What is Data Governance by Design?

Data Governance by Design is the way of managing and maintaining a company’s applications and processes that guarantees overseeing the involved data throughout their entire life cycle, from requirements definition to execution management.

But first things first, so let us start with Data Governance.

It aims at ensuring authority and control in data asset management. In effect, it means establishing the ways a data pipeline operates and defining technical and business characteristics of the managed data. It is necessary to set the data semantics, the relationships between the data and their elaboration processes, as well as the functions in this or that way involved in their management. What we mean are thus Data Catalog, Business Glossary, Data Lineage, ownership. All these phenomena can be conveniently represented with a structured metadata model.

Data Quality: from semantics to ownership

If the represented object – a data field, a process, a data pipeline – is already present in the organization, it is necessary to reconstruct the metadata that describe the related governance characteristics. Often active intervention is needed to boost, for example, the determination of ownerships, definitions, quality supervision. In fact, Data Quality is one of the aspects that most often require intervention to reinforce control systems and revision of the operating modes. This occurs not only in terms of verification, but also of elaboration and accountability of control, execution and remediation processes.

Modern technologies allow to facilitate the process of reconstruction and reinforcement. However, in many cases the fact that the results are complete and maintained presents disadvantages. This has to do not only with the power of metadata capture tools, but also and above all with the technologies and the ways applications and processes included in the governance perimeter are implemented and deployed.

Data Governance by Design aims at overcoming these gaps for those data scopes and data-intensive processes that allow it since they are yet to be realized and implemented.

Data-intensive processes: the principles of Governance

Throughout the entire life cycle of a process or a software solution, it is necessary to guarantee the relevant Data Governance principles, i.e. ensure they are defined, formalized, implemented and activates. These principles are:

• semantics and ontology – what is the “official” meaning of the processed data? what are their mutual relations and relations with other entities?
• data architecture – what data in the enterprise information model relate to those managed in this process / application?
• Data Lineage – what path do the data follow along the process? What are the paths of the data managed by the application?
• Data Quality – what are the quality rules that ensure that the data can be employed for various uses?
• responsibility – who are the stakeholders involved in different ways in the process, in using the application?
• orchestration – how is the process elaborated? In which processes is the application involved? Should we anticipate automated steps to be synchronized to guarantee the overall correct Data Management throughout these processes?

These perspectives, these questions are also to be considered when we govern the data involved in an existing process or application. Yet it is much simpler and more efficient to consider (and manage in a structured metadata model) these aspects “by design” in the implementation phase. And, if needed, also “by default” throughout the entire life cycle.