Understand the principles of data governance, and how to create a secure data governance framework for your organization.
What is Data Governance?
Data governance is the process and methodology for establishing how data can be accessed, used, and secured within an organization. Data governance encompasses all the policies, processes, technology, individuals, and departments needed to ensure data is handled safely and properly.
What are the Benefits of Data Governance?
Data governance is a central component of your overall data management strategy. It ensures your company is using quality data that can be used to make data-driven decisions. With accurate data, you can be confident that data being used across the organization is clean, formatted, and in compliance with international data privacy regulations.
Having a data governance framework also reduces the complexity of audits, and streamlines waste by making data sprawl more manageable. This allows business operations to be more effective, with accurate data easily accessible to drive decision making.
What is a Data Governance Framework?
A data governance framework serves as the foundation that supports your data management strategy and compliance efforts. It will consist of a set of rules and processes for collecting, storing, and using data. Without a proper data governance program and framework, data can be fragmented, lacking in accuracy, and out of compliance with data privacy regulations.
First, your company should deploy a data model representing data relationships that can be shared with designers, developers, data scientists, and others. Your data governance framework can then be layered on top of the model to define rules, roles, processes, and responsibilities.
A data governance framework may include:
- Overall organizational structure, including the roles and responsibilities needed for enterprise data governance.
- Standards and policies that define who can use specific datasets, and how data can be used.
- Technology infrastructure, including the hardware and software needed to collect, store, and manage enterprise data.
- Metrics that track the implementation and results of enterprise data governance efforts.
- Data stewardship to ensure data is accurate, consistent, and compliant with regulations.
- Data quality management to ensure data is free of errors.
Data Governance Framework Examples
The following are a few examples of some data governance frameworks to help you get started:
- Designing Data Governance That Delivers Value, McKinsey, 2020
- Global and Industry Frameworks for Data Governance, PwC, 2019
- The Path to Modern Data Governance, Eckerson, 2019
What are Some Data Governance Challenges?
Some of the biggest challenges when implementing a data governance program across your enterprise are going to be centered around organizational change. Data cuts across all departments in a company. In turn, data governance programs will require close collaboration between teams that would not normally work together, along with buy-in from leadership, starting with the C-suite and business leaders.
Another big challenge for data governance is all the new sources of unstructured and semi-structured data coming in from new channels, like IoT devices and mobile. Many brands will need a data management solution like a customer data platform (CDP) to ingest and consolidate data accurately, so that data governance standards can be applied.
What Roles are Responsible for Data Governance?
Roles and responsibilities play a big part of a company’s data governance strategy, and need to be defined clearly. There will be both dedicated and shared responsibilities across the enterprise. The roles and teams you will need will depend on your business needs and capabilities.
Some data governance roles may include:
- Data Governance Steering Committee: A group of stakeholders who set the overall governance policies that the rest of the company will follow.
- Chief Data Officer: A senior executive responsible for enterprise data strategy and data governance.
- Data Stewards: Employees who make sure all policies and procedures regarding data sets are met.
- Data Operators: Employees in charge of the lifecycle of each data set.
- Data Owners: Employees that manage data at the record level.
A broader cultural shift may also be needed to establish data as a priority for business growth and innovation.
What is Data Governance Technology?
There are plenty of tools for managing data, data governance, and overall data strategy. The tools and technologies you deploy will depend on your goals, your industry, and your customers. Data governance tools can help companies automate some aspects of data governance. They can also be used for data mapping, data catalogs, workflow management, and documentation. Some tools may be stand-alone, or are incorporated into other data management platforms, like a CDP.
Here are some examples of features you should be looking for in a data governance tool:
- Master Data Management (MDM). A data governance tool should be able to track data management overall, including data quality, data rules, and data configuration. This controls the data lifecycle and documents metadata, which improves cataloging.
- Data cataloging. Data cataloging features are pretty standard for data governance technology. Look for the ability to find, gather, and organize data while applying categories and tags. This will make data much easier to discover.
- Data ownership and stewardship. These tools allow data owners and data stewards to manage data and keep it accurate and consistent.
- Policy controls. Policy controls allow you to manage and configure data policies.
- Data visualization. Data visualization tools give you the ability to see data relationships in a variety of graphical treatments.
- Standards and definitions. For governance to be used across an organization, everyone must be aligned on the terms and language used to communicate data-related issues.
- Compliance. All data governance tools should help maintain compliance with regulations like the GDPR and CCPA.
Building a Data Governance Framework
Companies must have a complete understanding of their entire data supply chain to govern data properly. Knowing where your data comes from, who owns it, how it’s being used, and where it gets stored is critical to developing an effective data governance strategy.
Data has to be relevant, accurate, high-quality and trustworthy for it to serve as a useful asset to your company. And, it has to be centralized so it can be managed for regulatory compliance. In other words, your data needs to have integrity.
Centralized data can be used to develop better products, make customer service more efficient, and reduce complexity and waste. With the right data governance best practices, you can democratize data so it can be leveraged across the enterprise.