Data Governance, the neglected side of data solutions

Article written for IT Systems magazine 11/2023

Data is the new oil of the 21st century. Its value to businesses is irreplaceable. Data Governance (DG) plays an indispensable role in ensuring the quality, integrity and security of these valuable assets. Without data governance, you can end up in a messy mess.

What is Data Governance?

Data Governance (DG) is the process of managing the availability, usability, integrity, and security of data in a company's systems, based on internal data standards and policies that also govern the use of data.

Effective data governance ensures data consistency, reliability, and also guards against misuse. This is increasingly important as organizations face new data protection regulations and increasingly rely on analytics to help optimize their operations and make business decisions.

A well-designed Data Governance framework typically includes a management team, a project committee, which serves as the governing body, and a group of data stewards. Together, they create data governance standards and policies, as well as implementation and enforcement procedures that are primarily managed by the data stewards. Ideally, organizational leaders or other business representatives should also be involved, in addition to IT and data governance teams.

Why does Data Governance make sense?

Without effective Data Governance, data inconsistencies across an organization's various systems cannot be resolved. This can make data integration difficult and cause data integrity issues that affect the accuracy of Business Intelligence (BI), business reporting, and analytics applications. In addition, errors in the data may not be detected and corrected, further affecting the accuracy of BI and analytics applications.

Poor data governance can also hinder compliance initiatives, causing problems for companies that must comply with a growing number of privacy and data protection laws, such as the European Union's GDPR. A Data Governance program typically involves the development of common data definitions and standard data formats used across enterprise systems. This increases data consistency for both business and compliance purposes.

What Can Happen Without Data Governance?

Without proper Data Governance, several issues can arise:

  • Data will suffer from a lack of quality, potentially resulting in inaccuracies, inconsistencies, or outdated information.

  • Duplicates of data may be created, leading to increased data storage and management costs.

  • Inadequate data organization may cause employees to spend more time searching for and correcting data instead of focusing on their core tasks. This can slow down work, reduce productivity, and result in missed opportunities.

Additionally, if data is not managed and quality assured, it can lead to erroneous decisions based on unreliable information. The lack of control over data can also increase the risk of data leakage and security incidents. In summary, the absence of Data Governance in an enterprise can have serious and widespread negative consequences. Therefore, it is crucial for enterprises to prioritize the governance and management of their data.

What Are the Goals and Benefits of Data Governance?

Data Governance focuses on achieving several key aspects. The first emphasizes overall data quality, including standardization, completeness, consistency, and the elimination of duplication. Another equally important goal is to ensure data security, protecting data during storage and use against both internal and external risks. It also aims to educate users to promote the most efficient use of data. Last but not least, Data Governance strives to achieve compliance with prescribed rules and standards, including the establishment of company-wide data management guidelines. This approach helps improve data quality and security, enabling effective use for the benefit of the organization. It ensures data accuracy and reliability, reduces errors in decision-making processes, and facilitates data organization and optimization. By establishing clear communication channels for requirements and responsibilities, it improves information sharing across the organization and fosters collaboration between departments.

The Fundamental Pillars of Data Governance

Data Governance relies on four fundamental pillars:

  • Rules and Policies: These define what data is important, who is responsible for it, and how it should be managed and protected. These policies ensure consistency, security, and compliance.

  • Processes: They define the procedures for data collection, storage, updates, and analysis. Clearly defined processes minimize errors and enhance data quality.

  • Organizational Structure: This includes identifying the key people and teams responsible for data governance. This structure ensures that data responsibilities are clearly allocated and managed.

  • Technology: This encompasses tools and platforms that support data collection, storage, analysis, and security. The right technologies can streamline the entire data governance process.

Data Governance Challenges

Implementing Data Governance can pose challenges, including:

  • Demonstrating business value, as Data Governance may deliver results in the future and often requires significant investment. Concrete financial indicators to measure success may be lacking, becoming apparent only when a data breach or security incident occurs.

  • Adapting to cultural changes within the organization, which can be complex and time-consuming.

  • Managing big data, which requires advanced technologies and tools as data volumes increase. Poor data quality often becomes a risk with larger datasets, necessitating more time-consuming data cleaning and editing.

  • Cultivating a data culture within the organization, as the lack of it can lead to a lack of awareness of the benefits and motivation for Data Governance, lack of accountability and responsibility, lack of transparency, lack of collaboration and networking, and inaccurate decision-making.

Best Practices and Implementation

When implementing Data Governance, it is crucial to have clearly defined and measurable goals to track progress and achieve desired outcomes. The deployment process begins with an initial understanding of the requirements and needs of departments and stakeholders, which helps effectively design and implement a Governance framework. Understanding the requirements and needs of various departments and stakeholders is essential to ensure that the Governance framework aligns with organizational goals and expectations.

C-level management support from the outset and sufficiently informed leadership is also essential, including in the employee training process, where important data governance principles are emphasized and a robust data culture is built. In addition, it is necessary to ensure that all data domains are covered and data is properly documented. Communication with data owners and stakeholders is key to maintaining transparency and collaboration within the organisation, while clear and concise processes will ensure effective governance and use of data for the business. In this way, data governance can help to better organise and control data and enhance its value to the organisation as a whole.

It is generally recommended to use software to help with automation and mechanical and technological domains. Examples of such tools include Attacama, Atlan, Apache Atlas, IBM Cloud Pak for Data, One Trust Data Governance, Oracle Enterprise Metadata Management, SAP Master Data Governance, and SAS Information Governance.

For example, Attacama, Atlan, Apache Atlas, IBM Cloud Pak for Data, One Trust Data Governance, Oracle Enterprise Metadata Management, SAP Master Data Governance or SAS Information Governance.


David Pešava, Business Intelligence Analyst and Developer at Lundegaard