From data catalogs towards knowledge catalogs – The demand for business metadata management

In the constantly evolving and changing data landscape, organizations are increasingly recognizing the need for data catalogs to maintain a comprehensive understanding. This shift is driven by the growing demand for better data understanding, enhanced decision-making, and more efficient utilization of data.

At the moment data catalogs are heavily focused on technical and operational metadata. This means that the priority is to get technical and usage related information from reports, datasets, data assets and data products documented and under proper data governance. When it comes to managing technical and operational metadata within a data catalog, we are achieving a plateau of productivity, but in the business metadata, we are just getting started in understanding the great opportunities it provides for organizations.

Currently, business metadata is often seen only as a business glossary and in way too many cases it only contains the definitions for data. This is due to the fact that a data catalog is often seen as a tool for data people and thus, everything in it revolves around data. But when we really start to look at what business metadata actually is, it is a much broader term containing metadata from the whole business ecosystem: processes, capabilities, KPI’s, organization structures, IT systems, both digital and physical assets, business language, etc. These are not really data related, but more business related. And this is the reason why they are not really existing in data catalogs. There are other tools managing these aspects such as EA and BPM tools as well as different portfolios, but they are all usually isolated and thus, the full potential is not harnessed. 

Imagine the possibilities if we could connect this business metadata to the data landscape. As there is a lot of business metadata existing, linking in to technical metadata would enable building a digital twin of an organization. It would help us to understand and optimize digital processes better, simulate different scenarios and establish data governance with for example finding business ownership for certain data.

Achieving such fundamental change of a mindset requires a significant change on both organizational and technological levels. The problem of maintaining the business metadata is that it cannot be automated in a similar way as is being done for technical metadata. As it is manual work and the actual knowledge lies in the business, the accountability for maintaining the solution should also be on the business side. And this leads to the second challenge related to technologies. In order for business to maintain e.g. a knowledge graph containing business metadata, there is a need for a very easy to use and intuitive UI. The current data catalogs are still rather technical even though they are slowly moving towards more business-oriented UX.

Nobody knows how things in business metadata management will evolve within the next few years, but it is pretty certain that themes like ontologies, knowledge graphs, semantic interoperability and enterprise modeling will have an increasing amount of traction.