated pursuit. Information is moving through companies' systems at a fast pace, from countless sources. Whose job is it to corral everything, verify it and keep it in a format that's easy for everyone to understand and use?
The simplest answer to that question is: everyone. For data quality goals to be achieved at the enterprise level, an "all hands on deck" philosophy is likely required.
The complexity of the problem
It's beneficial to understand why the challenge of achieving data quality is so great. According to TechTarget, the optimal way to go is to have everyone "own" data quality together - but David Loshin, president of data consulting firm Knowledge Integrity Inc., notes that this is difficult for three reasons.
- Originators create data for one purpose, and it's often very difficult to translate to another purpose that someone else can use.
- Data users might not have the resources - meaning time, money, hardware or software - to achieve everything they'd like where data quality is concerned.
- Transforming data collected by one system into something useable in another is often, unfortunately, just not feasible.
The trick is to overcome all of these difficulties. There are no easy answers, but collaboration should certainly help.
Accountability for everyone
Loshin believes that the first step is to "put the data quality controls in users' hands." Have everyone take accountability for the quality - and ultimately, the utility - of their data.
"If a department or business unit controls the rules, it seems unwieldy to 'outsource' enforcement to another party," Loshin explained. "The only logical conclusion is that users should enforce their rules at the point of consumption. In other words, because data quality is relevant based on the context of how the data is used, this is a case of beauty (or quality) being in the eye of the beholder (or user)."
Simply put, everyone who works with data should be doing their part to make the process better. By collaborating in this way, entire enterprises should find the capability to achieve more daily.
