Mastering Data Governance For Ensuring Trust and Compliance in Data-Driven Environments
The volume of data generated worldwide is enormous: it is expected to grow to more than 180 zettabytes by 2025 (1 zettabyte corresponds to 1,000,000,000,000,000,000,000 bytes, i.e., 1021 bytes) and will increase by an average of almost 40% per year until then.
Such mind-blowing growth provokes the question: how to manage this data amount in a data-driven environment? Although most companies have volumes of data stored digitally or physically, most data is in a non-standard format. Additionally, companies cannot always be sure that data is reliable due to age, source, etc. Employees or business leaders often hesitate to rely on this data to make decisions because they are concerned about data quality.
This is where data governance processes come into play. It is becoming increasingly important as companies face new privacy regulations and rely more on data analytics to optimize operations and drive business decision-making.
In this article, we will learn the definition and role of data governance and provide instructions to form the basis for long-term data-based work.
Understanding Data Governance
Imagine you work at a library, and your job is to organize books. In this case, data governance is like having clear rules and procedures for categorizing, labeling, and maintaining those books. It ensures that everyone at the library follows the same standards, so when people want a book on a specific topic, they can easily find it.
Similarly, in a data-driven setting, data governance means setting rules and guidelines for collecting, storing, and using data so everyone can access and trust the data they need to make informed decisions. Corporate data governance includes the policies and procedures implemented to ensure that an organization’s data is accurate at the outset and handled adequately during entry, storage, editing, access, and deletion.
According to Gartner research, by 2025, 80% of companies trying to scale their digital businesses will fail because they don’t adopt a modern approach to managing data and analytics.Furthermore, in 2023, Amazon surveyed 350 CDOs and CDO-equivalent roles and found that 45% of chief data officers rank data governance as a top priority. These data experts want to adopt a governance model that allows them to make data available to the right people and applications when needed — while keeping the data safe and secure with appropriate controls.
Let’s look at the most prospective data governance benefits for business.
Benefits of data governance
Better, more reliable data. Data trustworthiness is the crucial point. Users and decision-makers have more confidence in the data and, consequently, more confidence in the decisions based on that data. These decisions will be better because they are based on accurate information.
Data monetization. Data governance enables companies to harness the potential of the valuable data they collect and generate. Businesses receive strategic insights, analytics, and partnerships, ultimately unlocking new revenue streams and business opportunities while mitigating risks associated with data misuse. For example, data governance allows for promptly identifying potential cross-selling opportunities and creating personalized product recommendations for its customers.
A uniform database. The advantage that all parts of the company and all decision-makers work with the same information is invaluable. No more time is wasted arguing over whose spreadsheet or plan is “better” or more current. All parts of the company are coordinated with one another.
Regulatory, legal, and industry compliance. Solid data management practices are key to compliance. Auditors and regulatory representatives focus less on the data itself and more on how it was generated, processed, and protected.
Cost reduction. You can reduce waste caused by decisions based on incorrect or outdated information. You can improve customer service by knowing the exact status of ongoing activities, inventory, and labor availability.
Since the importance of data security measures for the company cannot be overstated, companies must pay attention to the creation, quality, and handling of this data. And when they do, they can be confident that their systems and databases reflect reality and effectively support decision-making and business success.
Components of a Reliable Data Governance System
A data governance framework is a basis of data strategy and compliance. The process starts with the data model that describes the data flows (inputs, outputs, and storage parameters). After that, the governance model specifies the rules, activities, responsibilities, procedures, and processes that define how these data flows are managed and controlled.
Data governance tools include:
- Data lakes, warehouses, and catalogs
- Data assets such as tables, images, PDF files, and more
- Software and platforms for accessing and analyzing data, such as automation platforms, single-point solutions, and visualization software
- The infrastructure used to transmit data, business and personal devices, servers, cameras, medical and monitoring devices, sensors and more
Data governance components are unique to each company and reflect the specifics of data systems, organizational roles and responsibilities, legal requirements, and industry protocols.
Companies vary in their data governance policies, but typical roles include:
- Chief Data Officer (CDO). The CDO at the top of the data governance structure advocates for necessary funding and support. The CDO also defines the principles of data governance.
- Steering Committee. Typically composed of senior management, these leaders set governance strategies, oversee data stewardship work, and ensure adherence to schedules and deliverables.
- Data Owners. Responsible for specific data domains, data owners approve data glossaries, ensure data accuracy, direct data quality efforts, collaborate with other owners, and provide input on policies and software solutions.
- Data Stewards. Subject matter experts in data domains, data stewards manage data day-to-day, identify and resolve data issues, participate in data policy discussions, report to data owners, and foster cross-functional collaboration to manage and understand data across business units.
Defining roles and responsibilities in data governance is essential for its success. Here are three tips to make reliable choices:
1. Clear alignment with business goal
Ensure that each role directly aligns with your organization’s business objectives. Roles should be designed to address specific data management needs. Linking roles to business outcomes creates more focused, purpose-driven data governance policies.
2. Expertise and accountability
Assign roles to individuals with the necessary expertise and authority to fulfill their responsibilities effectively. Data governance roles often require subject matter expertise in specific data domains or technologies. Additionally, clearly define the scope of each role’s accountability to avoid overlaps and gaps in responsibilities.
3. Flexibility and adaptability
Recognize that data governance is an evolving process. As your organization’s data needs change, roles and responsibilities may need to adapt accordingly. Design a flexible framework that allows for role adjustments and updates as your data governance program matures and as new data-related challenges arise.
By following these tips, you can establish a robust and adaptable data governance structure that effectively supports your organization’s data management objectives. Also, you can consult experts in data analytics infrastructure solutions to get tailor-made advice.
Actual Data Security and Privacy Regulations to Consider
Implementing the key components of data governance listed above for any enterprise, especially a large one, is complicated due to the huge amount of data, the incompatible systems, and the numerous people involved in creating and consuming data. More importantly, it opens up multiple gateways for data leakage.
Therefore, those responsible in the company must ensure that every data handling is carried out following the rules. There are also specialists in this area who are trained externally by companies such as SAP or associations such as the “Global Data Management Community” (DAMA).
After successful completion, participants receive a certificate that can help companies demonstrate that data is handled per legal regulations and thus potentially gain an advantage in competition. The most well-known certifications include:
Several well-known certifications for data governance are recognized in the industry.
- Certified Information Management Professional (CIMP). Offered by the International Association for Information and Data Quality (IAIDQ), the CIMP certification covers various aspects of information management, including data quality assurance.
- Certified Data Management Professional (CDMP). Administered by the Data Management Association International (DAMA), the CDMP certification is a comprehensive program that includes data governance as one of its domains. It covers data governance concepts, best practices, and frameworks.
- Certified Information Systems Security Professional (CISSP). While CISSP primarily focuses on information security, it includes aspects of data governance related to data security and privacy. It’s offered by (ISC) and is highly regarded in cybersecurity.
- Certified Data Governance & Stewardship Professional (DGSP). The DGSP certification, provided by Data Management University (DMU), focuses specifically on data governance and stewardship. It covers topics such as data governance frameworks, data policies, and data quality.
- Certified Data Governance Professional (CDGP). Offered by the Data Governance Institute (DGI), the CDGP certification focuses on data governance fundamentals, strategy, and implementation. It is recognized for its emphasis on practical application.
- Certified Information Privacy Professional (CIPP). The International Association of Privacy Professionals (IAPP) offers CIPP certifications focused on developing and controlling data privacy policies. The CIPP/G certification, in particular, covers global data protection regulations closely related to data governance.
- Certified Information Systems Auditor (CISA). While CISA is primarily an audit and control certification, it includes aspects of data governance, especially in IT governance and risk management. ISACA offers it and is relevant for professionals involved in data governance audits.
These certifications cover various aspects of data governance, including strategy, implementation, security, and privacy, catering to different career paths and areas of expertise in data governance.
Implementing Data Governance Frameworks: Phases and Tips
Implementing a data governance framework is a strategic process that requires careful planning and execution. Management should approve and review them regularly to ensure regulatory compliance and best practices in place. In this part, we compiled a few general recommendations to watch for when establishing data governance processes in your company.
First, think of clear answers to the following questions:
- What is your approach to data governance?
- What are your data management policies and procedures?
- What are your standards for quality data?
- How do you protect data privacy?
- What plans do you have for monitoring and auditing your data governance?
A successfully implemented data governance strategy should deliver results across your organization’s analytical efforts. This includes reporting, advanced analytics, machine learning, data science, and more.
Your company’s data governance framework should include these key components:
- Mission statement. The written mission statement describes the goals and explains how they are measured. All employees should know the mission statement.
- Documentation. Each phase of data governance must be documented and serve as a reference point during system optimization or later adjustments.
- Business glossary. Compile key definitions and concepts in a business glossary. A common language prevents misunderstandings and increases the effectiveness of data governance.
- Metadata. Data is described using metadata. Metadata refers, among other things, to data type, tags or keywords, or the user rights of images.
- Master data. Define a codified, reliable data source that plays an essential role in the value-adding use of the data.
- Data catalog. This results from an inventory of all available data, including metadata (such as search functions), data origins, collaboration tools, and file types.
- Data mapping. A graphical overview of how data moves through the company, illustrating how this influences its properties. Data can be assigned to specific categories based on properties such as sensitivity. This determines where certain data sets are located in data governance.
Phase 1: Establishing data governance foundations
To establish data governance foundations, start by clarifying your vision, goals, and how the executives will measure the success. Define roles and responsibilities, analyze your organization’s current data assets, and select specific ones for inclusion in the framework. Define data formats, workflows, and policies for implementation.
Phase 2: Implementing the data governance plan
The following phase consists of four stages.
- Ensure data availability. Data governance teams should ensure data availability by integrating data from various sources, even in large organizations with distributed data silos.
- Ensure data integrity. Start by defining critical data components, analyze and standardize data formats, and enrich data as needed.
- Enforce compliance management. Every data asset should have an owner responsible for its integrity. Ensure compliance with data policies and foster a data-driven culture within the organization.
- Continuous feedback and monitoring. The feedback from the data governance is typically provided by various stakeholders within an organization, including data stewards, data owners, IT teams, compliance officers, and business units. This feedback can encompass issues related to data quality, compliance with regulations, data security concerns, and alignment with business objectives.
The feedback may be in the form of suggestions for data policy improvements, identification of data quality issues, recommendations for data access controls, or concerns about data privacy. It is a crucial concluding step as it helps refine policies, procedures, and practices to ensure data is managed in a way that aligns with organizational goals and meets regulatory requirements.
Collaborative data management offers significant business benefits by fostering a culture of data transparency, accountability, and trust. It enables cross-functional teams to work together to manage, define, and ensure data assets’ quality and security. Data sharing protocols reduce the risks associated with data errors and breaches while improving data-driven insights.
Employ custom software development services to create data governance frameworks to maximize success in this endeavor. Tailored solutions will meet your unique needs and ensure data governance best practices.
Data governance represents a continuous journey. Yes, setting up the system requires some effort, but these processes will be part of everyday life in your company.
To conclude, organizations must pay attention to creating an end-to-end data governance framework that helps them achieve multiple goals:
- Data governance should help companies understand what personal data they have, where it is located, and how it is used
- Data governance should provide information about who has access to personal data and how it is utilized
- Data governance should enable monitoring of compliance with regulations such as GDPR
- Data governance should be adaptable to changing needs over time
Ensure your organization gets the most out of your data governance investments and minimizes the risk of data breaches. Schedule a consultation with a Lightpoint expert once you’re ready to start.