Top-down Versus Bottom-up Data Quality Approach

Have you heard of the discussion comparing Top-Down versus Bottom-Up approaches to data quality implementation? In this blog post we’ll introduce the concept and outline some considerations for Data Quality professionals. Subsequent blogs will dive deeper into some of the activities of each approach that use the Conformed Dimensions.

Top-down Approach:

In general, the top-down approach uses a more business and process focused orientation. Typically, top-down approaches use the voice of the customer to identify the most urgent issues relating to data. The members of the team conducting the analysis are typically business department members or consultants. They typically start with a conceptual issue that is broad and then require more detailed definition all the way to root cause analysis (RCA). At the point that the 5 Whys (process of asking why something happens at least five times) have exposed a potential reason for the issue, you begin to conduct root cause analysis that often leads to technology team involvement and data queries.

It should be said that there is no judgment about which approach is better, so it should be said that the bottom shouldn’t be thought of as inferior in any way. However, it must be used at the right time and in conjunction with the Top-down depending on the executive leadership’s objectives for the company and current situation.

Bottom-up Approach:

This approach usually focuses on the actual data that documents a business issue. It is often started by technical teams in IT because they have first hand knowledge about where the data is located and data transformation rules in pipelines that feed the reporting or warehousing systems. Sometimes technical managers create a business case for clean-up efforts or data scrubbing based on data profiling (review of the completeness or validity of business critical data). These efforts rely on exposing existing issues often identified during root cause analysis of existing defects/tickets that application teams manage.

At this point you’ve probably realized that there isn’t a right and wrong way to use these approaches. You are right, they both are required and data quality leaders must be comfortable using them as needed.?

What does the?DMBOK2 say regarding these two approaches?

“Typically, a hybrid approach works best – top-down for sponsorship, consistency, and resources, but bottom-up to discover what is actually broken and to achieve incremental successes.”

In other words, in the steady state data quality management environment you’ll use both approaches. In the next blog we’ll discuss how the?Conformed Dimensions are used for each of these approaches.

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