I have long had an aversion to the term, “Data Governance.” As one senior executive put it, “it sounds like ‘Data Government,’ and that can’t be good!” For analysts and data scientists who aspire to innovation and data experimentation, governance can imply restriction, limitation, and unnecessary bureaucracy. For executives operating in industries bound by regulation, compliance, and sensitivity to issues of privacy and data protection, the thought of more governance runs counter to aspirations of innovation and “data freedom.” Yet, in spite of the bureaucratic-sounding name, and in contrast to the sexy-sounding term ”Big Data,” Data Governance has become an essential requirement for any organization that aspires to derive insight and business value from their data assets. Here is the case for Data Governance, and why data governance should matter to every business executive, not only data professionals.

Data is a shared asset. It is often stated that good fences make good neighbors. This principle applies to data as well. In complex organizations, agreed upon boundaries delineate ownership and areas of responsibility. In business, responsibility and ownership is commonly delineated by product categories, market segments, geographic regions, and internally by business functions. Data ownership and responsibility does not correspond to traditional business functions and boundaries, with the result that data management presents a profound challenge for most organizations. A very high profile example, from the world of national security, illustrates this point. The 911 Commission faulted agencies of government – FBI, CIA, NSA, and the executive branch – for an unwillingness or inability to share vital security data across branches and agencies of government. The same challenges exist for any large organizations – corporations, universities, hospitals, state and local governments – that are faced with requirements to make data available for sharing. Resolution of this challenge begins with the recognition that data is a ‘shared asset’, and must be treated as such. This is why data governance is essential – as a set of practices, policies, standards, and guideposts that provide a foundation for deriving value and insight from the data that an organization maintains. In the case of national security, effective data governance establishes a set of practices intended to afford us protection.

Data is consumed by many. For many executives, discussions revolving around data management can seem abstract or arcane. There is general consensus that decision-makers want data that is credible, accurate, reliable, and hopefully predictive and insightful. But, what exactly does this mean, and how does it translate into a set of rules and processes for effectively managing data as an asset? I have found that one of the best ways to make data personally relevant to an organizational executive is through the process of ‘data lineage’, which helps an executive understand why and how any element of data is important to them. The data lineage process chronicles the ‘flow’ of data through an organization from its origin points, generally beginning with a customer or patient or sensor or outside source, through its use and consumption at varying stages along the ‘data life cycle’. This life cycle process illustrates all of the points along the way when data is accessed, used, reported, changed, or used to derive new data.

Data ownership is a responsibility. The data life-cycle process illustrates the various forks along the way where data may be transformed, or where ‘bad data’ may be propagated. These forks represent likely decision points, or points of governance, where data can be checked, validated, authorized, approved, and where there may be a transition in data ownership and responsibility. Data lineage provides a two-dimensional roadmap which helps any executive understand how data is produced and how it is consumed within an organization. Think again of the national security example and the importance of checkpoints and effective handoffs, or consider the financial services crisis of 2008 and the impact of financial services instruments that were sliced and repackaged with increased risk. The same principles apply to patient information and successful patient outcomes, and to business data and successful business outcomes.

Data is not just for specialists. Like with any specialized field – medicine, military, sports, finance, academia – data is a complex domain that fosters its own specialists with their own jargon – meta-data, master data management, ETL, Hadoop. For a line decision-maker, simply trying to understand “what the numbers mean”, a lack of common language can be a barrier between process and result. I have long heard the understandable frustrations expressed by executives and decision-makers who want answers quickly to what they believe are simple questions – how many customers do we have; will this treatment lead to a successful outcome? In response, they often encounter complicated technical explanations. What are intended as genuine explanations of why it takes longer to get an answer, or why there is not always a simple answer to some questions, can come across as excuses or evasive.

Data success requires partnership. Organizations that have been successful in developing a ‘data culture’, where data is embedded in key decision-making processes, share strong alignment and partnership between data, technology, and business professionals. Business and technology partnership was cited as the number one factor in ensuring successful adoption of data-driven initiatives according to NewVantage Partners 2016 Big Data Executive Survey. Successful organizations have developed a common language and reached common agreement on how data is organized, managed, and processed, founded on a set of data governance principles and practices. In establishing these principles, data-driven organizations build a bridge between technologists and decision-makers, whether they are line-of-business executives, care givers, or national security officials. Data Governance may not sound sexy, but sound data governance provides a foundation from which insight and value is derived.

This article originally appeared in Forbes on June 22, 2016.