Unified Data Governance: The Key to Greater Visibility

The past few years have been transformative, with global events reshaping our personal and professional lives. From a rapidly shifting regulatory environment to volatile economic conditions, today’s business leaders are faced with challenges unlike any in recent memory.

In the midst of this turbulence, there has been a pronounced shift toward data-centric strategies. Economic pressures have prompted business leaders to cut costs. Labor shortages have challenged business leaders to run lean. Uncertainty has generated interest in greater agility and resilience.

Together, these forces have pushed companies to accelerate the shift to technologies like Cloud, AI, and workflow automation. In the context of this change, business leaders recognize the pressing need for data-driven decision-making. Without data integrity, however, initiatives to enable data-driven decisions will fail to meet expectations.

What is data integrity? Data integrity describes a state in which data has maximum accuracy, consistency, and context. That, in turn, enables confident decision-making.

Data integrity is a journey, and that journey is different for every company. The path you take depends on your organization’s strategic priorities and the specific business initiatives that drive them. As you strive to achieve higher levels of data integrity, data governance becomes imperative.

What is Data Governance?

Robert Seiner, author of Non-Invasive Data Governance and founder of KIK Consulting, defines data governance as “the execution and enforcement of authority over data.” That may sound a bit harsh or restrictive, he acknowledges, yet it doesn’t necessarily imply a heavy-handed approach involving excessive control.

Rather, Seiner says, data governance is about aligning people, processes, and technology in ways that best serve the organization. Governance also calls for formalized accountability, as well as mechanisms to ensure decisions are made at the appropriate level within the organization.

Consistency in unified data governance is also essential, according to Seiner. Organizations must govern their data (including master data) as well as metadata. But they also need to govern the way people interact with data. That means creating formal definitions around various roles within the organization and clearly defining the relationship between that role and the various datasets throughout the company.

To do so requires data governance teams to formalize processes, including who has the right to view the data, create new records, or change existing data. Those processes require an enforcement record as well. This is especially important in light of the stringent legal requirements that have emerged in recent years. The privacy and security of data, as well as data sovereignty rules and retention requirements, make it essential that organizations gain control over processes that touch upon their data in any way.

Labeling Your Data Governance Program

Many organizations struggle to frame data governance in terms that stakeholders can clearly understand. The phrase “master data governance” is common in many companies, for example, but the term can be interpreted in multiple ways. It can refer to a mastery of data governance, or to the governance of master data.

The problem of incomplete or inaccurate labeling can apply to data sets as well. Someone at a university, for example, might refer to “students,” yet that doesn’t necessarily provide a precise description of the dataset in question. Very often, it’s helpful to add a contextual qualifier. For example, we might be concerned with “active students,” “on-campus students,” or “undergraduate students.” Contextual labels are very important.

The same principle applies to data governance programs. To the extent that data governance encompasses business rules, data privacy and security, and regulatory compliance, it can be tempting to opt for two or more distinct governance initiatives to address data sets with disparate requirements.

A one-size-fits-all approach is not as efficient or as effective as a dedicated approach for each category of data assets. Multiple data governance programs introduce complexity and add administrative overhead. They require additional team members, tools, and processes. They can also result in duplicative effort and a lack of standardization.

Separate data governance programs can also lead to a siloed approach that may stand in the way of efficient data access or make it difficult to map datasets that span multiple domains.

Data Governance as a Multi-Faceted Diamond

On balance, distinct data governance programs tend to be costly and they make it difficult to leverage data to generate business value effectively. A single, holistic approach, on the other hand, has numerous advantages, provided that leaders within the organization understand the multiple facets of an effective governance program.

That holistic approach must be built on a framework that takes into account the various facets of an organization, its people, and its processes. Data governance leaders must consider the needs of various stakeholder groups, from executive level to operational and support staff. They need to consider the various roles, business processes, technology tools, and metrics important to the organization.

Data governance leaders should also consider geographical, divisional, or industry-driven distinctions. Each of these implies different sets of requirements with respect to data security and privacy, regulatory compliance, and business requirements.

Companies that aim to consolidate multiple data governance initiatives, according to Seiner, should perform a comprehensive assessment of existing governance programs and identify common elements to create a starting point for the consolidated program. Next, they should develop a unified vision for data governance that serves the strategic and tactical needs of the organization. That can serve as the foundation for a common data governance framework, with clear roles and responsibilities for the key stakeholders charged with moving the process forward.

Precisely’s Data Governance service offers a comprehensive framework for managing data policy and processes, enabling deeper insights into the meaning, lineage, and impact of an organization’s data.

That translates to confidence, enabling data leaders to automate governance and stewardship tasks, bringing the organization’s people, processes, and technology together in ways that maximize the business value of data.

Want to learn more about unified data governance? Read our free ebook, Steps to Improved Data Governance – A business-first approach.