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Core Data Governance Principles for Every Organization

How can all data stakeholders within an organization be on the same page when addressing different data-related decisions?

Every organization has its own needs and requirements for data governance. Data Governance principles can help organizations establish a robust data framework for guiding decisions, managing data lifecycle, and aligning with their data goals. The focus of these principles may differ depending on the data goals of the organization, such as establishing data accountabilities or responsibilities or simply ensuring data quality.  

Continue reading to know more about universal data governance principles that collectively establish and define effective data governance. 

What is Data Governance, and why it matters?

Before moving to data governance principles, it is essential first to understand data governance and why it matters for organizations. 

In simple terms, data governance is a framework of procedures and policies, standards, processes, and metrics that govern how data within an organization is created, processed, controlled, distributed, stored, secured, and consumed. 

The data governance principles framework also helps to ensure that data governance policies align with the data goals of that organization and are compliant with data regulations. 

Common data goals of an organization include the following; 

  • Better decision-making.
  • Lower operational conflicts. 
  • Ensure transparency in data processes. 
  • Protect the needs of all data stakeholders. 
  • Build standardized and repeatable data processes. 
  • Lower costs and boost effectiveness via sustained coordination between different teams and data stakeholders. 
  • Outline a framework and approach to address data issues that data stakeholders and the staff can follow. 

The Size and Nature of Data are Changing

A data governance framework governed by a set of universal data governance principles is necessary because organizations today generate massive amounts of data and data types via new and advanced tech tools. 

In most organizations, a substantial percentage of the generated and collected data is unrefined and of low quality, and its actual value remains unleveraged. 

The primary reason why this happens is that organizations cannot ignore the need to ensure all their data is processed, distributed, and consumed by the right set of people – in the proper form and at the exact moment of need. 

Most organizations fail to ensure transparency in managing their data lifecycle and regulatory compliance. 

These factors have a financial downside since poor data quality or unleveraged data ultimately impacts decision-making and can lead to legal complications.  

The only way to effectively address these issues is by creating a practical data governance framework that can help organizations realize the full potential of their data and generate more data value. 

8 Core Data Governance Principles 

While the data governance framework within every organization may differ in some ways based on the goals of that organization, the general framework on which any data governance model is created is based on certain universal principles. 

Consider the following data governance principles. 

Integrity

Integrity is one of the most important data governance principles. Data can only deliver value when all the data stakeholders are honest in upholding the organization’s data governance goals and values and in the processes that are used for data consumption. Only then will data integrity be maintained.  

All the data stakeholders should be truthful and transparent with their decision-making vis-à-vis the actions they take, the impact of these actions, and the controls or constraints in place. 

  • Transparency

Organizations must ensure that all the data stakeholders, teams, and auditors are aware of controls and the audit trail of how and when data-related decisions. Controls prevent future conflicts. 

Of course, there will be aspects of data governance that cannot be transparent because of certain security and confidentiality needs. However, it is important for all the right people, with the necessary clearance, to identify and discover all that is happening through the entire data lifecycle. 

  • Accountability

 Situations when “no one is accountable” or when “far too many people/groups are accountable” – can lead to lead to chaos or “zero” accountability. Therefore, a clear path of accountabilities is critical. An essential aspect of effective data governance is defining accountabilities for all data-related processes, controls, and cross-functional data-related decisions.  

  • Stewardship

Within an organization, data stewards are responsible for how data is both stored and utilized at all times. While doing this, they need to ensure that the laid guidelines and best practices are followed for data storage and utilization.

This can only happen when accountabilities for stewardship activities in data governance are clearly defined. In addition, the responsibilities of individual contributors and groups of data stewards should be clearly defined and maintained.  

  • Auditability

All the data-related decisions, processes, and controls should be auditable with the necessary support documentation to display complete compliance. 

At present, amid the increased data privacy scrutiny, organizations have to ensure that even if compliance is not a primary data governance program goal, the processes, decisions, and controls are ready for review and auditable at all times. So organizations should maintain necessary documentation where necessary. 

  • Introduction of Appropriate Checks-and-Balances

Clearly defined accountabilities in data governance require the introduction of necessary checks and balances. 

It becomes necessary to establish a chain of checks and balances between business and technology teams, other data stakeholders such as data collectors and creators, and also between those who manage the data and those who use it, and those responsible for introducing standards and compliance protocols.

  • Standardization of Data

 Within any organization, enterprise data is used by multiple teams using multiple applications, leading to multiple data formats where the data format used by one team might not be compatible with the application used by another team.

To prevent this from happening, standardization of data is necessary. 

Organizations should maintain clearly defined guidelines and rules that support the standardization of the data. 

For example, there should be clear rules for data accessibility, data security, privacy, etc. 

  • Change Management

There are times and instances when data might need to be changed. 

However, this can increase the risk of tainting or corrupting the data. For this reason, organizations should pay close attention to how their data changes. 

Data governance can help support proactive and reactive change management activities, including reference values and the use and structure of both master data and metadata. 

In Conclusion

Managing large data sets and different data types within an organization can be challenging unless the organization has a robust framework of data governance principles. 

Data governance guiding principles can help an organization lay down clearly defined best practices for data generation, distribution, consumption, security, and storage. In addition, data governance principles can also help establish complete data compliance.