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Data governance is not a business issue, nor a technology issue. It is not a local issue; it is a national and a global issue. The quality, relevance, timeliness, and integrity of data often has a significant and immediate impact on employees who make data-driven decisions, small or large, in an organization. The collective impact of decisions made by individuals at different levels in an organization in turn affects the competitive positioning of an organization and its ability to do well in the global marketplace (Khatri & Brown, 2010). Therefore, critical issues relating to data and the governance of data are core business issues that must be addressed at the highest level of an organization. For organizations that are international in scope, this must be done on a global scale with seamless integration between systems, people, and processes around the world.
Several integral concepts of data governance have been a part of the IT landscape for decades (Panian, 2016). However, today powerful and dynamic macro forces are at play, resulting in a dramatic shift of the established view of data and its relationship to decision-making, privacy, security, risk, compliance, and governance. In short, our understanding of data, its value, and its power to influence and shape the future of organizations and even societies around the world have undergone a significant makeover because of technological and political factors. First, the volume of data being generated has exceeded the ability of humans to comprehend the world in which we live. The complexity, volume, and velocity of data has made it imperative for organizations to address data challenges head on (Marr, 2018). Second, the value of data as an asset is no longer an academic discussion, but one that organizations and governments around the world are directly grappling with. For example, public health experts rely on the global exchange of large volumes of complex data from a multitude of legacy and proprietary systems. If data has monetary power, then it deserves to be closely monitored, controlled, and leveraged (Tallon, 2013). Third, while the above two factors have the potential to deliver tremendous benefits to an organization, there are great risks as well, including security and privacy violations, data manipulation, and massive algorithms that can fail with catastrophic consequences.
Large volumes of data from multiple sources managed by massive algorithms can trigger important decisions with lasting impact for entire societies. Global companies must navigate the intricate challenge of navigating social, political, economic, legal, and cultural rules, regulations, policies and nuances to ensure that there are no violations that could result in public embarrassment or punitive damages. “A global data governance or ethical framework, supplemented by local memoranda of understanding that take into account the local context, is more likely to succeed.” (Clarke, 2015)
Several core concepts of today’s data governance, such as data quality, data administration, data management, data security and integrity, risk, and compliance, are not new (Begg, 2012). At the same time, the rigor, priority, and strategic expectations of these factors may vary from one country to the next. It is also evident and palpable that the planning and execution context of data and its governance has changed in critical ways around the world. Therefore, it is essential to take a snapshot of how our definition and understanding of the core concepts of data governance have transitioned within this emerging context of macro forces (Ibrahim Alhassan, (2016). This is a significant endeavor that can make an important contribution to an emerging discipline. This paper provides the foundation and framework through which an organization can create and refine the core concepts and fundamental goals that uniquely define its worldview for its data governance. The contributions of this paper to the field of data governance are believed to be unique in laying out how an organization can take a generic definition of data governance and customize it to meet its own needs. The paper also explores how the definition of data governance will continue to evolve, in both cases moving the existing definitions of data governance to the next level.