Gartner defines data governance as the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics. Data governance is vital to ensure that your data is consistent and trustworthy and doesn’t get misused.
One of the first steps you’ll need to get started is to create a data governance framework. This will define policies, procedures, and practices that your organization should put in place to manage and protect its data. Establishing a well-defined framework is essential for the success of your data governance initiatives.
Data Governance Framework
There are various data governance frameworks; each has its own pillars and associated responsibilities. I’m using a five-pillar framework, as shown in Figure 1 that includes data ownership, data stewardship and management, data quality, data privacy, compliance, and data protection, as outlined below. I prefer this framework since it’s simple and includes a pillar dedicated solely to privacy and compliance, an area that is increasing in importance to help businesses avoid expensive non-compliance fines, improve their brand image, and gain customer trust.
#1. Data Ownership
- Assigning data assets and accountability to specific individuals or roles
- Defining data quality, data security, compliance, data access, and data usage policies and processes
- Identifying data lifecycle requirements (including creation, storage, usage, archiving, and disposal of data)
#2. Data Stewardship and Management
- Overseeing day-to-day data management and oversight of data, including data quality, metadata management (data definitions, data dictionaries, data catalogs, data lineage), and compliance
- Collaborating with data owners and data users to ensure that data is used effectively and responsibly
#3. Data Quality
- Creating data management practices to ensure data accuracy, completeness, consistency, and reliability
- Defining data quality standards, data profiling, data monitoring, data cleansing, and data validation processes
#4. Data Privacy and Compliance
- Defining and enforcing standards and guidelines for data classification, data access, and data retention in accordance with legal and regulatory requirements.
- Ensuring implementation of compliance measures for relevant data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)
#5. Data Protection
- Protecting data from unauthorized access, breaches, and cyber threats
- Maintaining security to support data confidentiality, integrity, and availability
You should consider these five pillars as a guide to developing your own data governance framework. They point out various aspects of data management that your organization should address. However, you will likely need to tailor these to suit your organization’s specific needs based on size of business, industry, regulatory environments, available expertise, priorities, and many other factors.
In addition, a data governance framework is just one checkbox for effective data governance. Building a data governance culture within your organization is crucial for a successful implementation of your framework. Your business may also need invest in data governance software and tools to help automate framework execution as well as training in technical, analytical, communication, and organizational skills and competencies to meet multifaceted data governance demands.
You’ll also need a data platform that offers the right underpinning for your data governance framework. The Actian Data Platform’s modern architecture supports policies, procedures, and best practices for data ownership, data stewardship, data quality, data privacy, compliance, and security, making it easier to implement and scale data governance. Our data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.