Data Knowledge Democratization

Data Governance initiatives often struggle to gain real business traction and bring about noticeable change. A lack of executive sponsorship is often cited as the main reason why the data governance effort is not able to drive through a change in corporate culture. However there are other factors at play.

Doing better work around data on an enterprise-based scale requires the engagement of staff across multiple disciplines and functions. Most large organisations are faced with a highly complex data landscape, and reducing that complexity requires the knowledge and collaboration of local and central staff across a number of areas. If we could achieve a leaner and meaner data landscape by just involving data and IT people, we would be there already.

To date, knowledge about data often remains locked away in specialist modelling and architecture tools as well as an eclectic mix of Visio and Excel artifacts. This inhibits any broad collaboration, as not only is specialist knowledge required to interact with those tools, the views captured within do not easily connect up to form an integrated and meaningful whole.

The knowledge about the firm’s data desperately needs to be shared to empower anyone and everyone in the firm to be a well informed data citizen and enable collaboration across disciplines and functional silos. It needs to be democratized both in terms of providing an accessible view irrespective of the user’s skill-set and in providing an interconnected view across the different perspectives each of the disciplines or functions have. Knowledge Democratization is a terrific driver for Data Governance adoption across the organization.

Data definitions are a good starting point, but in order for the IT practitioners to be able to contribute, those definitions should be linked up to the technical metadata. Similarly if one wants to understand data usage the process viewpoint needs to be linked in as well. The compliance and regulatory viewpoints are equally important, as are the more conventional data quality and data lineage viewpoints. How about providing visibility of what items are about to be affected by which change project?

With each new facet being connected into the knowledge base, a new community of experts and stakeholders is brought into the governance model while growing the understanding of data and broadening the context of its use.

It also makes the conversation around data more objective and meaningful to the community, as they see things together, from the perspective of the business outcomes that data supports.

Data governance is the people component within enterprise data management. As such it should first and foremost focus on collaboration by connecting people and knowledge. When it does this it can transcend any single group’s capacity to imagine, understand and unleash a new way of working around data when being aligned is not the exception, but the norm.

Republished from a LinkedIn post with permission of Diaku.

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Patrick Dewald

Patrick Dewald

Patrick Dewald is a Data Governance architect and founding partner in Diaku, the maker of the Diaku Axon Data Governance Software platform. Patrick has a wealth of experience designing Master Data Management and Data Governance solutions for financial institutions. Patrick has been heading up Data Governance initiatives, designing and implementing group-wide data services for the best part of 15 years. Patrick is recognized by its peers as a thought leader in the field of Data Governance.

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