In one of my prior posts, I briefly mentioned harmonization of reference data sets, which basically consisted of determining when two reference sets referred to the same conceptual domain and transforming the blending of the two data sets into a single conformed standard domain. In some cases this may be
If you’re involved in some way with data quality management then you will no doubt have had to deal with the completeness dimension. This is often one of the starting points for organisations tackling data quality because it is easily understood and (fairly) easy to assess. Conventional wisdom has teams
.@philsimon on those who minimize the importance of data.
In his book Proofiness: How You’re Being Fooled by the Numbers, Charles Seife coined the term disestimation, defining it as “the act of taking a number too literally, understating or ignoring the uncertainties that surround it. Disestimation imbues a number with more precision that it deserves, dressing a measurement up as absolute
There are really two questions about reference data lineage: what are the authoritative sources for reference data and what applications use enterprise reference data? The criticality of the question of authority for reference data sets is driven by the need for consistency of the reference values. In the absence of
Whilst it’s nice to imagine a world of perfect data quality the reality is that most organisations will be dealing with data quality defects on a daily basis. I’ve noticed a wide variation in the way organisations manage the life cycle of defects and nowhere is that more apparent in
@philsimon says that perfection is elusive.
In my previous post Sisyphus didn’t need a fitness tracker, I recommended that you only collect, measure and analyze big data if it helps you make a better decision or change your actions. Unfortunately, it’s difficult to know ahead of time which data will meet that criteria. We often, therefore, collect, measure and analyze
Last time we looked at some of the benefits to be derived form managing the metadata associated with reference data. Practically speaking, the first step in managing the metadata for reference data is to identify which reference data domains exist and document how those domains are used. That suggests taking
If we look at how most data quality initiatives start, they tend to follow a fairly common pattern: Data quality defects are observed by the business or technical community Business case for improvement is established Remedial improvements implemented Long-term monitoring and prevention recommended Move on to the next data landscape
