Data is one of a company’s most precious assets. Data is essential to the growth and ongoing success of companies, especially data driven ones. Data, in essence, is an evolving legacy that a company can use to understand where the company started and how it should move forward and improve.
However, how well the company manages the asset’s quality, governance, and ownership will define, to a large extent, the company’s overall success. From data stewards to top-level executives, efficient data governance necessitates engagement and accountability across the enterprise. To ensure that the implementation is successful, one should be aware of how data governance works.
This article will discuss the following:
- What is Data Governance?
- What is Cloud Data Governance?
- The 5 Principles of Data Governance
- The 10 Components of Data Governance
- How to Start a Data Governance Program
- Common Data Governance Framework
- Data Governance in Healthcare
- Data Governance Tools
What is Data Governance?
Data governance is a broad term that refers to the organizational policies and procedures that govern data management. Data governance guarantees that data is safe and secure. It also ensures that the data provided is:
- Protected
- Reliable
- Documented
- Controlled
- Evaluated
What is Cloud Data Governance?
In multi-cloud or hybrid cloud computing systems, when data gets stored in various locations, and cloud data governance protocols such as permissions, guidelines, and metadata are inconsistent across databases, data governance takes on a new level of sophistication. Moreover, modern tools from a single software platform that enable data engineers, data governance and compliance teams to automate data governance, data access rules, and privacy protection can help data teams negotiate the complexities of data governance.
The activity of managing data availability, authenticity, consumption, and security in cloud computing systems to fulfill critical business objectives is known as data governance. These objectives most likely include the following listed below.
- Increasing the privacy and security of data.
- Access to sensitive data is regulated and monitored.
- Using timely data analytics to improve operations and corporate decision-making.
- Obtaining and ensuring compliance with data privacy and security standards on an ongoing basis.
- Data breaches and other cyber security threats get avoided.
Simply put, data governance refers to an organization’s ability to handle data effectively at all stages of the data lifecycle. Effective data management policies and processes improve business outcomes and drive business growth, a prerequisite for enterprises in today’s competitive market. Currently, businesses collect an enormous amount of internal and external data. Effective data governance is required to use that data, control risks efficiently, and cut costs.
Why is Data Governance Important?
Data is, without a doubt, an organization’s most valuable asset. Data governance ensures that information is useful, available, and secure. Data governance translates to improved data analytics, which leads to better decision-making and operations management. It also aids in the prevention of data inconsistencies or errors, which can result in data integrity concerns, poor decision-making, and many organizational issues.
Moreover, data governance is also critical for compliance issues, ensuring that firms meet all levels of regulatory obligations consistently. This feature is essential for decreasing operational expenses and eliminating vulnerabilities. At its most basic level, data governance leads to greater data quality, lower data management costs, and greater data access for all users. As a result, you make better decisions, which leads to better business outcomes.
The 5 Principles of Data Governance
To guarantee successful implementation, keep the following data governance principles in mind.
Accountability
Any successful data governance approach requires a high level of accountability. An organization’s data governance will be aimless, blunt, and useless if no one takes responsibility. Across the organization, You must implement ownership and accountability. If only one department, such as IT, takes responsibility, other business executives may begin to consider a company-wide issue as something that only IT needs to understand.
Establish a data governance council with representatives from all departments, including senior executives, to ensure cross-organization responsibility. The newly formed data governance council should develop data processes and policies that will be executed throughout the organization and have the ability to guarantee that they get strictly followed.
Normative Laws and Regulations
Every successful data governance process will need to develop and follow uniform rules and regulations to secure the data and guarantee it gets handled according to all relevant external laws. These standardized rules and regulations, which will be developed at the data governance council level and implemented by the data steward, will provide criteria for all aspects of data usage.
Administration of Data
Responsibility follows accountability to execute effective data governance across the organization. Employing a dedicated data administrator is the key to ensuring that proper responsibility for your company’s data gets undertaken. This person will report to the data council, enforce its data rules and regulations, and follow them regularly.
Data Quality Standards
Individuals must draw on high-quality, reliable data to make educated and productive business decisions. Yet, one would be shocked how few companies can genuinely trust the data at their disposal.
In partnership with the data steward, the data governance council should create a shared set of data quality standards to improve the business data quality. These standards will ensure that the data’s quality gets evaluated and documented. Plus, it helps ensure the entire organization knows and adheres to these guidelines.
Transparency
All data governance processes established within the organization should be transparent to the maximum extent possible. By maintaining a permanent record of all relevant data governance actions and procedures, any possible third-party audit should be able to see exactly how sensitive data gets handled, what you used it for, and why you used it in that way. This openness will secure the company in the event of a data breach, and it will also allow you to understand more about how data gets used throughout the business.
These principles are the cornerstone of any effective data governance policy, allowing key personnel across the organization to help with implementation and verify that it gets followed regularly.
The 10 Components of Data Governance
To create an effective data governance plan, you need certain components. All data management programs get built on the foundation of data governance. It is a necessary practice that underpins all other aspects of data management expertise. It also features components for each knowledge area that fulfill the company’s data management requirements. These 10 components include:
- People
- Data Planning
- Data Processes
- Data Protection Regulations
- Data Rules & Data Standards
- Data Protection
- Technology
- Key Performance Indicators
- Communications
- Socialization
People
The foundation of any data governance program is the data governance specialists, data stewards, and other essential business and IT workers. They create and maintain workflows to guarantee that the company’s data governance needs get addressed. To ensure that this component adds the most value to the company, organizations must invest extensively in training and education.
Data Planning
Developing an enterprise data strategy is an important stage in the data management process; nevertheless, many businesses are unaware of the need for a formal and documented data plan. A data strategy is an administrative document that outlines the high-level corporate data requirements and a plan to meet those goals.
Data Processes
For data management, data governance programs must develop critical data processes. Data issue tracking and resolution, data quality surveillance, data exchange, data lineage tracking, evaluation, automated data quality testing, and various additional regulations are all common.
Data Protection Regulations
A data policy is a high-level collection of statements that describe expectations and intended results, intending to influence or direct data habits at the business level. Data governance policies get established through data governance initiatives. Outbound data sharing, regulatory conformity, and other policies are only a few examples.
Data Rules & Data Standards
A data standard is a framework and technique for guaranteeing that a data policy gets followed. A data rule controls or constrains activity to assure conformity to data standards, ensuring data policy compliance. Organizations will typically seek to set data rules for:
- Master and reference data
- Data definitions and domain development
- Metadata management
- Classification
- Accessibility
- And more.
Data Protection
Protecting digital data, such as those in a database, from damaging factors and undesirable acts of authorized and unauthorized users, such as theft, hacking, or a data breach, is what data security is all about.
Technology
To be as automated as possible, the data governance program will require a variety of technologies. Smaller data governance projects will often rely on the software platform that their company already has. Larger data governance projects typically invest in software tailored to data governance and the functions required by the enterprise.
Scanning databases and files become much easier with data governance software so you can quickly locate the needed files and complete tasks such as:
- Relevant metadata
- Managing metadata
- Automating data stewardship processes
- Decision trees
- Social participation
- Collaboration
Key Performance Indicators (KPIs) and Metrics
The effectiveness of the data governance program depends on establishing business metrics and KPIs for monitoring and measuring the program’s overall business impact. Metrics and KPIs must be quantifiable, easily traceable over time, and measured in the same manner year after year.
Many organizations make the error of failing to conduct a baseline measurement and then attempting to measure the performance of their data governance program years later. In these circumstances, determining an appropriate measure of success is extremely challenging.
Communications
All written, oral, and electronic interactions with organizational audiences interested in or need to know about the data governance team’s activities are included in data governance communications. A communication plan, which includes objectives, targets, and instruments for all communications, should be included in a governance program from the beginning.
The essential component of enterprise data governance communications is a communications plan. The strategy defines ways to communicate data governance and stewardship challenges and triumphs to key parties and the rest of the company. The communications strategy emphasizes the correct business arguments and their outcomes.
Moreover, a successful communication plan is more effective than one with disconnected and scattered messages because it gets built on strong themes. All communications required by the Data Governance program get covered by the plan, including objectives, targets, and tools.
Socialization
The cornerstone of data governance socialization is the data governance socialization plan. The data governance socialization plan is a strategy and process for integrating data governance into an organization’s rules, culture, structure, and operations. The strategy is unique to the company because you must adjust it to its culture and standards of conduct.
Because data governance represents a shift from the existing quo, it is critical to socialize it. This new method to effectively control an organization’s data must be implemented across the board and become part of the culture. Data governance socialization is a continuous process and vital activity in every governance program.
Starting a Data Governance Program
Working with relevant stakeholders to develop data governance standards and rules is the first step toward establishing a successful data governance practice. The next stage is to create plans for putting such procedures in place and enforcing them. These processes are just as vital as the underlying policies to ensure that projects are carried out properly and that guidelines are followed consistently.
Take these pointers into account:
- From the start, including all stakeholders and data asset owners in the process.
- Train and educate all necessary teams, people, and stakeholders on data governance on a routine basis.
- Assist in creating and executing the data governance systems and procedures by maintaining open lines of communication. This assistance can take the form of emails, newsletters, official reports, updates, or conferences, but regular contact with all stakeholders is critical.
- Make sure to work with goals that are clear, precise, and attainable.
- Begin small and gradually build up once ready. It is tempting to take on all the goals at once, but it is better to start with a few minor goals and gradually build up from there.
Common Data Governance Framework
Data governance encompasses a wide range of topics. As a result, a good data governance structure includes several features. For starters, it should comprise data management policies, regulations, procedures, and systems. It is good to produce some essential documents to guide the process and ensure that these procedures are focused and working toward the proper goals.
Every Data Governance Framework should include the following:
- A statement of purpose, more commonly, a mission statement.
- Data governance objectives that you can measure.
- Metrics that you will use to assess the goals.
- Clearly defined roles and responsibilities for various components of data governance.
Furthermore, it is critical to document and discuss all aspects of the framework at every process stage. However, some businesses decide to use data governance tools to assist with installation and implementation. This type of software can help ensure that plans get designed properly, implemented, and enforced. While it is not required, many businesses use software to assist with the following listed below.
- Management of programs and workflows
- Creating a data catalog
- Documentation of the policy development process
Working with relevant stakeholders to develop data governance standards and rules is the first step toward establishing a successful data governance practice. The next stage is to create plans for putting such procedures in place and enforcing them. These processes are just as vital as the underlying policies to ensure that projects are carried out properly and that guidelines are followed consistently.
Take these pointers into account.
- From the start, including all stakeholders and data asset owners in the process.
- Train and educate all necessary teams, people, and stakeholders on data governance on a routine basis.
- Assist in creating and executing the data governance systems and procedures by maintaining open lines of communication. This assistance can take the form of emails, newsletters, official reports, updates, or conferences, but regular contact with all stakeholders is critical.
- Make sure to work with goals that are clear, precise, and attainable.
- Begin small and gradually build up once ready. It is tempting to take on all the goals at once, but it is better to start with a few minor goals and gradually build up from there.
Data Governance in Healthcare
Healthcare may be at the top of the list of industries that require a comprehensive data governance policy. Consider the enormous amount of healthcare data available to any individual, the sensitive nature of that data, and the life-or-death situations that rely on accurate data. It is indeed understandable why data governance is critical in healthcare.
Patient records, blood test results, EKGs, MRIs, billing records, drug prescriptions, and other sensitive medical information are all examples of data in the healthcare profession. Medical professionals require healthcare data to make educated decisions about patient treatment. Data governance gives healthcare organizations a regulated and structured way to share medical data so that each patient receives the best possible treatment.
Healthcare Data Vulnerabilities
Individual pieces of data are at the heart of data governance in healthcare. Protecting, securing, and precisely gathering each part of data is a major challenge. Below are some points to consider in Data Governance when it comes to the Healthcare System.
- A healthcare business plan should include good data governance and high-quality analytics. Reduce risks, fines, and other penalties by better understanding and fine-tuning the underlying data operations.
- The healthcare industry has the potential to be transformed for the better via automation and data exchange. Universities, hospitals, research institutes, and technology businesses must communicate in the same data language, and data must move freely and safely throughout the healthcare system.
- Managing the increasing volume of data and combining various data sets is a major undertaking.
Data Governance Tools
A committee, a specified set of procedures, and a plan for implementing these processes must adopt data governance in the organization. Setting data management guidelines, creating methods to resolve data challenges, and assisting businesses in making decisions with high-quality data are all roles of data governance in an organization.
Data governance is critical to handling all of a company’s data requirements. Some of the most common Data Governance systems are listed below.
- Satori
- OvalEdge
- Truedat
- Xplenty
- Alation
- Dataddo
- Atlan
- Collibra
- IBM Data Governance
- Talend
- Informatica
Conclusion
Data governance has an impact on an organization’s strategic, operational, and tactical levels. As a result, data governance must be undertaken in the continuous iteration for effective data structure and consumption. Enhanced company revenue, lower data management costs, increased data value, standardization of data systems, norms, processes, and rules, are all advantages of data governance.
Agile Data Governance with Satori
Satori helps you with DataSecOps for your modern data stack. This includes continuous sensitive data discovery, universal access policies, and integration with existing data governance tools to make data governance more efficient and immediate. Satori helps streamline access to sensitive data and create security policies that are independent of the specific data infrastructure you’re using.
