Reactivity vs. Proactivity

Blog Administrator | Analyzing Data Quality, Data Management, Data Quality | , , ,

By David Loshin

In the past few months, we have looked at technical approaches to data quality and the use of data quality tools to parse, standardize, and cleanse data. In this next series of posts, it is time to look at harnessing the power of these tools and techniques to support a data quality management program. Most organizations are relatively immature when it comes to addressing data quality issues. Some typical behaviors in an immature organization include:

   · Few or no well-defined processes for evaluating the severity or root causes of data issues
   · Little or no coordination among those investigating data errors
   · Evaluating the same issues multiple times
   · Correcting the same errors multiple times

These are all manifestations of a more insidious problem: knee-jerk reactivity,
which presumes that addressing the symptoms solves the problem. But in reality,
applying these bandages to open wounds is merely a temporary fix. This suggests
that incremental maturation of data quality processes involves transitioning
from a reactive environment to one that operates within the context of a series
of policies and controls.

The manifestations of immaturity listed here are some fertile areas for improvement, namely:

   · Defining processes for evaluating data errors when they are identified
   · Instituting methods for coordinating those evaluations
   · Applying corrections once, and only once.

As a byproduct of coordinating evaluation, your team will be less inclined to
evaluate the same issues multiple times! In my next set of posts, we will look
at ideas for each of these suggestions.