Data Governance and Four Important Principles

Data has been considered an unavoidable by-product of business operations, providing business insights for decision-making. Organizations need high-quality data to back up their increasingly sophisticated and international operations. It is thus important for organizations to develop an effective strategy protecting their valuable data as well as complying with stringent data privacy regulations.

To effectively manage the growing data volumes and overcome difficulties in data security and compliance, organizations need data governance frameworks in place. Implementing data governance will have a significant effect on how businesses operate and how they comply with laws and regulations. This article will provide a perspective on data governance to further clarify the values it adds and the key principles for effective data governance.

How does Data Governance Affect Data Security and Privacy?

Data governance is the process of ensuring that all of an organization's data is accessible when needed, accurate, protected from misuse, and kept in a secure environment in accordance with the organization's internal data standards and regulations. When properly implemented, data governance can safeguard data by keeping it under control and ensuring its veracity, accuracy, auditability, and documentation (in terms of where and what data the organization holds).

Businesses today needs to integrate a data governance program in their organization for a number of reasons, such as:

• Large amounts of data from a variety of sources, which might lead to discrepancies.
• Inadequate data quality
• The need for standardized data-access regulations
• "Data democratization" and the emergence of self-service analytics in organizations
• Compliance with regulations like GDPR
• The utmost importance of a standardized data vocabulary for enterprise-wide data analysis
• The importance of better metadata in organizations

Businesses can risk significant harm if they do not protect their customers' personal information as required by laws. To ensure data privacy, data governance necessitates that businesses be aware of the data they own, where it is housed, how it moves through their IT infrastructure, and for what purposes it is being utilized.

Data governance facilitates data privacy compliance as its frameworks advocates the integrity and overall quality of data. It ensures data privacy by outlining how that information should be handled in accordance with data privacy regulations. With data governance, organizations can achieve data privacy compliance, maintain customer trusts and improve cybersecurity posture.

Overall, data governance and data privacy align on helping organizations get the most out of their data value, ensure that it is widely available throughout the business, and meeting any data protection laws and standards. An organization's productivity, security, data quality, and usability may also all benefit from data governance. Some common use cases of data governance include:

• Compliance for data privacy: Data governance can help clarify who is responsible for what and how data is utilized, and what procedures should be in place when data-related problems arise.
• Self-service analytics: Data governance ensures data sets are available and reliable and provides a solid foundation for self-service analytics.
• Data quality for AI/ ML models: a strict data governance program can improve data quality, making the data sets suitable to be used in AI/ ML which can reduce the likelihood of biased models.

Key Principles for Effective Data Governance

To achieve business success, a company must have access to reliable data and ensure data privacy compliance and data security. An effective data governance program can help organizations achieve such objectives. An organization’s data governance program should adhere to the following key principles to ensure the effective implementation:

Transparency 

It is crucial for an organization's data governance program that everyone in the organization and its customers have a clear understanding of what data assets the it really has. Establishing transparency and credibility with stakeholders requires being open about data collection, use, storage, and sharing practices. This is essential for gaining support for data governance programs among employees and customers.

Transparency will also allow auditors to figure out how sensitive data was handled, what it was used for, and why, preventing potential data misuse. This principle will help organizations meet the data privacy regulation requirements, identify potential threats inside an organization, prevent data breaches and enable organizations to continuously get insights from their data use.

Accountability

As a part of their accountability, organizations must implement programs that promote compliance with data privacy regulations and be able to outline how they protect individuals. By mandating the implementation of such programs, businesses are more likely to take a methodical, consistent approach to meeting privacy regulations.

Accountability recommends a paradigm in which both individuals and businesses take part in the responsibility for data privacy and data security via the use of openly documented policies and procedures. An organization's degree of accountability can be estimated against the following factors¹:

• Organizational dedication to accountability and implementation of internal policies that are in line with external criteria, such as laws, generally accepted principles or standards
• Methods, such as software, tutorials, and lectures, for making privacy policies workable
• Mechanisms for both internal, continuous monitoring and assurance reviews and external verification.
• Transparency and mechanisms for individual participation
• Means of remediation and external enforcement
  
  

Rules and regulations

To safeguard data and guarantee that it is utilized in line with all applicable external standards, an effective data governance program will need to comply with standard rules and regulations (such as the GDPR). A data governance policy should be in place to ensure that these rules and regulations are easy for organizations to adopt. The foundational aspects of a data governance policy for a data governance program can be the following:

• Data access and availability
• Data usage
• Data integrity and integration
• Data security

Data quality 

High-quality data assurance is one of the primary drivers for organizations to build a governance program. Each member of an organization will be held accountable for ensuring the integrity of its data under a comprehensive data governance program. The following common data quality metrics can contribute to an effective program that can ensure data quality and aid in the accomplishment of business goals².

The majority of tasks in data governance involves in how to improve and monitor data quality. Organizations thus need data governance that leverages tools to provide them with visibility of their data sources and allow for in-depth analysis and quality assurance checks. Good data governance also brings data creators and data users onto the same platform, enabling communication and a shared understanding of data quality.

An effective data governance program guarantees that data is accurate, reliable, and secure. As businesses need to comply with data privacy laws and increasingly depend on data analytics to aid in process optimization and drive strategic decision-making, data governance has become an urgent matter. Adnovum can assist your business with customized solutions dealing with demanding business requirements for your successful data governance programs.

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References:

1.    The Centre for Information Policy Leadership. (2009). Data Protection Principle of Accountability Discussion Paper.

2.    TechTarget. (2021). How data governance and data quality work together.