In 2009 we introduced the concept of data discovery, as distinct from data profiling where we defined data discovery as “the discovery of relationships between data elements, regardless of where the data is stored”. This distinction is important because data discovery has far wider application than just data quality. For example, data discovery is important when implementing MDM (master data management), it can be used to complement data modelling tools, it may be employed for business intelligence purposes, and has a significant role to play in supporting data migrations, data archival and data governance, amongst other areas of application. At that time there were data profiling tools that did a little of this, but not much, while there were data discovery tools that could discover relationships but did not do much in the way of statistical analysis and monitoring to support data quality initiatives.
That positioned has changed. Since we last reported on the data profiling and discovery markets a significant shift has taken place. It is apparent that many traditional data profiling vendors have been adding data discovery capabilities to their products while suppliers of data discovery tools have added statistical and profiling functions to their tools. While some vendors are clearly further down this path than others, you might therefore conclude that data profiling and discovery should be re-merged as a single market sector. However, that is not currently the case.