Choosing a Data-Governance Framework for Your Organization
Data governance refers to the process of managing enterprise data with the aim of making data more accessible, reliable, usable, secure, and compliant across an organization. It is a critical feature of organizational data management and promotes better data quality and data democratization.
A well-planned data-governance framework is fundamental for any data-driven organization that aims to harness the business value of its data and downstream capabilities that drive robust decision-making. It covers and details best practices for data processes, roles, policies, standards, and metrics.
Naturally, data-governance frameworks vary from one organization to the next. Here are a few examples of strong data-governance frameworks recommended at companies like PWC, Hubspot, and ING.
However, there are a set of commonly accepted best practices, as listed below:
For more examples of recommended best practices, check out these data-governance tips from Tableau and Snowflake.
In this article, you’ll learn more about data-governance frameworks and their essential components, exploring use cases and best practices for choosing a data-governance framework for your organization.
Without effective data governance, organization’s data science teams fail to extract the full value of their data. Weak data governance leads to poor data practices and decision-making, causing organizations to lose their competitive edge in an increasingly data-driven corporate environment.
Crucially, poor governance can also impair compliance with regulatory standards like GDPR, HIPAA, SOX, CCPA, and the like, which can have massive consequences for businesses. Hefty fines for violating such laws can cause a dent in a company’s bottom line. For instance, in 2021, Amazon received a whopping GDPR fine of $877 million.
Strong data governance ensures that data is readily available, reliable, consistent, and of high quality to empower businesses to create value from the data. It encompasses processes, people, organizational bodies, and a set of policies that all work together to determine best practices of managing data.
The benefits of data governance are manifold, some of which include the following:
Investing in a robust data-governance framework yields significant returns and helps accelerate an organization’s digital- and data-transformation journey.
There are several foundations of a modern data-governance framework. The primary focus areas include data quality, accessibility, security, and compliance. However, the success of a data-governance framework cannot be realized until people, processes, and technology are combined effectively.
Designing an effective data-governance framework also includes creating a clear set of organizational roles, responsibilities, and data stakeholders for enforcing and managing the governance policy. In this section, you’ll read about the core aspects of building an optimal data-governance framework.
Data accuracy is a central pillar of data quality and refers to error-free and reliable information. Inaccurate data is often the result of poor data entry practices, poor regulation of data accessibility, and poor data quality standards. It is critical to improve data accuracy standards as clean, consolidated, and accurate data can lead to a two times improvement on the return on investment.
There are many ways to test and improve existing data accuracy standards. A real-time data quality audit can identify issues like duplicates, missing values, incomplete information, data being stored in multiple locations, and so on. Some of these common issues can be fixed through tools that automate data quality and accuracy checks, while other issues require manual intervention by data teams.
Data relevance refers to whether the data is pertinent and applicable for deriving business value (i.e., whether it is fit for the purpose it was originally intended for).
