Data Governance includes the processes and technologies required to manage and secure data as a company asset to guarantee complete and reliable corporate data. It concerns creating methods and techniques performed by the responsible team for standardizing, integrating, protecting and storing corporate data. This paper aims to define the concept of data security and governance; in particular, it investigates mechanisms through which data governance can be implemented in the business processes. Furthermore, in this context, the link between effective data governance and corporate management is also discussed, providing examples of how various units can use this practice. The results showed that data governance serves to decrease additional costs and time for decision-making. It also stimulates communication within the organization and eliminates the unsuccessful steps in business strategy.
Data Security/Governance
These days, the market demands the development and implementation of information technologies (IT) in all sectors of the economy. Data has become one of the principal resources for most companies. The organization’s growth highly depends on data security and governance, which is considered an urgent issue for enterprises. Data security of a company, public organization or industrial enterprise is a set of measures aimed at preventing unauthorized access to the internal IT infrastructure, illegal seizure of confidential information and changes to databases.
The security of a company’s information infrastructure implies protection against accidental or deliberate actions that may harm the data owners or their users. The company is responsible for building a security system that prevents data leaks rather than combating their consequences. It is essential to maintain secure access to information for those who legally use the databases. The paper aims to discuss the methods businesses use to ensure data security by implementing data governance. The latter helps corporate management organize data according to the objectives and business strategy of the company. This is a relatively new concept due to the rapid progress of the IT-sphere. As a result, the research faces some challenges, such as a lack of theoretical background and data limitation.
Examination of the Topic
Definition
With regards to the definition of data governance, it can be confused with data management. One of the main differences between the two business functions is that data governance is a strategy, and data management is a tactic. The latter is defined as “the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets” (Al-Ruithe et al., 2018, p. 842). Referring to data governance, it is “the exercise of authority, control, and shared decision making (planning, monitoring, and enforcement) over the management of data assets” (Al-Ruithe et al., 2018, p. 842). It is necessary to understand that data governance and management complement each other; however, they are different.
Moreover, the research on this topic is characterized by a vast amount of irrelevant literature that should be eliminated. This means that leaders should accurately define specific practices to achieve their goals and create standard data management policies for an organization. Besides, data governance is not managed by technology solutions. Instead, a business uses technology to address the fundamental issues that a data governance plan poses.
Challenges for Data Security
It remains crucial for enterprises to ensure confidentiality, integrity, and availability of information for the stakeholders. Concerning the challenges the companies face, they can be divided into legal, technological and business-related (Al-Ruithe et al., 2018). There is also another system that distinguishes internal threats and external threats, appearing within the enterprise and in the external environment (Al-Ruithe et al., 2018). These risks may occur when implementing data governance in the cloud.
Concepts of Data Security/Governance
Organizations utilize different mechanisms to implement successful data governance. According to Abraham et al. (2019), there are three types of governance techniques: structural, procedural, and relational. Principally, planning and control over data management activities are performed by utilizing these tools considering legal structures relating to business, IT, and data management functions (Abraham et al., 2019). Companies are required to examine formal methods for decision-making processes, enhancing stakeholders’ cooperation.
The first is structural mechanisms; they are governance institutions, reporting structures and accountabilities. Therefore, they involve several crucial figures, such as the executive sponsor and data governance leader (Abraham et al., 2019). It also encompasses data owner, steward, data producer, governance council and office, and data consumer (Abraham et al., 2019). Structural mechanisms include roles and responsibilities and the allocation of decision-making authority.
With regard to procedural and relational mechanisms, the latter contributes to collaboration within stakeholders. It comprises communication; training; and the coordination of decision making (Abraham et al., 2019). For instance, stakeholders receive all needed information through training programs, which can be conducted in computer-based form, job-specific and one-on-one coaching (Abraham et al., 2019). It allows them to perform their activities according to data governance processes (Alhassan et al., 2019). Procedural governance enables data to be recorded accurately, securely, managed effectively, and distributed appropriately (Abraham et al., 2019). It can be achieved by adopting the data strategy, standards and policies, processes and contractual agreements (Cheng et al., 2017). Procedural mechanisms also include performance measurement, compliance monitoring and issue management.
Therefore, the organizational unit should be created to handle data security to support the administrative process for effective data governance. The team should examine ways to manage the data lifecycle and maintain the corporate data model (Alhassan et al., 2019). The information has no value unless there is a data model, accompanied by understanding how to control it and implement it in business processes (Alhassan et al., 2019). Data administration most often combines concepts such as the maintenance of data stewardship, data quality, and concepts, which contribute to the achievement of advantageous control over the company’s data assets (Alhassan et al., 2019). It can also include specific technologies and tactics for proper data management.
Position on Data Security/Governance and Management’s Role
Data governance is one of the major strategic initiatives for an organization. Data has become the asset affecting the prosperity of the business. The use of an information resource and a successful digital transformation are only reasonable if the company can manage its data. Improving data quality and ensuring security and availability is the objective of data governance. Unmanaged data quality is considered a concern for IT and analytics departments and the business as a whole (Al-Ruithe and Benkhelifa, 2017). While data quality technology is being deployed and maintained by the IT department, other departments are the primary beneficiaries (Al-Ruithe and Benkhelifa, 2017). For instance, the marketing department’s incorrect markup of promotional sales can lead to errors in predicting the number of goods for the next promotion. Hence, it is important to introduce data quality control into the workflow (Alhassan et al., 2018). Thus, this will decrease the time for structuring the data.
There is no system that will show good performance if it does not access the needed information. Regarding security and availability, it is necessary to ensure the system’s operability, which provides users with access to information (Alhassan et al., 2018). For this, it is recommended to use cloud storage and fault-tolerant databases (Al-Ruithe et al., 2019). Data can be lost – accidentally or intentionally erased from the media; therefore, it is necessary to back up valuable information (Al-Ruithe et al., 2019). With this approach, managers can be sure that the most relevant and correct information will be obtained when accessing the data. Consequently, it is unnecessary to arrange and verify the entered data, contrasting when the data is entered into the table manually by the operator (Alhassan et al., 2018). Thus, it significantly facilitates production and reduces the time for decision-making processes.
Management’s Role
For corporate governance, the implementation of data governance processes allows managers to obtain control over the information and details of the performance. Consequently, it is possible to monitor where and in what format data is stored, provide versioning, keep the data up-to-date, and define the rules for accessing data (Al-Ruithe et al., 2019). If employees work with the same data but do not synchronize it, this can result in incorrect results (Benfeldt et al., 2019). It is possible to place all information in one shared storage, for example, in a database or the cloud (Al-Ruithe et al., 2019). This will allow employees to work with relevant information to guard against errors.
Moreover, the accumulation of information from many sources leads to the receipt of data in a scattered form. For the system to understand them correctly, it is necessary to bring all the data to one format, which can take a long time (Abraham et al., 2019). It is more productive to determine a strategy for working with data and a standard that all employees should follow in advance(Abraham et al., 2019). It is reflected in structural mechanisms of implementing data governance. Thus, even a new employee or outsourced analyst will understand the data, and production will be more efficient.
Companies using data governance highlight several advantages in terms of business processes. These include lower costs of data management due to the reuse of procedures and regulatory compliance administration (Brous et al., 2016). Data governance solves three problems; it ensures the availability of data, transparency of the data life cycle and gives the company’s users consistent and verified data (Abraham et al., 2019). According to Abraham et al. (2019), cloud computing helps to improve the economic performance of the organization by responding to risks and crisis outcomes. Such factors as top management support, organization size, and technology readiness affect the implementation of data governance tactics. However, there is no established approach to adopt these policies in all companies.
Conclusion
Data Governance is a requirement of the current, rapidly changing and highly competitive corporate environment. At present, companies can gather vast amounts of information; hence, discipline is needed to maximize its value, manage risk, and reduce costs. Regardless of whether the information is used only within the company or in the external environment, data security and governance are critical since almost all business processes are based on effective operations with information.
There are three mechanisms that ensure the effectiveness of data governance; these are structural, procedural, and relational tools. Implementing data governance practices in the workflow will help an organization improve corporate data quality, increase its value, and monetize in the long-term perspective. An adequately structured data strategy can be compared to financial management; the latter is required to be transparent and maintained through audit and various control tools. It is equally crucial to ensure the funds’ safety; data is the same asset, and in the modern world, it can sometimes be considered more valuable. Data governance helps to reduce the number of errors, increase business efficiency and clearly understand what results can be achieved further.
References
Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424-438.
Alhassan, I., Sammon, D., & Daly, M. (2018). Critical success factors for data governance: A theory building approach. Information Systems Management, 36(2), 98-110.
Alhassan, I., Sammon, D., & Daly, M. (2019). Data governance activities: A comparison between scientific and practice-oriented literature. Journal of Enterprise Information Management, 31(2), pp. 300-316.
Al-Ruithe, M., & Benkhelifa, E. (2017). Analysis and classification of barriers and critical success factors for implementing a cloud data governance strategy. Procedia computer science, 113, 223-232.
Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2019). A systematic literature review of data governance and cloud data governance. Personal and Ubiquitous Computing, 23(5), 839-859.
Benfeldt, O., Persson, J. S., & Madsen, S. (2019). Data governance as a collective action problem. Information Systems Frontiers, 22, 299–313.
Brous P., Janssen M., Vilminko-Heikkinen R. (2016) Coordinating decision-making in data management activities: A systematic review of data governance principles. In Electronic Government (pp. 115-125). Springer.
Cheng, G., Li, Y., Gao, Z., & Liu, X. (2017). Cloud data governance maturity model. In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 517-520). IEEE.
Larcker, D., & Tayan, B. (2016). Corporate governance matters: A closer look at organizational choices and their consequences. Pearson Education.