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Data Governance Guiding Principles

05 May, 2016 | Anne Marie Smith, Ph.D. | Organization

Base your data governance program on a set of enduring guiding principles to ensure long-term success!

Introduction

Guiding principles are statements that direct the organization in the course of its operations, in all circumstances, regardless of changes in management or other impermanent things.  In a continuing program such as data governance, it is important to establish a set of points that will allow the organization to focus on the fundamental value of data management within the organization and how it will support its business goals.  As the data governance program grows and encounters challenges, having these guiding principles as reference and beacons will enable all stakeholders to retain the motivation to manage data as an organizational asset.

Data Governance Guiding Principles

  • Data is essential to making and enforcing decisions regarding an organization’s information assets for enterprise effectiveness
  • Developing and documenting a common vocabulary for data in easily accessible glossaries and repositories is an important function for a data management organization
  • Identifying the common data of the organization and managing it appropriately, according to industry standards and best practices is an essential function of any organization
  • Providing accessible, available, trusted data and the capabilities for decision support leading to analytical competence is necessary for a 21st century organization
  • Developing and maintaining the complete metadata definitions for all critical data to enable the business and IT staff to understand all aspects of all data captured, stored and used is an essential component of any data management initiative
  • Supporting the integrating of data from multiple sources with repeatable processes to enable re-use of existing data is an important task to be performed by staff from business areas and IT
  • Implementing data security practices and processes across the organization to ensure privacy, confidentiality, appropriate access and conformance to regulations is an essential part of any organization that is concerned with the management of data
  • Managing the information lifecycle that defines the stages of data through creation, usage, storage, archiving and purging to meet business needs, technical capabilities and legal requirements according to industry standards and best practices is a necessary activity for effective data management
  • Managing and measuring the quality of data throughout the organization as defined by accuracy, completeness, validity and timeliness will ensure the organization’s ability to trust its data and information assets
  • Recognizing content (unstructured data) as a valuable resource that can provide information context across the organization when managed according to best practices is a necessary companion activity to structured data governance and data management
  • Recognizing the need for continuing talent assessment and evaluation and for continuing professional development in all areas of enterprise data management is essential for successful organizations

 

Many organizations summarize the guiding principles into short phrases (“Delivering for Clients and Consumers,” “Focus on Performance and Delivery”, “Improving Business Value,” etc.).  Many organizations use the vision and mission statements as the starting points for writing the guiding principles, and often accompany these documents with a set of examples to demonstrate the uses of the principles.

Please note that these guiding principles are not goals.  Goals are a desired result that a person or a system envisions, plans and commits to achieve; a desired end-point in some sort of assumed development process.  Many people confuse the terms “goals,” “guiding principles,” “objectives,” etc…  Guiding Principles are not realized or achieved fully; they are a continually moving objective whose final result will never be completely accomplished.

Additionally, there are some general data governance behavioral approaches that all practitioners should adopt.  Drawn from the DAMA-DMBOK© and the Data Governance Institute, this list is considered to be a good reference for all those who are data governance or data management professionals:

  1. Integrity

Data Governance participants will practice integrity in their interactions.  They will be truthful and discuss openly all aspects of data-related issues and the consequences of actions and inactions.

  1. Transparency

Data Governance and Data Stewardship processes will exhibit transparency.  Through permanent documentation, it should be clear to all participants and auditors how, why, and when data-related decisions and controls were introduced into the processes.

  1. Auditability

Data-related decisions, processes, and controls subject to Data Governance will be auditable.  Permanent, approved, and comprehensive documentation will support compliance-based and operational auditing requirements.

  1. Accountability

Data Governance will define accountabilities for cross-functional data-related decisions, processes, and controls with permanent bodies such as the Executive Council and Data Governance Committee and all business data stewardship teams.

  1. Data Stewardship

Data Governance will define accountabilities for business data stewardship activities that are the responsibilities of individual contributors, as well as accountabilities for groups / teams of Business Data Stewards. Data Governance will develop a structure to manage the interactions among the variety of Business Data Stewards teams and other associated Data Governance-related organizations.

  1. Checks-and-Balances

Data Governance will define accountabilities to introduce checks-and-balances between business and technology teams, and between those who create/collect information, those who manage it, those who use it, and those who introduce standards and compliance requirements.

  1. Standardization

Data Governance will introduce and support standardization of enterprise data from business and technical perspectives.

  1. Change Management

Data Governance will support all Change Management activities for reference data values and the structure/use of master data and metadata.  Data Governance will support the management of quality for data and adhere to principles that address change management toward total quality management.

 

Conclusion

In the final analysis, having a set of guiding principles is an essential aspect of any successful data governance program.  Keeping them as an active part of the data governance initiative is just as important to success!

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