Data Ownership and NDB’s Data Tracing Method.

Increasingly, as data is generated from many more sources, the data being exploited by the business in not solely controlled within the business. This situation has long been typical in a scientific or engineering-based business, where key decisions are made from spreadsheets and aggregated models. There are lessons that can be shared.

Users creating and changing data are focused on their own function and meeting their own, often momentary, needs. User actions can help or hinder the longer-term quality, preservation and value of data. Strategically, it is often recognised that the data is owned (accountability for the content) by the creators and users and not the curators, IM or IT. In practice it is very difficult to develop the most effective working relationship between the owners and the curators. The label of ‘ownership’ itself is contentious.

How can we overcome this challenge and develop the relationship between the business and IM/IT that is required to better exploit data?

Barriers to effective ‘Data Ownership’:

• Business users don’t welcome additional responsibility,  
• Data and data management is seen as the domain of IM or IT
• The additional value from effective data ownership is not well understood,  
• Data Ownership is often implemented as part of Data Governance, not seen as a direct and practical initiative.

And as we found using NDB’s Data Tracing Method …Valuable data is being used for key decisions that is not in any way controlled or under management by IM or IT

… and the corollary ....IM or IT spend time engaged with data clean-ups or creating master data sets with data that will never be used.

NDB’s Data Tracing Method begins and ends with key decisions and traces the origin of the data used. The quality of the decision making will improve if the data being used is of higher quality because of the risks associated with the data.

·      Users are more engaged because when looking at the actual data used.  

·      Users are more engaged because it is the data that matters to them and they can see the activity is focused entirely on improving their data.

·      We are likely to understand the value of the decision, so we have a model for understanding the value of the data.

Engagement and relevance

By looking at the actual data being used for key decisions we are directly engaging business users with the challenge and working together on the solution. We are ensuring that activities in data management are business relevant and focused on business priorities.