Data Governance

Data governance is the establishment of processes, policies, standards, and procedures within an organization to ensure the proper management of data in terms of its accessibility, cohesion, and protection. Thus, it prioritizes the ethical and legal use of crucial information while preventing unauthorized access, loss, or corruption.

Since data is considered a critical asset to any organization, it is essential to focus on various aspects such as data retention policies, data quality management, data security, data classification, data privacy, and data access controls. Hence, this helps companies effectively maintain corporate information and diminish the risk of theft or loss.

• Data governance is a process of maintaining the availability, accessibility, security, and storage of an organization's internal data.
• It involves developing a framework for data management and usage by limiting access to crucial information and preventing the risk of its theft, loss, breach, or unauthorized access.
• The key elements of the governance framework are data quality, technology, policies, processes, standards, strategy, KPIs, security, communication, integration, collaboration, and literacy.
• The various models for governance of data include top-down, bottom-up, center-out, silo-in, and hybrid governance.

Data governance is the overall management of organizational data. It is critical for any organization that relies on data for its operations, decision-making, and strategy. The companies have various departments like finance, production, design, legal, marketing, IT, customer support, etc., that deal with different kinds of information daily. It includes operational, analytical, regulatory, credential, technical, and collaborative data types. Thus, they require IT professionals and experts for data management to maintain authenticity and limited access to crucial business information.

Some of the primary elements of the data governance framework to ensure effective data management are as follows:

• Data quality: Keeping the relevant data and prioritizing the crucial information.
• Technology: Automating the governance process using IT tools and software.
• Data strategy: Defining the purpose and operating standards for data management at the top level.
• Data policies: Setting internal and external guidelines for data sharing, quality, storage, regulatory compliance, and access.
• Data procedures: Stating the steps for data sharing, storage, quality check, tracking, and testing.
• Data standards: Developing data modeling standards, metadata management, and other standards to ensure compliance with the data policies.
• Data integration, communication, and collaboration: Maintaining a database, authorizing individuals for its access, and managing internal data sharing and collaboration.
• Data Security: Protecting the data from cyber theft, breach, unauthorized access, or loss.
• Data literacy: Training the team on the importance of data management and best practices and keeping them updated with new developments.
• Key performance indicators (KPIs): Measuring and tracking the information's relevance, accuracy, and impact.

There are several models of data governance that organizations can implement based on their specific needs and hierarchy. The major forms of governance are as follows:

• Top-down governance: Under this model, the top-level executives or leaders take decisions for governance practice and communicate the same to the governance groups via managers. The executives and employees at the lower levels need to follow the orders without any argument.
• Bottom-up governance: Here, the stewards take the decisions related to the data management, such as for policies, standards, and procedures. The decisions flow upwards to the higher level management, who then comment on the issues and scope of improvement.
• Center-out governance: Such governanace decisions are left up to the experts or centralized resources who evaluate the various aspects of the business and the impact of governance on the stakeholders to determine the controls and protocols for the organization.
• Silo-in governance: In such a model, the representatives from various groups or departments collaboratively form a council, i.e., a stewardship council or data council, to frame the data policies, standards, and framework and take decisions related to the data management according to the individual needs of the groups.
• Hybrid models: Some governance frameworks are developed at various levels where the decisions are partially taken by the top management and partially by the governance groups or experts.

The effective implementation of data governance requires a systematic approach that involves the following steps: 

1. Make a committee of top-level executives from the IT, legal, compliance, data management, and other business units. This team will develop, implement, and maintain the governance program.
2. Define the contribution of data governance in realizing the corporate vision and business objectives. Thus, it facilitates designing a framework that aligns with the organization's goals.
3. Identify the existing governance practices and activities such as information management, privacy policies, and security standards.
4. Figure out the necessary data practices and the extent of their implication depending upon the size, nature, and type of business.
5. Develop data policies, procedures, and standards for data collection, management, storage, security, and sharing within the organization in compliance with the laws. Using automated governance tools and information technology can ease the process. 
6. Assign the roles and responsibilities within the data committee. Determine the data stewards accountable for data quality, accuracy, and consistency.
7. Establish standard data management processes, including data collection, integration, processing, storage, and retrieval.
8. Provide training to the committee members to ensure everyone understands the data governance policies and their responsibilities in managing data.
9. Regularly monitor compliance with data governance policies and update them as required to reflect the organization's data landscape changes.
10. Continuously evaluate and improve the governance program to ensure its effectiveness and alignment with the organization's objectives.

Microsoft's Azure purview is a unified data governance solution. There is also other software like SAP MDG and Collibra that solves the purpose of data governance. The examples of data governance can be categorized as follows:

#1 - In Healthcare

In the healthcare sector, hospitals use data governance software to maintain the patients' database of personal information, medical history, treatment, and other data. Such information is kept confidential and shared only with patients, family members, attending doctors, and relevant medical staff.

#2 - In The Software Industry

The companies that develop software for their clients undertake data governance to maintain the secrecy of the programs, codes, passwords, and other technical information.

#3 - In Marketing

The companies that serve institutional buyers and other high-end consumers often govern data of such dealings and customer databases to have a competitive advantage.

Data governance is crucial to any information-based organization for the following reasons:

• Improves data quality: It helps to establish standards and processes that provide data accuracy, reliability, and consistency. Hence, minimizing the errors and inconsistencies leaves the organization with more trustworthy information.
• Mitigates risk: It gives due attention to data security and privacy, which enables the organization to protect its sensitive information from a data breach, loss, or theft.
• Ease of access, documentation, and search: With superior data governance practices, the organization can systematically maintain and document the database to make it easily discoverable and accessible by authorized people.
• Facilitates decision-making: Data governance helps to enable better and informed decision-making, both tactical and strategic, by ensuring that data is accurate and reliable to attain business goals and competitive advantage.
• Increases efficiency: By streamlining data management processes and reducing redundancies, organizations can handle critical information and reduce associated costs.
• Increases trust: An efficient data governance demonstrates an organization's commitment towards responsible data access and management in front of the customers, partners, and stakeholders.

Successful data governance is essential for a firm's growth, and it requires the following best practices:

1. Define clear data governance objectives and goals for the team to align with the organization's overall business goals.
2. Form a dedicated team of professionals for data governance who understand the importance of data management and security.
3. Consider data governance as a program rather than a project since it is ongoing.
4. Encourage employees to ethically follow the data governance policies and practices to build a strong system.
5. Keep updating the framework according to the current developments for the betterment of the organization and data safety assurance.
6. Being honest with the investors, stakeholders, partners, suppliers, and customers is crucial.
7. Determine the level of risk involved in the data sharing and plan accordingly.
8. Integrate and involve the representatives from all the departments or data stewards to ensure organizational implementation of the framework.

While information governance is a big picture that includes both data governance and management, let us discuss the differences between the three:

This has been a guide to what is Data Governance. We explain its best practices, comparison with data management and information governance, and importance. You can learn more about it from the following articles –

• Data Warehouse
• Data Bias
• Data Integrity