When many organizations take their first steps towards implementing a data governance program, they are usually shocked by the many facets they must consider. The motivation for starting their journey may be the result of an effort to address regulatory compliance or simply realizing the important asset they have in their data.
Regardless of the reason, as they begin to address data governance, they will be faced with several challenges. These can range from the maturity of their data management processes to the role software platforms play in enforcing data governance. While this article focuses on the role of software in data governance it will touch upon aspects of the other challenges.
Data Management and Data Governance
Data management and data governance are closely interrelated. Naturally, governance will heavily influence the approach to management. In the most basic of terms, governance establishes the goals and guidelines that management uses to create and enforce policies and procedures.
Given how closely the two are intertwined, it is understandable that certain aspects of data governance can be confusing. Consequently, some organizations do not prioritize data governance because they believe it’s only necessary for firms who must deal with regulatory compliance, or that it doesn’t apply to their organization because they already have some data management processes in place.
The emergence of applications that make it easy for non-IT trained personnel to prepare ad hoc reports, engage in data analytics, and develop business intelligence assets, have helped bring data governance to the forefront. In many of these use cases, data governance provides controls on the dissemination of information and creates basic standards to both establish norms and avoid confusion regarding the use of terminology that identifiesrelevant business concepts, otherwise known as Business Terms.
The establishment of standards and policies is followed by the establishment of methods to ensure compliance. Compliance, in this instance, means that standards are being applied appropriately and consistently according to established policies. Traditionally, standards and policies related to data were in the purview of Data Management, which typically resides within the scope of an IT department. The basis for this rationale is that IT is “traditionally” responsible for the design, development, implementation and maintenance of data stores, reports, and interfaces to both data and underlying functionality.
However, this management rationale has been brought into question with the advent of reporting and data analytic software offerings that cater to a non-technical end user. This emphasis on end user report development and analytical application usage has only increased the importance of not only naming conventions and standards, but standards for the definition and use of Business Terms.
It also highlights a fundamental change in the scope and role of both data management and governance; both data management and governance are no longer encapsulated within an IT organizational scope. Now, the scope crosses organizational boundaries sharing joint responsibilities, or as an IT “enterprise management service”.
This brings us to the discussion of the “what and how” of data governance. The “what” is comprised of the “data about the data” (metadata) that defines the scope, intent, rationale, measurement, and critical processes of a business expressed through business terminology. The “how” is comprised of identifying, tracking, reviewing, and monitoring the use of the data that provide quantifiable Business Terms.
What is Governed through Data Governance
The term governance implies the overseeing of the “control” or “direction” of “something.” The “somethings” of data governance consist of Business Terms. These terms describe a wide range of topics and items that are of interest to a business.
Business Terms influence several ancillary items such as the names of fields on forms, report field names, elements in presentations, elements in business correspondence, elements in regulatory language, application screen fields, and database fields. While Business Term may influence the naming of the elements described above, the underlying naming standards are commonly governed and managed by IT and usually do not fall within the purview of data governance processes.
Where these processes and procedures commonly intersect with data governance is when they deal with the defined business terminology. In some instances, such as master data management, only a specific subset of business terms is managed resulting in a patchwork of data governance policies and procedures.
How is Data Governed
The control and standardization of Business Terms is at the heart of data governance. The use of business terms can be governed by both corporate and governmental policies and regulations. The definition is governed so that it is widely understood and to “control” the context in which it is applied.
The use of tiered reviews for the identification, development, approval, and deployment of business terminology is the most common method used in data governance. In some cases, specific reports are created to identify term use, but they are used in review processes themselves. The scope of a governance process extends to the “elements” that “define” Business Terms and where they occur. Usually, the scope is limited to a specific subset of terms defined to be important to justify management.
It is in this context that we begin to see where software plays a key role in data governance.
Most data governance software functionality centers on the managing of Business Term definitions, which includes identifying the data elements that make up the terms, where they originate from, how they are stored, where they are stored, where they are used, and where they are referenced from. All of this is better known as metadata.
There are several sources that define metadata elements. To name a few of the most common: definition of business terms, business correspondence, business forms, and regulatory processes. Business software applications facilitate the identification of data elements that comprise Business Terms in their process-specific interfaces, the way they store data, and in the reports they provide.
Notably, software applications tend to introduce their own definition of Business Terms – at times introducing a disconnect between the application and enterprise definitions. In all fairness, business software applications are specific to one or more business processes such as accounting, general ledger, inventory management, or coordination. As such, they generally do not focus on the definition and management of metadata.
The exception to this rule is Master Data Management (MDM) software. MDM software has been described as a “slice of data governance” as its main purpose is to address the standardization of both Business Term usage and data values. Absent from its responsibilities is the management of the definition of business terminology, identification of the elements that make up a definition, and the management of the use of business terminology.
Software’s biggest role in data governance is managing metadata and Business Terms. The former, metadata management, is an implicit aspect of software applications that support the modeling of data structures. Data modeling applications generally cater to technical end users and typically do not support preparing non-technical user outputs. The latter role, managing Business Terms, is an area where MDM software provides some support.
There are several data governance-specific software vendors, however, no software solution or approach supports all data governance processes completely. Most solutions provide a patchwork of interfaces to address some of the key data governance processes.
Given the underlying complexities in the processes, many of the software offerings only scratch the surface, and in many cases, the attempt to address these complexities can introduce obstacles for the end user.
The maturity of an enterprise’s data governance processes will influence the approach to implementation and which software product will be best for the organization.
The role of software in data governance could include assisting and supporting the following processes depending on organizational maturity:
Undoubtedly, implementing the right software can play a crucial role in data governance, however, temper your expectations. No software can fulfill all possible roles necessary in a comprehensive data governance program.
There are complexities in establishing a complete understanding of the underlying data environment, especially one that utilizes different approaches to the gathering and representation of an enterprise’s data. An enterprise looking for software to augment or support their data governance efforts should first consider their process maturity and then select a suite of applications that aligns with this approach.
At RevGen Partners, we can help you navigate this challenging journey by collaborating with you, sharing insights, and providing guidance tailored to your unique data governance needs. If you’re interested in how our team of experts can work to make your data governance efforts a reality, we invite you to reach out or visit our Technology Services page to learn more.
Paul Flores is a Technical Architect with experiences across the range of data management from conceptual development to retirement. He especially enjoys crafting resolutions to intricate problems striving for both simplicity and transparency.
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