Business today is fueled by a constantly expanding, increasingly complex body of data. And that means data governance — managing the data produced, acquired, and used by a company — has become a critical function.
A comprehensive data governance strategy is essential to addressing data quality, security, privacy, and access — all of which are key to maximizing the value of a company’s data assets.
The building blocks of a data governance strategy include:
- a strong team with well-defined roles
- a robust policy framework
- a culture that prizes high-quality data
Here’s how they all come together to make data governance a key differentiator for your business.
What is Data Governance?
Data governance is the principled management of the entire lifecycle of data — from acquisition to disposal, and everything in between. It involves every activity a business takes to ensure the security, privacy, accuracy, availability, and usability of data.
Effective data governance is critical for:
- Obtaining the right data assets to support your goals
- Avoiding the cost and risk associated with bad data
- Realizing positive ROI on data-enabled processes, like sales and marketing
- Securing your data assets as a main competitive advantage
Main Benefits of Data Governance
Improved Quality
Data policies and processes that help companies organize, manage, and use data more cost-effectively also positively impact data quality. By establishing data governance procedures, companies can detect and resolve quality-related problems faster, improving their bottom line.
Better Visibility
Comprehensive data governance brings greater insight and visibility to your entire dataset. This can increase awareness of trends, which makes it easier to establish priorities and boost your competitive edge.
Greater visibility also contributes to your business culture. Effective data governance requires buy-in, collaboration, and participation from all levels of your company.
Better Compliance
Good data governance improves compliance with regulatory requirements. To govern data correctly includes assessing and accommodating data laws and regulations like the General Data Protection Regulation (GDPR).
Common Challenges With Data Governance
Defining and aligning
Even though data acts as a lifeblood that touches virtually every part of the enterprise, businesses are often unsure of the value of data governance. The universal need and implementation of data makes it hard to pinpoint exactly who is responsible for data governance.
Defining data governance is challenging due to a mix of key factors:
- Varied understanding of governance information
- Lack of leadership
- Limited resources
- Low data literacy across the business
- A lack of alignment and transparency on common goals
Silos
Siloed data prevents the free flow of information. This makes alignment and collaboration on data governance initiatives a challenge. Siloed data often means hidden data that can’t be managed, updated, or have its impact measured appropriately.
Siloed data limits the ability to make the right data-driven decisions, follow through with governance policy, and access data for stewardship purposes.
Monitoring
According to Gartner, 42% of data and analytics leaders aren’t assessing, monitoring, or measuring their data governance efforts.
Without robust data and analytics tools, data teams miss out on making better, faster data-driven decisions. For example, if you monitor data quality over time, you can check the effectiveness of data governance policies as they are introduced.
“If a governance policy is introduced at a certain baseline and the quality drops without other changes, then one can infer the governance process is flawed,” says Kevin Burr, director of product management at ZoomInfo.
Security
Ensuring data security is a large and complex task. It’s an ongoing challenge for data stewards, who are responsible for checking and confirming that data is used with appropriate security measures.
Privacy
Companies must comply with complex data privacy regulations. Data governance teams are responsible for the security, privacy, and proper use of all the collected data.
Changes
As data evolves over time, data governance teams must find ways to adapt processes to maintain the accuracy and reliability of data. This requires careful planning and execution to ensure the right data is delivered to the right teams that need it. Without a flexible approach to data governance, change management becomes increasingly difficult.
Data Governance Team Roles
Running a comprehensive data governance practice requires effort from several team members. Effective data governance typically includes the following key roles:
Data steward
The data steward is responsible for setting and enforcing data governance policies, as well as ensuring that data is managed correctly and consistently across the business. They’re also responsible for establishing data quality standards, ensuring that data is stored securely, and monitoring data usage.
Data architect
Data architects oversee the design and implementation of data systems. Data models are developed, data mappings are created, and data warehouses are designed within this role. Data architects also work with data stewards to ensure compliance with data governance policies.
Data analyst
Analyzing and interpreting data to discover insights and understand trends are some of the responsibilities of a data analyst. This role also includes tasks like data cleansing, data mining, and developing predictive models.
Data manager
The data manager’s job includes creating and maintaining data warehouses, ensuring data integrity, and ensuring data availability. They also interface with the data steward to ensure everything is managed in accordance with data governance policies.
Business analyst
It falls upon the business analyst to understand the business requirements of data so they can then translate those into data governance standards. Identifying gaps in data governance and documenting the process for data governance also make up a part of their job.
How Does Data Quality Affect Data Governance?
Data quality and data governance work together to ensure sound data management — an equally important part of stewarding and measuring data assets.
“Data quality is about the data and metadata itself and its completeness and accuracy. Data governance is more about access and change control,” Burr says.
A lack of high-quality data that is trustworthy undermines data governance processes — the absence of one makes it difficult for the other to function
According to Burr, good governance ensures access and management of the data are controlled, monitored, and understood.
“If you can’t measure the quality of a dataset — or you can, and know it’s low quality — no matter how much governance you throw at it, you’re still left with low-quality in, low-quality out,” says Burr.
Data quality is interconnected with the success of data governance in a variety of ways:
- Measuring contact data quality and how it affects customer retention and customer lifetime value
- How your data ecosystem directly contributes to revenue based on accurate data usage, ingestion, and compliance metrics
- How well your data is delivered across your tech stack to drive growth
Together, the quality of data and a flexible data governance strategy create a strong foundation for accelerated growth.
Related: How to Evaluate and Measure the Quality of Your Data
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The Differences Between Data Governance and Data Management
The terms “data governance” and “data management” are often used interchangeably, but there are distinct differences between the two.
Data governance involves overseeing standards around the availability, usability, integrity, and security of the data used and produced within a company. Proper data governance encompasses the processes, roles, standards, frameworks, and metrics needed to ensure the effective and efficient use of data.
Data management is the practice or set of activities involved in collecting, storing, and processing data to support day-to-day business needs. Comprehensive data management involves acquiring data from various sources, storing it in a database management system, running it through a data normalization process, and ensuring that it’s easy to access and analyze across teams.
In other words, data governance is the higher-level framework that provides the guiding principles for data management, whereas data management is the actual “boots on the ground” daily maintenance of that same data to drive go-to-market (GTM) execution and uncover business insights.
How To Create an Effective Data Governance Framework
To build an effective data governance framework today, data leaders must start by establishing a company culture that cares for data and treats it as a competitive advantage.
“Responding to varying levels of uncertainty in today’s world requires speed and agility, and traditional approaches to data governance are becoming obsolete,” says Saul Judah, VP analyst at Gartner.
To build a modern data governance framework, consider the following components:
1. Data governance policies
It’s crucial to define how data is used and managed within a company, who is responsible for data stewardship, how decisions are made about its usage, and how users are expected to interact with that data.
2. Data quality standards
Establish a set of standards related to accuracy, completeness, and consistency that ensure your data is of the highest quality. This is where data orchestration becomes a key component.
3. Data security
The specific security needs of a company will vary depending on the type of data being protected, as well as the team’s specific business goals. However, there are some key elements that are essential for any effective data security strategy:
- Data classification: Allows the company to determine which data is sensitive and needs to be protected.
- Access control: Ensure that only authorized personnel have access to sensitive information.
- Encryption: Securely store and transmit data, to ensure that only the intended personnel access it.
- Security events and alerts: Alert and notify responsible parties in the event of a violation.
- Auditing and testing: Perform regular auditing and testing to ensure systems remain effective and up to date.
4. Data integration and management
Crafting a data governance framework requires making sure data is properly integrated across systems and managed in a way that meets the quality and security standards set by the organization.
In other words, it’s important to be able to show how the data you buy directly impacts performance.
“Whether it’s new revenue, cost savings, efficiencies, whatever. I need to be able to correlate results to contributory assets to show I’m going to have ROI on my data,” says Burr.
RELATED RESOURCE: 8 Ways Teams Use Data as a Service for Go-to-Market Success Download the eBook
5. Data culture
Data governance initiatives thrive in an environment where data is valued. Data leaders must connect everyone in the organization to training that helps them understand data and the role it plays in the continued success of the business.
6. Regular data governance reviews
Set up a regular review of the data governance strategy and processes to ensure they align with the company’s objectives — including assurances that the data remains high-quality and secure.
The Role of Data Governance in a Data-Driven Business
Data governance provides a sturdy foundation for business leaders to make data-driven decisions and build effective GTM strategies.
“Ultimately, any company’s growth is made up of the success of people, process, and technology,” Burr says.
By establishing clear policies, procedures, and protocols for data usage, data-driven organizations can ensure that data is secure and used appropriately, enabling the organization to reach its goals.