Advocates of Value: A NorthStarBI Blog

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November 22, 2010

Five thoughts for approaching DQ with a level head

1. Unless there is clear value (as defined by tangible outcomes that can be measured against stated business goals), there is no need for a massive DQ software solution. In fact, do not assume a specific solution of any sort. You DO need DQ measures (in order to assess size and impact of issues AND to monitor improvements over time), but this can be done using manual or semi-automated data profiling techniques. Data profiling tools are much cheaper to purchase (some are free) and carry little to no ongoing management overhead while enterprise DQ tools are very expensive. Follow the value. Move from profiling (to identify scope and location of most valuable issues to address) to these larger tools IF necessary.


2. Create data stewardship (ownership) from the audience. That is, get the business people involved up-front and then have a clear set of accountabilities that do not take an exorbitant amount of time. Have the interested people who use the data responsible for keeping an eye on the DQ. Then have a simple process for prioritizing any initiatives that come out of issues that arise so that the business defines DQ projects.


3. Do not initiate any DQ improvement projects without a context. This is not the same as attaching all DQ to a project and ignoring all other DQ (to bite you in the behind later). But the question of value should always drive DQ priority and the amount of time and effort to be expended. What specific business outcome or goal is going to be advanced if we improve this or that DQ issue?


4. Resolve any new or ongoing DQ issues in the most simple, cheap and effective manner. Is the best solution even an IT solution or can the issue be resolved through process changes? Are the people involved in the process clear on the value to them of making the change? Will process changes actually create better long-term DQ improvements than an IT approach would? If there needs to be an IT solution, be sure there is a clear method in place for communicating needs from business to IT.


5. Keep it simple! Do not create unnecessary process, software, documentation or meeting overhead where there is no clear added value! Drive all your activities from the goals and intended outcomes. Try to create a solution that is practical, doable and for which the participants can see the value for themselves.


Expertise required:
For improving and maintaining DQ for the purposes of valuable BI you need an understanding of the business goals, an understanding of the business processes, and an understanding of the way data can be used to drive business outcomes.


The IT skill sets required are much less critical and will be determined from the business requirements. It may be quite simple profiling skills (which is more about analytics than IT). On the other hand, especially in large enterprises, there may be a need for someone who can set up and maintain a full-scale DQ software system and related processes. But mainly, the management of the business data stewards, the DQ monitoring (by business), the project prioritization, and the assessment of solution are the key zones of expertise required.

Posted by Kelly Lautt and John E West at 1:30 PM | Comments (0)