All organizations make decisions about data, regardless of whether they have a formal Data Governance function. Those that establish a formal Data Governance program exercise authority and control with greater intentionality (Seiner, 2014). Such organizations are better able to increase the value they get from their data assets. Businesses are becoming more intelligent in their use of information. They’re ensuring that their organizational data is in the right condition to succeed in their business initiatives and operations.

Many companies set up a system called data governance to make sure they handle data well. Data governance is like a set of rules and practices that oversee how data is managed within a company or organization. It covers everything from how data is created or collected, to how it’s stored and maintained, and even how it’s deleted when it’s no longer needed. Within this modern framework, Data Governance empowers executives to alleviate from pain points.

Data Governance Goal

• To enable organizations to treat data as a valuable asset, ensuring it is properly managed, utilized, and protected.
• Ensure that organizational data is in the right condition to succeed in business initiatives and operations.
• Establish principles, policies, and processes for effective data management throughout the organization.
• Measure effectiveness and efficiency of data management practices to track progress and identify areas for improvement.
• Seamlessly integrate data governance activities into various organizational processes, including software development, analytics, and risk management.
• Ensure data governance initiatives are sustainable, requiring long-term organizational commitment.
• Secure business leadership, sponsorship, and ownership for sustainable data governance.
• Meet regulatory requirements and avoid fines by documenting data lineage and access controls.
• Improve data security through clear data ownership and related responsibilities.
• Utilize data to increase profits through optimal storage, maintenance, classification, and accessibility.
• Assign data quality responsibilities and monitor related KPIs to enhance overall enterprise performance.
• Evaluate and improve by rising the data governance maturity level phase by phase.

Data Governance Challenges

Data Visibility

Data governance requires businesses to achieve data transparency. Often, data consumer lack visibility into their own data landscape. This includes knowing what types of data are available, where they are located, and who has access to them, or whether they should have access at all. This uncertainty makes it difficult to use the data effectively, thereby impacting productivity and value. However, legacy

systems often obscure the answers to these questions. The ability to assess data content and sensitivity no matter where the data is become very important.

Unsecure Data

When businesses create more and more data, it becomes harder to manage it all and increases the chances of data breaches. To prevent sensitive information from being exposed to unauthorized people or systems, businesses are implementing additional forms of protection, such as encryption, to obscure embedded data. It is important to note that data security depends on traceability. However, most of traditional data platforms create isolated information silos that are difficult to visualize and trace. Without an integrated data store, invisible, untraceable data results in security risks. We also need other tools to help track data as it moves, manage who has access to it, find sensitive data, and set rules to protect it.

Lack of Control Over Data

Many businesses are required to comply with regulations such as GDPR (General Data Protection Regulation), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), and industry standards like PCI DSS. Regulatory compliance requires clear and measurable rules to ensure that both internal data policies and external government regulations are followed. When moving data, tools are needed to enforce, track, and report compliance. They also make sure that the correct individuals and services can access the appropriate data.

Legacy Data Systems

Many organizations have old data systems which are inflexible and difficult to manage. These systems hinder the free flow of data throughout the enterprise. This makes it difficult to share, organize, and update information. When data is stuck in separate silos, outdated, or not well-organized, it becomes difficult to establish data governance activities such as tracking data records, categorizing data, and applying detailed security models.

Data Governance Strategy

Enterprises need to think about data governance comprehensively, from data intake and ingestion to cataloguing, persistence, retention, storage management, sharing, archiving, backup, recovery, loss prevention, disposition, and removal and deletion. A data governance strategy informs the content of an organization’s data governance framework. It requires you to define, for each set of organizational data:

Where: Where it is physically stored

A best practice for data governance is data discovery to know what data assets company have. This process helps find data stored in the cloud and keeps track of where each piece of data comes from, how it’s changed, and what kind of information it contains.

Who: Who has or should have access to it

Data access management ensures robust data security and proper access control by addressing both aspects of data access governance. It establishes a well-managed access structure for who can access what, by defining identities, groups, and roles. It makes sure that only the authorised individuals and systems access data, following predefined rules and protocols.

What: Definition of important entities such as “Employee”, “Department”, “Position”

A business glossary is a core data governance tool. It houses agreed-upon definitions of business terms and relates these to data. Developing and documenting standard data definitions reduces ambiguity and improves communication.

How: What the current structure of the data is

Data cataloguing involves maintaining a catalogue that includes information about the structure of the data and details about each data object. This metadata often includes information like who created the data, its size, the number of records or when it was last updated. It also assesses the sensitivity of the data concerning governance rules, like compliance with data privacy regulations.

Quality: Current and desired quality of the source data and consumable data sets

Enterprises rely heavily on data for decisions and growth. Different data consumers may have different data quality requirements, so it’s important to provide a means of documenting data quality expectations as well as techniques and tools for supporting the data validation and monitoring process.

Data Maturity Assessment aids organizations in gauging governance maturity, guiding progress towards data-driven decision-making and market leadership. Understanding current data quality and maturity levels helps identify improvement areas, enabling strategies to enhance data quality, drive better decisions, and gain competitive advantages.

Requirements: What needs to happen for the data to meet the goals Policies.

Business needs to classify sensitive data to determine which governance policies and procedures apply to the data. Regulatory compliance requires clear and measurable rules to ensure that both internal data policies and external government regulations are followed. When moving data, tools are needed to enforce, track, and report compliance. They also make sure that the correct individuals and services can access the appropriate data. This process facilitates the identification of appropriate governance policies and procedures for effective data governance implementation.

Data Governance Capability Maturity Model

Capability Maturity Model usually define five of maturity each with its own characteristics that span from non-existent or ad hoc to optimized or high performance. Evaluating the maturity of your governance strategies can help you identify areas of improvement. When evaluating your practices, consider the following levels.

Level 1 Initial / Ad Hoc:

At this stage, data management is basic, relying on a limited set of tools and lacking significant governance. It represents an initial awareness phase, acknowledging the absence of ownership and sponsorship, as well as the need for policies and standards.

Level 2 Repeatable:

Emergence of consistent tools and role definition to support process execution. In Level 2, the organization begins to use centralized tools and to provide more oversight for data management. Roles are defined and processes are not dependent solely on specific experts. There is organizational awareness of data quality issues and concepts.

Level 3 Defined:

Emerging data management capability. Level 3 sees the introduction and institutionalization of scalable data management processes. Characteristics include the replication of data across an organization with some controls in place and a general increase in overall data quality, along with coordinated policy definition and management.

Level 4 Managed:

Data management includes performance metrics. Characteristics of Level 4 include standardized tools for data management from desktop to infrastructure, coupled with a well-formed centralized planning and governance function. Expressions of this level are a measurable increase in data quality and organization-wide capabilities such as end-to-end data audits.

Level 5: Optimization:

When data management practices are optimized, they are highly predictable, due to process automation and technology change management. Organizations at this level of maturity focus on continuous improvement. At Level 5, tools enable a view data across processes. Well-understood metrics are used to manage and measure data quality and processes.

Data Governance Tips

1. The value of data can and should be expressed in economic terms: Considering it as an asset, data holds significant value for organizations, and understanding its worth is crucial for making informed decisions.
2. Managing data means managing the quality of data: Ensuring that data is fit for purpose involves understanding the specific needs and expectations of stakeholders and aligning data quality objectives accordingly. It is also important to consider both the costs associated with low-quality data and the benefits derived from high-quality data.
3. It takes Metadata to manage data: Data cannot be held or touched, to understand what it is and how to use it requires definition and knowledge in the form of Metadata.
4. It takes planning to manage data: Managing data effectively requires careful planning to ensure that it is collected, stored, processed, and utilized efficiently and in alignment with organizational goals.
5. Data governance is cross-functional, it requires a range of skills and expertise: Data management requires both technical and non-technical skills and the ability to collaborate.
6. Data governance requires an enterprise perspective: Adopting an enterprise perspective in data management is essential for maximizing its effectiveness and ensuring that data-related efforts are aligned with organizational goals and objectives.
7. Data management is lifecycle management: This underscores the need for comprehensive strategies and practices to manage data effectively from creation to disposal.
8. Managing data includes managing the risks associated with data. Managing data effectively involves not only maximizing its value but also mitigating the associated risks. This requires organizations to consider the ethical implications of their data practices and integrate risk management measures into the entire data lifecycle.
9. Data governance is deeply intertwined with information technology: Managing data requires an approach that ensures technology serves an organization’s strategic data needs.
10. Effective data governance requires leadership commitment: Data governance involves managing a complex set of processes that, to be effective, require coordination, collaboration, and commitment. Getting there requires not only management skills, but also the vision and purpose that come from committed leadership.

References

Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy, Jessi AshdownData Governance: The Definitive Guide. People, Process and Tools to Operationalize Data Trustworthiness. March 2021.

Data Management  Body of Knowledge, DAMA International Technics Publications, Basking Ridge, New Jersey.

Anderson, Dean and Anderson, Linda Ackerson. Beyond Change Management. Pfeiffer, 2012.