A Data Governance framework can be described as a collection of processes, rules, and responsibilities used to structure an organization’s Data Governance program. A solid framework will cover data standards, data privacy, business strategies, and the responsibilities of key individuals. Well-designed Data Governance frameworks support effective Data Governance programs and should standardize rules and processes across the organization.
Without effective Data Governance, errors and inconsistencies can develop in an organization’s various systems. For example, if the customer accounts section stores data in various formats, it could make data integration efforts for research purposes difficult, resulting in misinformation and poor business intelligence.
Well-designed Data Governance frameworks help to ensure the data is trustworthy, easy to access, and kept both confidential and secure.
Every organization has its own unique goals and ways of doing things. One will be focused on international sales, while another offers accounting services. An organization’s unique features and goals dictate what data should be carefully controlled and can be shared freely.
The unique needs of a business should determine the Data Governance strategy and become the foundation for the Data Governance framework.
Another important consideration in the Data Governance framework is protecting the customers’ private data. Data breaches are a common, near-daily occurrence around the world. Consequently, governments establish laws and regulations (CCPA, HIPAA, and GDPR). A major part of Data Governance should be focused on privacy regulations and protecting the private information of customers and clients. A good Data Governance framework includes controls that protect data and comply with the appropriate regulations.
The use of automation has become remarkably important to reduce labor and minimize errors. Therefore, it should also be included in the Data Governance framework.
Developing a Data Governance Framework
A Data Governance framework must be designed to meet the individual needs of the organization. It should be designed with the goal of standardizing your organization’s rules and processes for storing, using, and collecting data. In this way, operations are simplified by making the data accessible to all appropriate staff. A well-designed framework can help in creating a program that can identify data sources, build catalogs, and deliver data.
Starting with a data maturity model is a good first step. It provides useful information for developing a Data Governance framework. A data maturity model assesses and evaluates how well the data is being managed and used. The basic premise is a comparison of the current state of data usage compared to the desired state of data usage. Once the differences are understood, the changes that need to be made will become clearer.
A Data Governance framework may involve the use of a number of apps and software programs for master data management, data quality, data warehousing, and metadata management (a Data Governance framework can be built with more than one theme in mind). An important question to ask is, “What is the business struggling with, regarding data?” Listed below are some of the important areas to consider when building your framework:
- Data Quality: Data Quality is the condition of data, based on its accuracy, consistency, completeness, reliability, and how up to date it is. Measuring Data Quality levels helps in identifying data errors.
- Data security and privacy: Compliance and regulations requirements must be addressed. This includes setting access management rights, data privacy procedures, information security controls, and more.
- Policies: Data Governance policies should establish the principles, standards, and practices needed to ensure high-quality data, and that it is well protected. These policies are normally developed by the Data Governance committee.
- Architecture: Operational efficiency can be improved by simplifying data integration. This involves using architecture components, such as master data modeling, service-oriented architecture, data modeling, etc.
- Data storage and business intelligence: Storing historical data can be useful for developing business intelligence. This will probably involve building data warehouses (or some other form of storage) and will need software and policies.
Establishing Roles and Responsibilities
While several Data Governance roles can be played in large corporations, small- and medium-sized businesses should include three basic positions of responsibility: the Data Governance committee, the chief data officer, and the data steward. These roles and responsibilities are included in the data framework, communicating who is responsible for what. Listed below are descriptions of the three primary roles and responsibilities:
- The Data Governance Committee: This group often comprises the organization’s business managers, IT leaders, and stakeholders. It is responsible for deciding policies and standards that are applied to the framework. The Data Governance committee is also responsible for resolving data disputes between departments and staff, and amending the policies as needed.
- The Chief Data Officer: Typically, this role is filled by a senior employee. They act as a middle person between the Data Governance committee and the data steward, and it is their responsibility to coordinate Data Governance programs and activities. The chief data officer’s responsibilities include overseeing the organization’s Data Governance program.
- The Data Steward: If data is being used for research, having a data steward is a necessity. In some ways, a data steward acts as the data police, keeping staff from straying into behavior that will cause data chaos. This person should be something of a generalist, having a good understanding of both IT and business. Depending on the size and goals of the organization, the data steward can be remarkably important.
Automation
Automation can be considered a critical component for success and should be included in the Data Governance framework. Including automation in the framework helps to assure data is used appropriately throughout the organization. An important use of automation is the continuous collection of metadata from interactions with other individuals, businesses, and other organizations. The data can be “automatically” translated into a standardized format, as well.
Steps to Make the Transition Easier
Data Governance frameworks organize people, processes, and technologies together to create a paradigm of how data is managed, secured, and distributed. But the path to a Data Governance framework often seems difficult to embark upon. Here are three steps to help you get started:
- Cleansing the data has the effect of streamlining the work it is used for. It is the process of repairing or removing inaccurate, corrupted data, or data that is incorrectly formatted. It also removes duplicate data. There is data cleansing software, though small amounts of data can be cleaned manually. After the data has been cleansed, it is important to continue the process as new data comes in. Data cleansing can be done with the help of automation and should be included in the Data Governance framework.
- Automated data transformation tools can be used to translate a variety of data formats into the standard format used by the organization. (It might be worthwhile for large or expanding organizations to investigate data mesh and data fabric.)
- Organizing data makes it easier to find and use. Arranging and classifying the data makes it more accessible. The data should be arranged in the most orderly and user-friendly way possible, allowing anyone with appropriate access to easily find what they are searching for. (For example, some websites seem to have no organization, making it difficult for potential clients to trust their services.)
Communication and Enforcement
Data Governance frameworks should be passed on to all areas of the organization. There should be a significant effort made to introduce and educate all staff and managers on how the new Data Governance program will work, including who to talk to if they have problems (primarily the data steward, though they may refer the problem to a technician).
Enforcement, after the Data Governance program is in place, is primarily the data steward’s responsibility. Hopefully, the data steward will act more as an educator than a bully. After the Data Governance framework has been developed and the program is in operation, complying with the changes must be mandatory. As Robert Seiner, president of KIK (Knowledge is King), once stated, “You need to execute and enforce authority over the management of data.”
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