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Data Governance and Business Process Alignment

Data does not exist in a vacuum.  Therefore, data governance should not be practiced alone.  Remember to include the processes that affect and are affected by data for a robust data governance program

Governance is defined by Merriam-Webster as “the continuous exercise of authority over and the performance of functions for an organization,” so “data governance” would seem to mean the exercise of control over data.  However, it is not that simple, data does not exist in a vacuum; business processes act on, change and manipulate data.  Therefore, discussing data governance organizations must include business processes in the scope of items to be governed. Successful enterprise governance requires identifying the root causes that impede business effectiveness, implementing governance over business processes as well as over data.

Include Business Processes in Data Governance

Include business processes– how can data governance include business processes when the governance bodies (data governance council and data stewardship teams) have so much to focus on: meta data, data values, data quality, data security, etc…?  Once again, data does not exist in a vacuum, since processes create and manage the data and metadata – they are inextricably linked together.  By including a focus on process as well as data, the organization and the data governance effort can start to reduce the silo approach to interactions between business and technologies.  Adopting this holistic view of data governance with attention to business processes as well as to data can give an organization an advantage in managing the totality of the environment it has created.

In a properly implemented data governance organization, there are intersection points across business operations.  Data governance offers the potential to give the business an understanding of the data it needs and how that data is used, focusing on the business operations versus focusing on IT applications.

Challenges to Incorporating Business Processes in Data Governance

There are several challenges to incorporating business processes in a governance effort.  One is that the data governance formation team (data governance program team and the data governance council) must recognize that processes are part of the data governance landscape.  Since many business processes have developed in an ad-hoc fashion, it may be difficult to include management and control of business processes at an enterprise level.  The data governance council must accept this challenge and include all fundamental business processes in the data governance program.  Why?  Once again, data does not exist in a vacuum; processes create and manage that data!

Another challenge to including business processes is the need to identify stewards who are knowledgeable about both data and process for their subject areas.  “Knowledgeable” means that they understand what the current state of the business processes and data are, what the data actually means and how it is used, and what processes are correct and which ones should be changed.

Identifying the right data stewards may lead to a significant elevation of influence and authority for those individuals who can answer the questions about the data and the processes.  Discovering this set of knowledgeable people can extend knowledge sharing, continuous improvement and consensus across the organization – for both data and process.  Frequently, poor data quality is the result of incorrect or broken business processes, and correcting / improving business processes often leads to improved data quality and to a deeper understanding of the rightbusiness process.

Approaches to Resolve Data – Process Issues for Governing Organizational Information

One way to discover the alignment of data and the processes that create and manage it is to model the current and desired business processes and develop metadata (definitions, common terms, etc.) for the processes.  A business process model facilitates the alignment of business specifications with the data the process needs.  A shared model (not siloed) can help to keep the process and its data synchronized and reusable across the organization.  Although this can be a major effort, similar to the development of an enterprise data model if taken to a significant detail, data governance programs find that the benefits to high-level business process modeling far outweigh the investment of time and resources.  

A robust data governance initiative can establish common process architecture, modeling and implementation approaches for the fundamental business processes and the data needed for these processes, leading to the stewardship of both the data and processes important to the organization.

Conclusion

In high-performing organizations that value data and information as significant resources, governance applies to both data and process.  Include the business processes that support the data to create a more robust and holistic governance approach for the enterprise.

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Anne Marie Smith, Ph.D., CDMP

Anne Marie Smith, Ph.D., CDMP is an internationally recognized expert in the fields of enterprise data management, data governance, enterprise data architecture and data warehousing. Dr. Smith is VP of Education and Chief Methodologist of Enterprise Warehousing Solutions, Inc. (EWS), a Chicago-based enterprise data management consultancy dedicated to providing clients with best-in-class solutions. Author of numerous articles and a Certified Data Management Professional (CDMP), Dr. Smith is also a well-known speaker in her areas of expertise at conferences and symposia.

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