What is data governance?
Data governance refers to all the actions companies take to ensure data is reliable, accurate, and accessible. It outlines the steps companies must take, the procedures they must adhere to, and the technology that will support them throughout the data life cycle. It establishes internal guidelines or policies regulating how data is collected, handled, analyzed, and destroyed.
Data governance is a critical element of any organization's overall governance framework because it ensures that data is managed in a way that supports the organization's strategic objectives.
It regulates who has access to what data types and which are governed. Many organizations use data governance software to ensure the availability, usability, and integrity of data. Another aspect of data governance is compliance with external standards established by business associations, governmental organizations, and other stakeholders.
What is a data governance framework?
A data governance framework is a model that serves as the basis for data strategy and compliance. The data model explains the data flow, such as inputs, outputs, and storage parameters. The model overlays rules and responsibilities that define how the data flows are managed and controlled.
The model is a blueprint of how data governance operates in a specific business. The governance framework is specific to each business or organization. It reflects the specifics of data systems, activities and responsibilities, and legal and industry standards.
However, certain things are universal within the data governance framework.
- Data scope covers master, transactional, and operational data.
- Organizational structure and corporate hierarchy consider roles and responsibilities between account owners and business teams.
- Data standards and policies are guideposts that outline the expected outcome.
- Oversight and metrics parameters to measure the efficiency of a strategy.
Data governance benefits
Data governance frameworks offer various benefits to the organization using them.
- Quicker and better decisions. All company users can access the required data to reach and serve customers, enhance goods and services, and take advantage of possibilities to generate additional revenue.
- Enhance cost regulation. One can manage their resources better with data governance. Employees or individuals can avoid overspending on expensive technology and the associated maintenance costs by removing data duplication brought on by information silos.
- Improved regulatory compliance. Due to the complex legal environment, establishing strong data governance policies is becoming crucial for enterprises. Businesses can proactively prepare for new requirements while avoiding risks related to noncompliance.
- Gain the confidence of clients and suppliers. Being in auditable compliance with both internal and external data policies means that customers and partners feel confident doing business as they know their sensitive information is protected.
- Easier to control risk. Strong governance means sensitive data is not exposed to people or systems without appropriate authorization or data breaches from insiders or outsiders.
- Enables more people to access data. Strong data governance enables more personnel to access data and ensures that data democratization will not harm the business.
Elements of data governance
Data is a critical asset for any organization, and effective data governance is essential for managing and protecting it. Below are a few elements that ensure effective data governance in a company.
- Processes and documentation. Processes should be enforced through regular training and motivational rewards.
- Data integrity. According to the data governance model and framework, considerations for data integrity must be incorporated into processes.
- Audits and quality assurance. Companies must consistently examine the data validity in all procedures to ensure compliance.
Data governance best practices
Implementing effective data governance practices is crucial to maintain the quality and reliability of data throughout its lifecycle. Below are the basic steps to begin one’s journey in data governance.
- Determine a project. The first data governance initiative is critical as it allows expansion into an enterprise-wide program. Someone new to data governance must be able to demonstrate how it will benefit the company. One must provide a concrete return on investment (ROI), or at the very least, a return on effort, within a reasonable amount of time. Make it as exciting a project as possible for high management.
- Create targets. Figure out what the project should accomplish. More governance initiatives fail as a result of unclear objectives or misaligned expectations. For example, teams can aim to accomplish more effective regulatory compliance (including risk reduction and penalty avoidance) or consistent use of trusted data across the company to guide every tactical and strategic decision.
- Select the suitable staff and deploy them properly. Several people are involved in data governance programs. Even if a company has a small data governance team, the project will affect everyone who relies on data, such as partners, customers, and workers. Many will have opinions, and some will loudly express them. Accept their enthusiasm, but make sure to manage it. Utilize a responsible, accountable, consulted, and informed (RACI) style matrix to assign responsibilities. A RACI Matrix is a document project management teams use to identify which individuals or groups are responsible for a project’s successful completion. This guarantees that everyone knows their specific tasks and that the appropriate individuals provide input and approvals at the appropriate times.
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Define the procedures. Data governance teams require clearly defined, repeatable procedures planned for the actual challenges of the work ahead. Four fundamental procedures support every data governance program. The first is to identify and comprehend the data that needs to be governed. Next, define and record data definitions, policies, standards, and procedures. From there, apply or operationalize stewardship, business rules, and data governance principles. And finally, track and measure to analyze the effectiveness of data governance activities.
- Pick the right technology. Regulations, internal data initiatives, and new threats are constantly emerging. Businesses need a technology platform that provides value today and can adapt and develop over time. For example, data governance technology must have the capacity to meet all essential demands, including data cataloging, data stewardship, data quality, data sharing, and democratization, to develop and scale as business needs change.
Data governance vs. data management
Data governance identifies essential information across organizations, ensuring high quality while providing value to the business. Data management is a range of technologies that implements numerous corporate policies and regulations while supporting the informational and regulatory needs of customers, shareholders, and other stakeholders.
Data governance is a part of overall data management. Data governance that is not executed is just documentation.
In simple terms, data governance defines guidelines and procedures for data, whereas data management implements these policies and procedures to compile and use for decision-making. Understanding these ideas helps to grasp better how they work together in practice.
Learn more about data management platforms that help store and analyze the entire company’s data.
Sagar Joshi
Sagar Joshi is a former content marketing specialist at G2 in India. He is an engineer with a keen interest in data analytics and cybersecurity. He writes about topics related to them. You can find him reading books, learning a new language, or playing pool in his free time.
