Why “Data Ownership” Matters

In Understanding data ownership in the data lake Elizabeth Koumpan, Executive Architect at IBM, writes:

Depending on the organizational point of view, different ownership rules may apply in different situations to data. It is a tricky part when we deal with Data ownership while using external sources, especially if we use social data which is an essential element, as we build our cases for front office digitization, customer sensitive analysis and so on. While we deal with tremendous amount of social data which describes the interactions of people, moving this data around, changing it for our analysis, at the end have a difficult question – who owns this social data? Is this the real authentic data that was truly originated from a person and has some valid purpose? Or it is modified, changed and became fake or misleading, which if used and analyzed can lead us to unreliable and wrong decisions.

While the topic of the above paragraph is “social” data obtained from external sources, the issue of “ownership” is an important one that attaches to all types of data and metadata that are created, managed, or used within an organization. This includes the structured data as well, regardless of whether it’s tied to an external customer or to an internal process or system.

The term “ownership” implies authority and responsibility. We would like to think that the “owner” of the data (a) will be responsible for the data‘s timeliness and accuracy and (b) can be contacted when questions arise about the data or its meaning. Both are relevant for internal and external data as well as for structured and unstructured data.

As IBM‘s Koumpan suggests, things become complex in the real world when various types of external (and internal) data are gathered, transformed, analyzed, interpreted, and moved around. Even when a formal determination is made for responsibility for a “master” source for key data or metadata (i.e., data about the data), the practical value of the data in question may only be realized when the data are used to support a defined or evolving business process or system.