There has been a lot of talk lately about the need to improve data quality for IT groups. But, is it always justified?
I agree with the importance of good quality data, but I find the driving force behind it is not always clear to most people.
When I think about positive business outcomes, I automatically think about the decisions being founded in high-quality data. I don’t know how positive business outcomes can come about without it. Some people, however, look at it in reverse and seek good data quality as the final goal.
What value does good data quality have if it is not associated to a positive business outcome? None.
Many projects and initiatives achieve the initial goals, but deliver zero value to the business. These have failed because they haven’t made the business more efficient or effective, nor have they enabled the business to grow. If none of these were accomplished, or worse, nobody set it as the goal, how could anyone have justified them to go forward? The answer is that nobody knew enough to question it.
The question we might want to ask ourselves then is why does good-quality data not always deliver positive business outcomes? This is generally because there is a lack of understanding as to how the data will be utilized. When there is no context for why it needs to be improved or the objective is stated solely as a raw data cleansing initiative, it is easy to not deliver value.
This continues to happen, in part, because of the continued isolation of departments within the organization. The ongoing pressures to positively contribute requires that every IT department demand to understand how the effort being asked of them impacts the business. The conversations will be challenging at first, but over time they will become more natural and the norm. Once there is a clear understanding of how the business operates and envisions growth, IT can become a major player in the discussions on growing the business.
IT departments have available to them raw material, data, that tells the story about the business. It does this, of course, in the historical sense as one might imagine, but it also provides the platform to project future direction for the organization. Once this is made available with business context, the opportunities present themselves to everyone making decisions on organizational direction and initiatives supporting the direction. The key is the enablement of the platform.
Data quality can only be defined within the context of what it does for the organization, and what the organization does with it. There is no shortage of talk about improving data quality, but the solutions must enable business growth, improve service quality and ultimately provide value to the organization. Organizations need to operate more efficiently and effectively and the resources to do that are available. Everyone just needs to demand that data quality initiatives have business context which is clear and understood.