Every organization has an extensive amount of data. But how do you use your data as an important asset for your management? According to a report by the analysts at Gartner, it is estimated that by 2025, 80% of organizations will fail if they do not adopt modern approaches for data governance. Data Governance is a complete set of practices and frameworks that can be followed by organizations for effective data assets management. Data governance and management aim to establish data ownership, accountability and understanding of the process of data collection, storage and usage in an organization. It plays an important role in maintaining trust in data and crafting informed decision-making.
Here’s what we’ll cover in this article
- What is Data Governance?
- The Framework of Data Governance
- What is the Importance of Data Governance?
- What are the Goals of Data Governance?
- The Principles of Data Governance
- Best Practices of Data Governance
- Some Data Governance Software
- Data Governance Roles
- Data Governance Vs. Data Science
- Beat the toughest data governance interviews with IK!
- FAQs about Data Governance
What is Data Governance?
Data governance is a process that defines which individual within an organization holds authority and control of data assets and the ways in which those data assets are used. It encompasses the process, technologies and people needed for managing and protecting data assets.
The Data Governance Institute defines data governance as – “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models, which describes who can take what actions with what information, and when, under what circumstances, using what methods.”
The DAMA International defines data governance as – “The planning, oversight and control over the management of data and the use of data and data related sources.”
The Framework of Data Governance
Data governance must be considered as a function supporting an organization's data management strategy. A framework provides an organization with a holistic approach to collecting, securing, managing and storing data. To understand what a framework should offer, consider the points mentioned below.
- Data architecture: The data-related sources and the entire structure of data as a crucial part of the organization's architecture.
- Data, design and modeling: Analyze, design, build, test and maintain.
- Data operations and storage: Physical data asset storage and management.
- Data security: To ensure confidentiality, appropriate access and privacy.
- Data, interoperability and integration: Extraction, acquisition, transformation, delivery, federation, virtualization, and operational support.
- Content and documents: Store, protect, index and enable access to data recovered from unstructured resources and make this data available for interoperability and integration with structured data.
- Master data and preference: To manage shared data to reduce redundancy and to ensure better data quality with standardized definition and usage of data values.
- Business intelligence and data warehousing: Manage analytical data processing and enable access to data for analysis and reporting.
- Metadata: Collect, categorize, maintain, integrate, control, manage and deliver metadata.
- Data quality: Define, monitor, maintain data integrity and improve data quality.
Every organization must consider the facets of management, data collection and operation while establishing a strategy.
Importance of Data Governance
Several organizations follow a certain form of governance for specific business units, functions or applications. Data governance is all about the way an organization establishes systematic and formal control over different processes and responsibilities. Once an organization establishes a systematic data governance, only then do they realize the overall benefits of data management. Some of these benefits are listed below:
- A more comprehensive decision-making support comes from uniform and consistent data throughout the organization.
- The IT and business become more scalable and agile with clear rules for data and changing processes.
- With the provision of central control mechanisms, it reduces the costs in different areas of data management.
- Better efficiency with the ability to reuse data and processes.
- Enhanced confidence and documentation of data processes and data quality.
What are the Goals of Data Governance?
The goals of data governance collectively support the responsible and effective management of data in an organization. They even help in maximizing the value of data governance along with reducing the risks. An organization’s goals for data governance must include:
- Data Security
- Data Quality
- Data Ownership
- Data Accessibility
- Data Integration
- Data Accountability
- Data Transparency
The Principles of Data Governance
For all the successful data governance and stewardship programs, the data governance institute mentions the eight key principles. The following are the 8 principles of data governance:
- While dealing with one another, all the participants must have integrity. When discussing the options, constraints, drivers and impacts, they must be truthful.
- Transparency is the key element in stewardship processes and data governance. All the auditors and participants must be clear with the when and how of the introduction of data-related processes and controls into the process.
- Processes, controls and data-related decisions should be accompanied by documents for supporting compliance-based and auditing requirements.
- Establish clear responsibilities and roles for data stewardship and management to ensure who is accountable for data security, quality and compliance.
- Establish procedures to manage from its acquisition or creation to archival or disposal to ensure that data is retained only till required.
- Put forward and enforce policies and standards for data management, including data classification, data definitions and naming conventions.
- Implement processes for better management of changes in data governance policies, technologies and procedures to align with evolving business needs.
- Updated assessment and improvement of data governance practices to adapt to changing business strategies and data requirements.
Best Practices of Data Governance
To align with the needs, goals and processes of an organization, data governance strategies must be adopted. Mentioned below are the six best practices worth following by every organization:
- To identify critical data elements and handle data as a strategic resource.
- Setting procedures and policies for complete data life-cycle.
- Involvement of business users in the governance process.
- Do not neglect master data management.
- Understanding the value of information.
- Avoid over-restriction of data usage.
Some Data Governance Software
Data governance is more of an ongoing program instead of a technology solution. However, there are a number of tools with integrated data governance features that help support your program. It depends on your needs, budget and data volume as to which tool suits your organization the best.
Some of the most popular solutions are listed below:
Data Governance Roles
Data governance includes several roles responsible for different aspects of governing and managing data in an organization. Even though all enterprises design their data governance differently, there are some similar roles.
- Data Stewards: They are responsible for the day-to-day management of certain data assets. They ensure data quality and data policies and act as subject matter experts for the domains assigned to them.
- Data Owner: Data owners hold accountability for certain data domains or assets. They hold the authority to make decisions about data security, access, and usage. They even ensure that data is used in regard to business objectives.
- Data Governance Director/Manager: This role is responsible for monitoring the entire data governance program within the organization. They define data governance strategies, report on the program’s progress and manage data governance teams.
Data Governance Vs. Data Science
For a successful data-driven organization, data governance and data science are the two most crucial aspects. However, both of them have different aspects. As data science governance teams require access to clean and well-governed data for doing their jobs effectively, their differences are a concern.
The need for data science governance occurred with the rising concern over data manipulation and misuse of statistical methods and data science. It involves implementing data governance structures in place to ensure the use of statistical methods and analysis is reliable, transparent and ethical.
The major reason why data science and data governance do not go hand in hand is that these two are entirely different disciplines. Data science is about analyzing data to find insights. However, data governance focuses on ensuring the reliability and accuracy of data. The two disciplines run with different objectives and goals, leading to a conflict when both of them try using the same data.
At times, data governance teams struggle to keep up with the fast-paced data science and data scientists feel restricted by following governance protocols. Data science is all about discovery and experimentation. However, data governance focuses more on ensuring that the data is used responsibly. This often leads to friction between data governance and data science teams as the two groups work differently.
Beat the Toughest Data Governance Interviews with IK!
We are all aware of the fact that a data governance profession comes with a number of responsibilities. It includes overseeing the development and implementation of procedures, standards and governance policies. However, the main responsibilities to be performed by individuals at different posts for data governance include data stewardship, data classification, policy development, analytics, data privacy, and other processes like data recovery, data archiving, and data backup. To build yourself as stable enough to get these positions and handle the responsibilities, you must be thoroughly prepared for your interviews. Hence, with a vast set of interview questions, videos and courses crafted by experts, Interview Kickstart has got you covered to help you prepare with the best for your interview.
FAQs about Data Governance
Q1. Who is the highest body in data governance?
The Chief Data Officer (CDO) is the highest-ranking executive responsible for data governance. This role is given the highest authority over all data-related initiatives. They are responsible for setting strategic direction for data governance and overseeing data management.
Q2. What is the role of data governance in data management?
Data governance includes data, reliability and quality. It establishes the policies, procedures and rules that ensure data reliability, compliance, accuracy and security.
Q3. What are the 4 pillars of data governance?
The four pillars of data governance include data, stewardship, data protection, data quality and compliance and data management. These pillars provide a strong foundation to establish a robust data governance framework.
Q4. What are the 3 key roles of data governance?
The goals, structure and needs of every organization are different. However, the three most common data governance roles include chief data officer, data Steward and data admin.
Q5. Is data governance a growing field?
Data governance is an evolving field as regulations and technology change. Data governance professionals must stay updated on industry developments and trends to align with the strategies and procedures accordingly.