Ten Steps to a Data Quality Strategy

If your CEO asked customer service, technical support, marketing and sales to provide a customer list with contact and history information, how likely would each organization bring the same list, with the matching addresses and history with correct and same spellings? If you answered not likely youíre not alone. This example illustrates a common data quality issues. Companies are collecting customer data in multiple sources for example from call centers, websites, and billing in multiple databases from CRM, to sales, to accounting systems, and then trying to effectively use this data. 

When it comes to make fact-based decisions, the importance of data quality cannot be stressed enough. Bad data leads to bad decisions. The effectiveness of any decision is based upon the quality of the data used to make the decision. As a result, companies are starting to see merit in having strong data quality strategy. Building an effective data quality strategy can help with many of the problems associated with poor data quality by ensuring issues are identified, corrected and ideally prevented, providing a foundation for reliable and consistent data use across the organization.

Building a data quality strategy begins by developing a data quality profiling and data cleansing plan. Because a company's data is a strategic asset, a data quality strategy should not be buried in the ranks of IT. It belongs at the executive level. 

Steps to developing a data quality strategy:

1. Secure the leadership team's commitment to a data quality strategy.

2. Avoid boiling the ocean. Select one specific business imperative and evaluate how the data impacts the ability to move this imperative forward.

3. Drill down to all the data elements associated with this priority.

4. Identify all the data quality issues in the data elements.

5. Identify the root causes of the poor data quality what processes need to be addressed so the data quality issues won't return

6. Create a single business rule for all data collected 

7. Define the information chains that feed into all the databases that support this priority

8. Establish a master data management system and data stewards

9. Monitor record completeness, accuracy, percentage of duplicates, etc in order to maintain data quality

10. Be proactive, revisit and adjust processes as data conditions change