Data Governance

Data governance refers to the exercising of control and authority over data management. It is a process that is based on internally applicable data standards and policies to manage the integrity, security, usability, and availability of data in organization systems. Effective data management ensures consistency and reliability and that data is not misused or beached. As the new rules on privacy are applied and more rely on data analytics to optimize operations and drive business decision-making, this is becoming increasingly important for organizations.

In general, a well-designed data management program involves a management team, a governing committee, and a group of data stewards. They work together to develop standards and policies for data management and to implement and enforce the data managers primarily. In addition to the information technology executives, data management teams and other representatives of the business of an organization participate in data security. So, data governance is a process of data security.

While it is not generally fully institutionalized, most organizations have some sort of data management for their specific applications or departments. Consequently, the systematic establishment of data management often changes from informal rules to centralized rules. Formal data management usually takes place once the company has reached the size of a company that can no longer perform cross-functional tasks effectively. In that situation, the perspective of DG comes forefront to prevent existed data breaches.

In brief, data protection is a collection of rules, processes, protocols, and indicators that track the usage, management, and stock of knowledge. Data protection recognizes the particular ethos and methodology of the business, as well as who owns the security of data. Data governance thus encourages the accurate, reliable, and responsible usage of data for everyone in an organization (Kovacovaet al., 2019).

Data governance offers the structuring, management, and update of data while maintaining consistency and enforcement in a company-specific environment by improved availability and access to real-time data. These processes maintain usability, accuracy, fairness, and data protection, as well as functionality for easy usage across the whole organization or company. Amid too many dependencies, many companies aim to adopt an efficient data processing strategy. More organizations implement technologies that better manage, navigate and secure data on a regular basis in order to minimize this labor-intensive operation. These solutions, or data processing systems lead to automating workflows and structuring vast quantities of data. Every tool for data governance must be able to handle and address the demands of specific end users efficiently and easily from several separate access and/or storage locations. These access points or collection points contain:

• Data warehouse centers
• The cloud (where 50 percent of data in the next three or four years is expected to be stored as well as processed)
• Social networking or media (where customers loyalty data has been stored and that data used for business enhance purpose)
• Mobile platforms – a point of access that is usually a unique obstacle in governance

Figure: Conceptual framework (data governance)

Source: Abraham, Schneider and Brocke, 2019

The above figure comprises six aspects of the conceptual framework for data governance (Alhassan, Sammon and Daly, 2018). Mechanisms of governance constitute a central feature of the system and involve institutional structural, relational and procedural mechanisms. The operational scope defines data collection organizational extension and approximately paroxysms the research unit.

Author: Thomas Devasia

Keywords: 

#Data Governance

#Data Protection

#Data Management

#Conceptual framework

#Information governance

References

Abraham, R., Schneider, J. and Vom Brocke, J., 2019. Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, pp.424-438.

Alhassan, I., Sammon, D. and Daly, M., 2018. Data governance activities: A comparison between scientific and practice-oriented literature. Journal of Enterprise Information Management.

InformationWeek. 2021. It's a New, Challenging World for Data Governance - InformationWeek. [online] Available at: <https://www.informationweek.com/big-data/its-a-new-challenging-world-for-data-governance/a/d-id/1329977> [Accessed 21 February 2021].