The Data Governance Terminology Dilemma

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The Data Governance Terminology Dilemma

Data Governance Definition

Depending on who you ask, the definition of “data governance” varies. Companies customize it based on their specific business priorities. Governance is defined by software suppliers in accordance with their product capabilities. Some definitions are still influenced by legacy top-down governance approaches while emerging collaborative governance is gaining traction. Even the selection of centralized, decentralized, or hybrid models results in unique distinctions. With so many considerations at play, there is no such thing as a one-size-fits-all concept of data governance. While language variances will likely continue, a common vision with flexibility allows data governance to evolve in tandem with the demands of both the data and the business. Defining what data governance means for your organization is the first step to aligning your program, approach, and resources to drive business value.

Data “Stewards”

The function of a data steward is critical for effective governance, although responsibilities vary greatly. Some firms employ full-time stewards, while others rely on part-time business or IT personnel to divide the workload. When referring to business partners who provide input, some people use the term “data steward” loosely. Stewards could concentrate on technical metadata or commercial context. It’s difficult to plan stewardship or set expectations when roles and time commitments are changing. Before implementing a governance operational model, businesses should first agree on the formal steward definition, beginning scope, and role expansion strategy. Even starting small, having a consistent basic understanding of the steward mission will allow for a successful program.

Data Owners & Ownership

The notion of “data ownership” has muddled many governance initiatives. It implies absolute control, including the ability to alter or destroy data. No individual in an organization actually owns the data. This discrepancy between the perception and reality of ownership opens the door to conflicts. Many organizations will refer to a data owner as a data champion or custodian, but some organizations will leverage this terminology for their formal data stewards (confusing, isn’t it?).

Emphasizing responsible data decision-making rather than possessive ownership can realign mindsets. Ultimately, semantics matter less than instilling a collaborative dynamic between business and governance around stewarding data for organizational trust and impact.

Data Literacy

The term “data literacy” has sparked some confusion in recent years given its varied interpretations. For some, data literacy means an individual’s or organization’s overall understanding of and capabilities with data. It encompasses the impact, importance, and activities related to data. However, literacy can carry a potential negative connotation, implying that those without data literacy are illiterate on the topic. To avoid this, some prefer alternative terms like “data confidence,” “data maturity,” or “data enablement.” These capture a more progressive building of data-related skills and positive mindsets. Regardless of terminology, it’s clear that both organizations and individuals need to continuously develop their data proficiency. This may include data governance basics, understanding the value of data, analytical knowledge, and ethical data usage.

Although terminology, definitions, approaches and models vary from organization to organization, this is what makes data governance unique. The need for standards in a practice that strives on flexibility and customization to business objectives presents an interesting challenge. However, with strong foundational principles, data governance education, a focus on business outcomes, and adaptability to evolve with strategic shifts, data governance can provide the necessary structure while accelerating progress.

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