A guide to data governance principles and best practices
Data is easily one of the most important resources for organizations. However, organizations gather a lot of data without any plan or process for it. According to the Pulse of the Industry Report 2022, as many as 75% of respondents have no tools or process for managing structured data across line of business systems.
But implementing a system to collect and process data can make it more accessible for the organization. That's where data governance and its principles come into play.
What is data governance?
The Data Governance Institute (DGI) defines it as "...a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods."
In more simple terms, data governance is the internal policies regarding how data is gathered, stored, handled, and disposed. Throughout the data's entire lifecycle, there is a set of rules to guide it and ensure security and compliance standards are in place.
Data governance includes actions, technology, and processes for an organization's data. The ultimate goal of the data governance framework is to ensure data is high-quality, reliable, and protected.
What is the difference between data governance and information governance?
Before moving on, let's discuss the difference between data governance and information governance. Both are used to improve data management. But while data governance focuses on the technical aspects of collecting and organizing data, information governance concerns itself with how the data can be used for business goals, customers, and regulators.
Essentially, data governance is about the processes for the lifecycle of data. Information governance has a broader scope and can include the insights gained from efficient data.
What are the business advantages of strong data governance?
A strong data governance framework benefits nearly every aspect of an organization. Data governance is used to meet a multitude of business objectives. By harnessing data, an organization is in a much better position to make accurate data-driven decisions and improve business performance. Here are a few business advantages of implementing an efficient data governance structure.
- Improve compliance: Depending on your industry and local regulations, your organization may be subject to legal requirements for keeping sensitive data secure. Data governance can ensure you are meeting compliance regulations.
- Make confident decisions: Data governance means your data is accurate, timely, and reliable. These factors are crucial to making business decisions that will drive growth in a fast-paced environment.
- Save costs: Many organizations are storing data that they don't need. Storage costs can quickly add up and excess data slows down the system. Data governance will have a policy for removing redundant, obsolete, and trivial (ROT) data meaning you only hold on to essential records.
- Manages risk: Without strong governance, you put your organization at risk of not noticing red flags of an intruder. With cybercrime a consistent issue, you need to keep track of who is accessing sensitive files.
- Increased efficiency: Setting up data policies makes your organization more efficient since everyone will use data that meets high-quality standards. Greater accessibility doesn't mean you lose reliability.
Everyone within your organization will benefit from effective data governance. Marketing can use data to create impactful messages. Finance will benefit from having accurate reporting. Your product team will gain insights into customer behavior and desires. Senior management will have the reliable data they need to make informed decisions. And your legal department will also be happy to have evidence that your organization is following compliance regulations.
The essential principles of data governance
The DGI established eight essential principles of data governance:
- Integrity: Stakeholders part of the data governance framework should have integrity and honesty when discussing factors that impact data.
- Transparency: It should be clear how and when data processes were introduced.
- Auditability: Data governance should be auditable, especially for compliance and operations auditing requirements.
- Accountability: Define who is accountable for cross-functional data processes and controls.
- Stewardship: Define responsibilities for people who are accountable for data.
- Checks-and-Balances: When defining accountabilities, you should ensure there is a checks-and-balances system in place with people who collect, manage, or use data.
- Standardization: Introduce and support the standardization of an organization's data.
- Change Management: Implement a system for controlling how data changes.
Regardless of your organization's goals, data stakeholders should ensure data policies are in alignment with their purpose. These principles may help stakeholders focus on what is important and prioritize accordingly.
Best practices of data governance
When creating a data governance framework and policies, you need to consider the process for how data is used, who gets access to it, and when they are allowed to access it. Here are a few best practices to consider:
- Planning: You need to create a data governance strategy. Consider what your goals are, how they will help your organization, and then determine the metrics you will use to determine a successful data governance process. You also need to define governance roles and who is responsible for what and when.
- Tools and capabilities: You'll need governance tools and capabilities that can help you deliver your framework and policies. These tools should aid in creating processes that are standardized, documented, and repeatable while also supporting compliance.
- Processes: When creating processes, you want to ensure you are clear about what is happening and who is in charge of it. You should document policies, controls, accountability, stewardship, managing changing data, and resolving issues.
- Regulations: External laws have a say in what you do with your data. Ensure you are using solutions that are compliant with data privacy and protection regulations.
- Rules and standards: You will need to design the data governance framework and include data policies, standards, and compliance requirements. Depending on your needs, you may want to evaluate your current data governance and then update data rules or find and address gaps.
- Security: A data breach is a significant problem, especially if it exposes the sensitive data of customers. Data governance can include the classification of data to find sensitive information. Then you can give special protection to classified data with tools like access management and encryption.
How RecordPoint can help
Creating data governance is crucial to ensure a company's data is relevant, timely, and secure. Implementing your data governance framework and policies means you need a solution for the full data lifecycle, including data inventory, categorization, privacy, and minimization.
RecordPoint streamlines data governance with a centralized data inventory. Connectors allows you to connect to all your data sources, allowing you to build a true data inventory and implement one set of policies for all of your data. You have all the tools you need to create your own file plan and retention schedules, set data categorization rules, and activate automated data categorization.
A guide to data classification: confidential vs. sensitive vs. public information
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Bringing consistency to your data management, no matter where the data is
Connectors provide the same high-value inventory and sensitive data identification to more and more data sources without the ongoing headache of integration maintenance and code-based customization