Re-Imagining Data Governance

Re-Imagining Data Governance

Kevin J. Sweeney (Stats NZ, New Zealand)
DOI: 10.4018/978-1-5225-3725-0.ch014

Abstract

Contemporary business environments reflect the growing influence of data as a mission-critical resource of relevance across the enterprise, suggesting a need for robust infrastructures to enable good data management practice. This includes data governance, a particularly foundational infrastructure with a crucial role to play. Data governance models in common use however, reflecting traditional top-down, hierarchical structures, and relying on designated governance roles, are not equipped to effectively embed data accountability within dynamic business environments. In response, this chapter offers a new approach designed to foster accountability by cultivating data knowledge and promoting good data management behavior amongst all relevant staff. Drawing from an operational data governance framework developed for New Zealand government, the new model employs a core set of capabilities and a steady states model to map data flow. It provides a deliberately business-centric view of data accountability and offers a means of maturing data thinking to support improved integration across operating scales.
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Introduction

Almost it seems while we weren’t looking, the world around us has changed.

Far reaching developments in technology, transitioning for all intents and purposes from the stuff of lofty ideas to indispensable components of our daily lives, continue to emerge at a profoundly rapid rate, undermining our ability to leverage traditional means of informing our choices and values (Allenby, 2015). And all the while we accept this state of flux as something altogether normal.

Fuelling that accelerated pace of change and shaping our opinions about the possibilities of technological disruption, is a vast network of data, sometimes visible, often unseen. Driven by interactions between producers and consumers, directed by the gravitational forces of sources and sinks, coursing in a myriad of ways through a complex labyrinth of connections, data streams continuously through its channels as the lifeblood of our modern world. And much like the technological tools that we employ for everything from managing an investment portfolio to locating the closest cup of coffee, the underlying data that we leverage for our varied purposes has thoroughly permeated our personal lives.

So too in business contexts, the proliferation of data and subsequent dependencies on it have recrafted operating environments, leading to a landscape of playing fields reflecting new sets of rules and novel realities, but also rife with possibility. Success in contemporary business environments is dependent more than ever before on the ability to harness and leverage the vast potential of data, on a willingness to adopt a sufficiently aspirational strategy that is nonetheless founded on solid data management principles and delivered through appropriate organisational infrastructure.

In response, this chapter presents a new approach to a particularly critical infrastructure, data governance, designed to better equip organisations to manage increasingly important data assets in the face of unconventional and rapidly changing operating environments.

Based on a Data Governance Framework developed for New Zealand government, the proposed model turns traditional governance on its head, to mature thinking beyond a longstanding bias that favours hierarchical and decidedly top-down execution. Emphasising instead elements of a bottom-up implementation that cultivates deeper levels of data knowledge and improved principles-based data management behaviours, it accentuates the role and relevance of governance within the operational contexts of line staff. This distinct shift in thinking about the purpose of governance addresses existing capability gaps and serves to embed data management good practice and accountability as an inherent, business-as-usual outcome across all parts of the enterprise.

Key Terms in this Chapter

Administrative Data: Data collected as a matter of course by an organization for the purposes of supporting the normal business operations for which it is responsible. It contrasts with survey or research data which is gathered to answer a particular question or need.

Value Chain: A business management concept or analytical model where production is viewed as a sequence of inputs, transformation processes, and outputs. Each transformation process adds value, which accumulates to the final product.

Asset: A business resource with strategic, economic, or intrinsic value that an organization manages with the expectation that it will provide future benefit.

Accountability: Assuming a transparent and appropriate level of responsibility for data assets that are under one’s care, which includes honoring obligations associated with good practice.

Capability: A measurable capacity to use resources that an organization needs to deliver to its strategy and achieve its agreed outcomes. It includes elements of people, process, information, and technology.

Governance: The framework of rules, norms, and accepted practice established as an organizational infrastructure to enable strategic outcomes, accountability, transparency, oversight, and the management of data, risk, and relationships.

Data Management: The full lifecycle care of organizational data assets, through the implementation of accepted good practice, to develop and maintain their value.

Data Flow: The lifecycle movement and storage of data assets along business process networks, including creation and collection from external sources, movement within and between internal business units, and departure through disposal, archiving, or as products or other outputs.

Data Lifecycle: The complete set of development stages from creation to disposal, each with its own characteristics and management responsibilities, through which organizational data assets pass.

Steady States: Those stages in the value chain where the output of a process satisfies a set of pre-agreed quality indicators. In a business context, data asset decision points where an agreed set of quality criteria are satisfied.

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