What Is Data Governance? Definition, Importance, and Best Practices
Data governance is the process of managing the availability, usability, integrity, and security of a company’s data. Click here to learn more.
Data governance is defined as the collection of data management processes and procedures that help an organization manage its internal and external data flows. It aligns people, processes, and technology, to help them understand data to transform it into an enterprise asset. This article covers the definition of data governance, its importance in the post COVID19 world, and the best data governance practices for 2021.
Table of Contents
What Is Data Governance?
What Is Data Governance
Data governance is the collection of data management processes and procedures that help an organization manage its internal and external data flows. It aligns people, processes, and technology, to help them understand data to transform it into an enterprise asset.
Data governance is the process of managing the availability, usability, integrity, and security of an enterprise’s data based on internal data standards, policies, and rules. Effective data governance ensures that data is consistent, understandable, correct, complete, trustworthy, secure, and discoverable.
Data governance covers various topics such as data architecture, data modeling, data storage and operations, data security, data interpretation and interoperability, document and contents, reference and master data, data warehousing and business intelligence, metadata, and data quality.
Data governance establishes the processes to standardize, integrate, protect, and store corporate data. The key goals of data governance include:
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- Minimizing data security risks
- Establishing internal rules for data use
- Implementing compliance requirements
- Improving internal and external communication
- Increasing the value of data
- Facilitating a robust foundation for the continued existence of the company through risk management and optimization
Data governance programs impact the strategic, tactical, and operational levels in enterprises. Hence, to efficiently organize and use data in the context of the company and in coordination with other data projects, data governance programs must be treated as an ongoing and iterative process.
Also Read: What Is a Data Catalog? Definition, Examples, and Best Practices
Key Components of a Data Governance Process
Key Components of a Data Governance Process
Data governance is the foundation of all data management programs. It provides a framework, workflows, and decision-making authority to properly govern an organization’s data. Data governance has ten key components that exist to meet the enterprise’s data management requirements across each knowledge area. Let’s look at each of these components in detail.
1. People
The data governance professionals, data stewards, and other key business and IT staff are the backbones of a data governance program. They establish and develop workflows to ensure that the enterprise data governance requirements are met.
2. Data strategy
The data governance team plays a crucial role in the development and implementation roadmap of an organization’s enterprise data strategy. A data strategy is an executive document that provides high-level enterprise requirements for data and ensures that those requirements are met. Building an enterprise data strategy is a vital step in the organization’s data management journey.
3. Data processes
Data governance programs need to establish key data processes for data management. These include data issue tracking or resolution, data quality monitoring, data sharing, data lineage tracking, impact analysis, data quality testing, and many others.
4. Data policies
A data policy is a high-level set of one or more statements that state expectations and expected outcomes of data that influence and direct data habits at an enterprise level. Data governance programs establish data governance policies for data management. Policies include outbound data sharing, regulatory adherence, and many others.
5. Data standards & data rules
A data standard provides a framework and an approach to ensure adherence to a data policy. An example of a data standard could be using the ISO 3166 standard for the definition of the codes for the names of countries, dependent territories, special areas of geographical interest, and their principal subdivisions.
A data rule directs or constrains behavior to ensure adherence to data standards, which provides compliance with data policies. An example of a data rule would be an organization that only allows country codes listed in the ISO 3166 Standard. Typically, organizations will look to establish data rules for master and reference data, data definitions and domain development, metadata management, classification, accessibility, and many others.
A data governance program can leverage many data standards. Some of the more notable data standards include:
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- International Organization for Standardization (ISO): 3166, 19115, 11179
- Dublin Core: A basic, domain-agnostic, most widely used metadata standard that can be easily understood and implemented.
Also Read: Top 10 Data Governance Tools for 2021
6. Data security
Data security involves protecting digital data, such as those in a database, from destructive forces and unwanted actions of authorized and unauthorized users. These unwanted user activities refer to espionage, cyber-attack, or data breach.
7. Communications
Data governance communications include all written, spoken, and electronic interactions with association audiences who need to know about the data governance team’s activities.
A communication plan encompasses objectives, goals, and tools for all communications and should be part of a governance program from the very beginning. The plan identifies how to present governance and stewardship challenges and successes to the various stakeholders and the rest of the organization. The communications plan highlights the right business cases and presents their results.
8. Socialization
Socialization of data governance is an important activity in any governance program. The data governance socialization plan is a plan that helps integrate data governance activities into an organization’s policies, internal culture, hierarchy, and processes. The plan is unique to the organization as it is tailored to its culture and standards of behavior.
9. Metrics/KPIs
Establishing business metrics and key performance indicators (KPIs) for monitoring and measuring the overall business impact of the data governance program is vital to the program’s success. The metrics and KPIs must be measurable, tracked over time, and consistently measured the same way every year.
10. Technology
The data governance program needs various technologies that make the process seamless and automated. Smaller data governance programs typically use the technology stack which they already have within their enterprise.
Meanwhile, larger data governance purchase software that is specific to data governance and the functions it requires. It simplifies the process of capturing the required metadata, management of the metadata, automating the data stewardship workflows, decision trees, collaboration, and many other data governance functions.
Also Read: What Is Enterprise Data Management (EDM)? Definition, Importance, and Best Practices
Importance of Data Governance Post-COVID
Today, organizations are generating and storing more and more data on a daily basis. Post-COVID, as the maximum workforce continues to work from home, employees often access and process sensitive data remotely. If not governed correctly, this “remote scenario” can cause reputational damage and potential financial penalties.
According to a 2021 research study by Mordor Intelligence, the data governance market is expected to register a CAGR of over 21.44% during the forecast period, 2021 – 2026. It is expected to reach a value of USD 5.28 billion by 2026.
Where is so much data coming from?
According to a recent report, the number of IoT devices reached 26.66 billion in 2019 compared to 4.1 billion people connected to the internet. McKinsey says every second, about 127 new IoT devices get connected to the internet, and at this rate, the total number of IoT devices is estimated to reach 30.6 billion.
It is highly improbable to store all this data in one place. The emerging “edge computing technology” allows for the storage of data at the point of collection. However, this requires protocols to ensure that data comes from trusted sources and is used for the consented purposes.
Hence, with growing digitalization during and after COVID, organizations could suffer heavily if proper data governance programs are not in place. Poor data governance can have a significant impact on:
a) Customer trust relationship with an organization storing customer data
b) Regulatory or legislative compliance (for example, GDPR)
c) Impaired reporting and decision-making
d) Elevated Data Management costs.
Considering these factors, organizations need to define a data governance assessment and remediation approach to focus on what matters most and target their spending.
Also Read: What Is Data Security? Definition, Planning, Policy, and Best Practices
Roadmap to next-generation data governance
Companies can employ the following tactics for effective data governance:
a) Align corporate and data strategies.
b) Re-align current organizational roles and responsibilities, processes, and tools to facilitate improved data governance.
c) Ensure that data governance is included in existing organizational standards, policies, and procedures.
d) Establish an environment that is endorsed by the executive leadership team that fosters the right organizational culture.
Though it may not be possible to completely remove data risk due to human error, yet implementing the above approach may prove handy in mitigating data risks in the long run. Besides, organizations can design customized data governance solutions by incorporating automation and advanced workflow tools along with analytics capabilities. This can prove helpful in generating meaningful business insights in a timely manner at an economical cost.
Also Read: What Is Data Security? Definition, Planning, Policy, and Best Practices
Key Challenges to Effective Data Governance
The power of data in driving business growth is well known today. Effective data governance allows organizations to get maximum benefits from their most valuable asset. With high-quality data, businesses are able to gain insights for better business decisions and increase efficiency and productivity.
Moreover, data governance also protects the business from compliance and regulatory issues which may arise from poor and inconsistent data. Gartner predicts that through 2022, only 20% of organizations investing in information governance will succeed in scaling governance for digital business. Here are some common challenges organizations face while establishing data governance frameworks and policies:
1. Data silos
A data silo is a collection of data held by one group that is not easily or fully accessible by other groups. Data tends to be organized by internal departments. Such data systems often hinder the free flow of data and information across the digital ecosystem. This makes it difficult to share, organize, and update information within the organization. With siloed and disorganized data, establishing data governance can be challenging.
2. Data quality
Data governance involves oversight of the quality of the data coming into a company as well as its usage throughout the organization. Data stewards need to be able to identify when data is corrupt, inaccurate, old, or when it is being analyzed out of context. They should be able to set rules and processes easily to ensure that company data can be trusted. The ability to trust data is a cornerstone for data-driven organizations that make decisions based on information from many different sources. Hence, poor data quality can act as a severe problem while running the data governance programs.
3. Data opacity
Data governance requires companies to achieve data transparency. Information such as the kind of data the organization has, data residing location, who has access, and how this data is used, should be accounted for. However, legacy systems hide the answers to these questions. Hence, organizations should implement a data management process to establish strategies and methods to access, integrate, store, transfer, and prepare data for analytics. This can help organizations in better decision-making.
Also Read: What Is Enterprise Data Management (EDM)? Definition, Importance, and Best Practices
4. Unsecure data
With the proliferation of data sources both inside and outside enterprises, data breaches are also on the rise. Like successful data management, data security hinges on traceability. IT teams should be able to track:
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- Where the data originated
- Where it is located
- Who has access to it
- How this data is being used
- How to delete it
Data governance sets rules and procedures, preventing potential leaks of sensitive business information or customer data so that sensitive data does not get into the wrong hands. However, legacy platforms create siloed information that is difficult to access and trace.
5. Control over data
Businesses often dive into data governance when they need to comply with regulatory policies such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI-DSS), and the U.S. Sarbanes-Oxley (SOX) law. All these regulations require organizations to have data governance structures that show traceability of data from source to destination, data access logs, and how and where data is used.
With set regulatory standards, companies can protect sensitive information from getting into the wrong hands and establish control over their data. However, the lack of control over data can pose a serious challenge to the organization’s data governance program.
Also Read: Top 10 Document Management Systems (DMS) in 2021
Top 7 Data Governance Best Practices for 2021
When looking for data governance best practices, organizations can learn from others who have already worked through the various processes and templates. Each organization is different and will need to customize and adapt the data governance practices to their process.
Organizations can apply an agile development mindset to data governance by starting small with a minimum viable deployment and then iterating and growing from that point. This can give long-term benefits and bring the rest of the organization on track with your journey.
Let’s look at the top 7 data governance best practices for 2021.
Data Governance Best Practices
1. Start small, but consider the larger picture
Data governance is built on three pillars: people, process, and technology. A business builds the larger picture when it starts with the people, builds the processes, and finally incorporates technology into the processes.
Without the right people, it’s difficult to build successful processes needed for the technical implementation of data governance. Hence, identifying or hiring the right people for your solution can be the starting point for an organization. The right people can then help build your processes and source the technology to accomplish the job.
2. Develop a business case
Ensuring buy-in and sponsorship from leaders is key when building a data governance practice. But buy-in alone won’t fully support the effort and guarantee success. Instead, building a strong business case by identifying opportunities that data quality will bring may be helpful.
Improvements can include an increase in revenue, better customer experience, or efficiency. Leaders can be convinced that poor data quality and poor data management is a problem. But, data governance plans can fall flat if leadership isn’t committed to driving change.
3. Measure goals with metrics
As with any goal, if you cannot measure it, you cannot reach it. When making any change, you should first measure the baseline to justify the results after. Collect those measurements early, and then consistently track each step along the way. Your metrics should show overall changes over time and serve as checkpoints to ensure practical and effective processes.
Also Read: Top 8 Big Data Security Best Practices for 2021
4. Ensure effective communication
Irrespective of the organization’s position in the data governance program and processes, it is essential to communicate. Consistent and effective communication is critical to show the impact of the program, celebrate wins, and acknowledge setbacks along the way.
Create a list of stakeholders within your organization and enable communications that are easy to access and easy to digest. This will ensure that the right people know what they need to know while avoiding surprises and streamlining overall progress.
5. Keep in mind that data governance is not a one-time project
Creating a data governance program may appear like handling a ‘new project’. Sections of an organization may feel tempted to assemble a team to take on the project while the rest of the organization waits for it to be done. This is where many businesses witness their data governance strategies slow down.
A data governance strategy isn’t a one-time project. There is no set end date for it. Instead, it’s an ongoing practice that’s introduced as a regular policy. When implementing a data governance program, make sure that you present it as a long-term investment, not a one-off project. Data governance may eventually become a part of everyday life at your organization.
6. Identify the roles and responsibilities
Data governance calls for teamwork with deliverables from all the departments. Clearly defined roles are essential to every data governance program, and it is important to assign levels of ownership across your organization.
Determining who has authority and responsibility will help socialize the data governance program and establish an intelligent structure to tackle data programs as one team. Data governance roles might include data governance council, data managers, data owners, data stewards, and data users, to name a few.
7. Focus on the operating model
An operating model is an asset model that outlines how an organization defines roles, responsibilities, business terms, data domains, etc. This, in turn, affects how workflows and processes function. It impacts how an organization operates around its data.
The operating model is the basis for any data governance program. The idea here is to establish an enterprise governance structure. Depending on the organization, the structure could be centralized (a central authority manages everything), decentralized, or federated (multiple groups of authority).
Also Read: Data Governance in a Hybrid Cloud Architecture: Can DSaaS Solutions Help?
Takeaway
Executives and senior leaders in every industry know that data is important. It can bring about digital transformation to propel the organization past its competitors. Without proper data governance, it is challenging to run even a simple business. But for data to fuel such initiatives, it must be readily available, of high quality, and relevant to the business. Efficient data governance ensures that the data has these attributes, which enable it to create value.
As the pandemic propelled digitization, data governance is set to play a pivotal role in any organization’s success in this digital world. Post COVID-19, it will be interesting to see how companies set up their data governance frameworks for a better future.
Are you following the above best practices to overcome the possible challenges of data governance? Comment below or let us know on LinkedIn, Twitter, or Facebook. We’d love to hear from you!
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