Data Governance in Business Organizations

All companies need to use high-quality data to achieve success in their fields. It is necessary to organize the data in such a way that a company can solve the problems it faces with the help of it. The usage of data of questionable accuracy increases the risk of loss of profit, followed by the wrong management decisions. The company’s managers should, therefore, know if they can trust the data and whether they are accurate and complete enough for the company to benefit. One of the vital tasks of the managers is to understand what data are useful and how to implement them. The necessity to understand the data relevant for each particular problem is leading to the creation of the strategy known as data governance. This program provides answers on how to define which data are financially beneficial for the company and avoid improper data usage in the decision-making process.

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Definition of Data Governance

There are various definitions of the term data governance, which scholars use. According to Sen (2019), data governance is the usage of “a set of rules, tools, and practices to exercise control or action over a given data asset” (p.9). The company defines what data are appropriate to successfully address the problems. The implementation of this approach contributes to the structure and organization of all acquired data. Sen (2019) claims that data governance consists of three principal aspects: accountability, consistency, and adaptability. The combination of them ensures proper data management in the company. The first task is to define the data owner and make it clear to all the company’s managers. Then, it is essential to figure out if the data are complete and consistent enough to use it in an efficient way for the company’s benefit. And then, finally, the company has to make sure that the chosen data correspond to its goals. The combination of the three factors is the key to the successful use of data by the company.

There is another approach to the understanding of the term data governance. Ladley (2019) suggests a definition, according to which it is “a required business capability if you want to get value from your data” (p.17). In this case, the methods of data governance refer to the principles of supply chain management. All the acquired data moves through the data supply chain of an organization (Ladley, 2019). The duties of the company’s managers are separated, which contributes to standard-setting for user data and, therefore, success in meeting the challenges.

Implementation of data governance depends on several conditions, which a company needs to meet. Alhassan, Sammon, and Daly (2019) identify seven factors, which are critical for the success of an organization. The most fundamental one is the human factor, which refers to employee competence. The second important factor is precise and detailed data, which the employee needs to process. Third, data tools need to be flexible so that the employee can adapt them to a specific task. The fourth factor provides that the employee needs to adjust the data according to the company’s data policies. Fifth, it is important to define roles and responsibilities within an organization for the successful performance of its tasks. Sixth, the data requirements need to be clear for all the employees of the company. And, finally seventh, data strategies need to be well-planned and focused. Compliance with these factors contributes to the better performance of a company’s employees in this program for achieving its objectives.

Importance of Data Governance within an Organization

The ultimate goal of data governance implementation is to achieve data quality. In short, data governance is “providing the right people with the right information at the right time” (Brous, Janssen, & Vilminko-Heikkinen, 2016, p.1). Proper management of the received data is the key to the benefit of a company. The data stored by the company help to improve the decisions it makes and increase the efficiency and effectiveness of organizations.

The benefits of data governance for a company can be either direct or indirect. According to Brous et al. (2016), direct benefits include a reduction in costs and risks and an increase in revenue. The indirect benefits are improvements in the trust of the customers and the perception of the information initiatives’ performance. Thus, data governance has a significant impact on the company’s success and productivity.

Successful Examples of Data Governance Implementation

Major companies all over the world start to implement data governance programs for their benefit. They use various strategies and technologies to achieve better performance. The new technologies provide safety for their data and contribute to a faster and simpler solution to a company’s problems. The success of modern companies depends directly on their ability to process data correctly. The two examples of these companies are AXA XL and Lufthansa Technik.

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AXA XL is a subsidiary of the French insurance and investment group of companies known as AXA Group. The company provides insurance services such as professional liability insurance, casualty insurance, specialty insurance, and others. AXA XL used the services of Collibra, the company developing data intelligence products. AXA XL’s goals were to facilitate the exchange of information within the company, have a specially designed platform to communicate with their customers, and develop a service, which can analyze their efficiency on the market. They also wanted to have an opportunity to track all the data issues and make the data of the company transparent so that their business partners can trust it.

Collibra offered the product named Data Governance Center of Excellence to achieve the objectives specified above. They choose this data governance technology as transparency of the data was the company’s priority. Another product of Collibra they used was the Business Glossary developed specifically for the company. As a result, business users of AXA XL can access a page in Collibra with the information they can trust. The benefits from the technology outweigh its costs as the company has found an efficient way to find business partners.

Lufthansa Technik

Lufthansa Technik is one of the biggest companies providing aircraft services. They used the products of the platform Cloudera to achieve better performance. The main objective of the company was to reduce the costs connected to flight delays and optimize the work process. Cloudera helped Lufthansa to build a system that can process enormous amounts of data and make them transparent and secure for both managers and customers of the company.

Cloudera developed a product named AVIATAR that optimizes the whole process and provides the solutions of airline companies. It analyzes all the company’s data and predicts possible issues they might face in the future. As a result, Lufthansa has significantly reduced its operating costs as now they can predict and prevent problems and find the best possible solutions.


To protect the users’ data and offer the best solutions for the daily needs of businesses, many modern companies start to use data governance models. As shown in the example of AXA XL and Lufthansa Technik, the companies benefit from cloud products, reduce operational costs, and make their work more efficient than before. Despite the high price of such services, cloud platforms are gaining more popularity among businesses. Companies start to understand the necessity to take on the new models and become more successful in a changing world.


Alhassan, I., Sammon, D., & Daly, M. (2019). Critical success factors for data governance: A theory-building approach. Information Systems Management, 36(2), 98-110.

Brous, P., Janssen, M., & Vilminko-Heikkinen, R. (2016, September). Coordinating decision-making in data management activities: a systematic review of data governance principles. International Conference on Electronic Government (pp. 115-125). Springer, Cham.

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Ladley, J. (2019). Data governance: How to design, deploy, and sustain an effective data governance program. San Diego, CA: Elsevier Science.

Sen, H. (2019). Data governance: Perspectives and practices. Basking Ridge, NJ: Technics Publications.

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