The Emergence of Corporate Data Governance

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Read more about author Tejasvi Addagada.

Within corporations, the theme of governance has been one of the most important themes for decades. Scholars have discussed and debated how corporations can be governed in order to protect shareholder value and resolve agency issues. Companies have implemented a variety of corporate governance mechanisms to ensure effective decision-making and risk management. These include board committees, independent directors, audit committees, and independent auditors. Companies have also implemented internal controls to ensure efficient and effective operations.

Effective corporate governance involves careful oversight and accountability of organizational assets to promote a company’s long-term success, ethical behavior, and responsible management. These assets can include financial assets, physical assets, human capital, and technology assets to name a few. However, starting in the 1970s, information and data assets were viewed as assets determining and influencing company value. Since then, data has been considered a valuable asset that must be managed and protected to ensure availability, accuracy, and security. Moreover, companies must also consider the impact of data on their organizational strategies and goals. It is a known fact that by using data-driven business models, a corporation can transform itself to profitability by understanding the markets and internal environment better.

For organizations to protect their data through a good “corporate governance framework,” a framework that provides guidance is necessary. The term corporate Data Governance differs in principle from the usual Data Governance. In accordance with corporate governance objectives, Data Governance frameworks should contribute to market integrity and economic performance. An overarching domain of managing data risks can also contribute to corporate Data Governance. Corporate Data Governance prioritizes value, however, and identifying risks is crucial to determining the effectiveness of the internal data control environment. For the enterprise and its stakeholders, the framework must enable organizations to assess, direct, monitor, and protect data and related infrastructure. The direction provided by corporate Data Governance can be different in scale and in its objectives to a generic Data Governance framework.

It is both an opportunity and a challenge for corporate governance as new technologies such as big data platforms and cloud platforms digitize and process data at scale in firms. The pandemic has also intensified the need for digitizing data and improving accountability in organizations. Thereby, data availability in firms started to increase, which has become a strategic asset to drive the firm’s valuation. A growing amount of data combined with insignificant and poor-quality information has been a challenge for large corporations for years. In 2008, by conducting a survey of 200 organizations across the globe, Pierce, Dismute, and Yonke stated that 58% recognized data as a strategic asset. The management of data as information and its intelligibility has become a high priority in corporate governance and regulatory compliance. 

Data Governance is generally defined as the allocation of roles, decision-making rights, and accountability relating to data assets. The goal of Data Governance is to ensure that policies and ownership of data are enforced within an organization. It focuses on formalizing data operations as well as roles, responsibilities, and accountability associated with data. Moreover, Data Governance provides professionalism to better manage data, which is often lacking. In order to govern data, a set of change management activities can be implemented that influence the continuous development of the organization.

Furthermore, data as a corporate asset must be protected and reviewed within the context of the data control environment, in addition to being governed by accountability. It is common for Data Governance frameworks to describe how to define activities that govern data in a generic way. There is more emphasis in the frameworks on “defining” how to manage and govern data, and less importance attributed to the “assessment” and “monitoring” domains. Data Governance must also emphasize the central role of business executives. The formalization of managing data through corporate Data Governance can improve transparency, accountability, responsibility, independence, and fairness.

A well-governed data ecosystem is characterized by four constructs: structure, process, participants, and success. In addition to the four constructs above, in the present day, it is also necessary to identify the relationships between activities in Data Governance decision domains with respect to validation in implementation environments, including risk management, responsibility, and accountability, as well as inter-organizational coordination to achieve goals.

Organizations go through strategic changes internally. As organizational contingencies change, a contingency-adaptable framework helps configure corporate Data Governance accordingly. Often, two domains will have to be adaptable to organizational changes: organizational structure of Data Management activities and the placement of decision-making authorities related to data. Contingencies affect Data Governance and a Data Governance configuration is specific to a given company. A corporate Data Governance model includes a combination of contingency factors, design parameters, and outcome parameters. The contingency factors influence the design of the Data Governance operating model as well as its outcomes in the form of benefits to the shareholders. Common contingencies include:

  1. Firm size
  2. Structure
  3. Competitive strategy
  4. Decision‐making style
  5. Ownership dispersion
  6. Data Management as a service

The contingency-based Data Governance model directs the organization to understand the interrelationships within and between divisions and functions. It emphasizes the multivariate nature of enterprises and attempts to interpret and understand how they operate under varying conditions.