'Data Provocateurs' Disrupt Organizational Momentum
Thomas C. Redman
All of us depend on data created elsewhere to do our work. In the face of errors, most people’s natural reaction to correct such errors in the data they need—after all, when you’re dealing with a mountain of day-in, day-out demands, it seems the fastest, most efficient way to complete the task at hand. The problem is that finding and fixing erred data soon becomes a permanent fixture. Writ large, it is expensive and time-consuming. Worse of all, it doesn’t work well—too many errors leak through, rearing their ugly heads later on and leading to larger mistakes, bad decisions, and angry customers.
As data quality practitioners know, the alternative is to prevent errors at their sources, obviating the need to find and fix them. While this seems obvious enough, it simply doesn’t occur to most people. That’s where data provocateurs come in—individuals who get their teams, departments, and companies to address data quality proactively. More people need to step into these roles and companies need to remove barriers that prevent them from doing so. Data quality practitioners have a special obligation, both to become provocateurs themselves and to assist potential provocateurs.
In an earlier article, I called out “data revolutionaries,” those pushing for change within their companies. Since that time, I have ruthlessly re-examined why some achieve stunning results, others do okay, and a few fail miserably.
This article summarizes what I’ve learned, proposes a simple process by which anyone can evaluate whether they have a DQ problem, and points the way to qualifying as a provocateur.
Read this article in the September 2016 IQ International Journal
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