Principles of Data Governance
One of any organization’s most valuable assets is data. Data is critical to the company's growth and long-term success. In essence, data is an ever-evolving legacy that a firm can utilize to understand where it came from and how it can progress and evolve.
However, to a considerable measure, the company's total performance will be determined by how well it controls the asset's quality, governance, and ownership. Effective data governance involves involvement and responsibility across the company, from data stewards to top-level executives. To achieve a successful deployment, it is necessary to understand how data governance works.
In this in-depth guide, we’ll explore everything you need to know about the principles of data governance– from GDPR data governance to the differences between data governance and data management. Let’s start by defining exactly what data governance is and its purpose.
Everything You Need to Know About the Principles of Data Governance
What is Data Governance?
Data governance is a set of procedures, responsibilities, rules, standards, and measurements that guarantee that information is used effectively and efficiently to help a company accomplish its objectives. It defines the procedures and responsibilities for ensuring the quality and security of data utilized within a company or organization. Data governance establishes who has the authority to take what actions, based on what data, under what circumstances, and with what techniques.
Any organization that deals with big data needs a well-crafted data governance program, which will describe how your company benefits from uniform, shared processes and responsibilities. Business drivers emphasize the types of data that must be properly handled as part of your data governance plan, as well as the expected advantages. Your data governance structure will be built around this plan.
Data governance ensures that data responsibilities are clearly defined and that responsibility and accountability are established within the organization. Strategic, tactical, and operational duties and responsibilities are all covered by a well-planned data governance structure.
Data Governance vs Data Management
Data governance framework refers to a collection of policies that are enforced throughout an organization. Data management is a more limited notion, focusing on carrying out the specific operations that support the data governance strategy. In other words, when it comes to data governance vs. data management, data management refers to the execution, while data governance refers to the direction that guides the implementation.
Data governance and data management, on the other hand, can complement each other. Data management and data governance work together to support a company's data management and security goals, with the former focusing on operations and the latter on policy.
For example, a data governance policy can state that customer data must be kept for a certain amount of time in order to comply with legal obligations. The necessary data archiving and deletion inside the data storage systems can subsequently be performed using data management techniques.
Data access is another example of how these two systems might function together. A data governance policy may state that personally identifiable information (or PII) can only be accessible by workers who require it to do their jobs. As a result, a data management procedure may be developed to allow role-based access to personnel who match the requirements.
7 Principles of Data Governance
So what are Data Governance principles? The following seven principles make up the structure of data governance as a whole.
Integrity
The most crucial principle of data governance is integrity. Whether or whether the data you have is being utilized correctly is determined by the entity that is using it. The integrity of data is maintained if the organization’s means and purposes are ethical. All participants who use the data must be honest and forthright in all data-related choices. This can involve judgments regarding actions, consequences, and restrictions, among other things.
Stewardship
When it comes to creating effective data governance throughout your organization, responsibility follows accountability. Employing a committed data steward (or a team of such individuals) is the key to ensuring that adequate accountability for your organization's data is taken. This person or team will report to the data council and be in charge of implementing the data rules and regulations established by the council, as well as ensuring that they are followed on a regular basis.
Auditability
Audits must be allowed on all of the data that is used and stored. Audits can be conducted on all data governance choices, controls, and procedures. When conducting audits as part of your data governance framework, you must provide sufficient paperwork to demonstrate compliance.
Accountability
Any successful data governance approach requires a high level of accountability. Your organization's data governance will be aimless and ineffectual if no one takes responsibility for it. Across the organization, ownership and responsibility must be implemented. Other business executives may start to consider what is an organization-wide matter as something that only IT needs to worry about if just one department takes responsibility.
Checks and Balances
Accountabilities for data governance should be defined in a way that creates checks and balances between business and technology teams. A good framework should also create checks and balances between those who create, collect, manage, and use data. Those who introduce standards and compliance requirements should also be subject to this principle. The single most critical job of Data Governance is to provide suitable checks and balances that can guide management operations.
Standardization
Data is typically used by several teams inside a business. This might indicate that data stored in one format is incompatible with data stored in another. As a result, certain principles and criteria for data harmonization must be established. These include, for example, regulations for data definition, accessibility, security, and privacy. Standardization is key.
Change Management
Data Governance should also enable proactive and reactive change management operations for reference data values, master data, and metadata structure/use. If you want long-term success with data-driven initiatives, you must be able to govern how the data evolves. Yes, certain data is expected to be updated on a regular basis. Other unrelated data should be changed only in exceptional circumstances, and then after thorough consideration and effect analysis. Even though change notification actions are baked into data stewardship accountabilities, most Data Governance programs become involved in data-related change management activities.
Data Governance Roles and Responsibilities
There are a number of important roles and individuals involved in data governance. For the sake of brevity, we’ll just look at the core roles: Leaders, council, and data stewards.
A data governance program must begin with an executive data governance sponsor who ensures that the program has sufficient resources and is aware of the program's overarching objective. The council's data governance policy will be aligned with higher-level corporate strategy objectives, according to the sponsors. A chief data officer or a data governance manager may perform the sponsorship function in big organizations. A CDO or governance manager in this role often handles data governance communications and analyzes multiple efforts to ensure the entire program stays on track and within budget.
There will also be a need for a council. The data governance council is an internal steering body that controls the strategic development of the organization's data governance program. In order to handle multiple aspects of the program, it might comprise technical, business, and legal professionals from throughout the corporation. Data stewards from each domain are typical members, who represent their domain's interests as well as the governance program's interests at the implementation level.
Last but not least, data stewards are required. Data stewards are often domain experts who are well-versed in how the organization's data governance standards relate to the data they are responsible for. Regular users can utilize data stewards as a point of contact for a variety of practical data governance problems. However, their primary responsibility is to work behind the scenes to assure data quality and build confidence in the data they manage. Furthermore, data stewards guarantee that the data they are responsible for is compatible with the ever-changing environment of data regulation, whether it be local, regional, national, or international. As a result, data stewards serve as a resource for their domain's compliance needs.
Data Governance Best Practices
There are a number of best practices worth considering when developing a sufficient data governance framework for an organization:
- Concentrate on the business model. An operational model, also known as an asset model, describes how a company defines roles, duties, business terminology, data domains, and other things. This, in turn, has an impact on how workflows and processes work. It has an influence on how a company manages its data.
- Make a list of your data domains. You'll need to decide the data domains for each line of business after you've established the data governance framework. Customer, vendor, and product data domains are some of the most well-known examples.
- Within your data domains, identify essential data items. The next step is to determine the key data items after identifying the data domains. There's no need to boil the ocean by focusing on all data artifacts at once in the early phases of your data governance program. Only the most important aspects of the business should be identified.
- Ascertain that all of your stakeholders are on board. To build a data governance plan, you'll need executive support, but that's only the beginning. You also want to motivate stakeholders to take action so that your governance strategy is implemented throughout your company.
Conclusion
Data governance really isn’t just an optional framework to incorporate into an existing business model. Virtually every organization out there deals with sensitive data in some capacity. As a result, a lack of data governance could have serious repercussions. These repercussions could also result in legal troubles for organizations that are not properly protecting and using customer data. If your organization does not have a regulation-compliant data governance plan in place, now is the time to start developing one.
