Kentucky Finance

Jul 4 2018

Advanced Data Governance

#data #governance #tools


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Although there are numerous areas of focus and a variety of ways to implement Data Governance programs, some of the most fundamental prerequisites for such a program include:

  • Definitions: Determining what attributes key data terms consist of, and providing a degree of consistency in how those terms are used and how the data that represents them are governed.
  • Roles: Designating who is supposed to be doing what with an organization’s data, including various levels of access and responsibilities that each member of the enterprise has in regards to the different types of data it ingests and produces.
  • Hierarchy: It is invaluable to begin with a Data Governance Council, assign stewardship positions, and construct a hierarchy of positions for effecting governance.
  • Processes: Perhaps the most rudimentary point of any fledgling Data Governance program is to define and refine the processes that data goes through so that it is cleansed and trustworthy, and deserving of influencing decisions and processes critical to business and operations.

Thus, at the novice level of Data Governance programs, the objectives is to regulate the process by which data is taken in and disseminated so that they are trustworthy, secure, and ideally not subject to issues of discoverability and regulatory violations.

For organizations that are able to master these fundamental points, however, there are a new host of additional concerns that need to be addressed to not only validate existing governance programs and the quality of the data they facilitate, but ideally to provide tangible, positive business outcomes for the organization. According to Forrester :

“By not incorporating outcome results of using derived data into our data governance efforts we miss the point of why we govern data. What matters is not that data is used, or shared, or trusted. It matters what the results are by using the data.”

In order to ensure that governance programs are not only making data secure but also enhancing an organization’s mission and vision objectives, it is vital to incorporate governance responsibilities into the hands of the business as much as possible, to utilize critical selection criteria for selecting governance tools and infrastructure, and to provide an objective means of evaluating how effective governance is in fulfilling business objectives.

Because of its correlation to security and sophisticated IT processes, Data Governance has traditionally been conceived of as the domain of IT departments. Although there are numerous tools in existence that allows IT to function as an overseer for governance on various mobile devices and self-service platforms. elite Data Governance increasingly requires the business to shoulder more of that governance responsibility.

Doing so, of course, is an almost necessary prerequisite for ensuring that governance revolves about business goals (which requires initial governance objectives to follow in accordance with business objectives when first constructing a governance program); Gartner’s very definition of master data is “a minimal set of data attributes representing the conceptual objects that business users use, and that are widely referenced in the organization’s most important business processes, analytics and activities” (White and Saul, 2014).

The crux of incorporating a business first approach to Data Governance is to extend the capabilities of governance beyond one of trust to one of overall organizational strategy. Governance should ideally be designed as a way to further those strategic objectives. Involving the business in the roles, responsibilities, and the procedures of Data Governance helps to ensure this goal and creates greater possibilities in which governance is actually fulfilling the overall aims of the enterprise.

Quite often, organizations with Data Governance programs rely upon conventional Data Management tools as a means of effecting governance. Indeed a good number of these tools, such as single or multi-domain Master Data Management tools, are created with substantial capabilities to ensure governance according to predefined definitions and rules.

However, the potential shortcoming of these tools is that they are somewhat shortsighted and focus on governance at the tactical level instead of at the strategic level. The ideal is to utilize tools that provide their own Data Governance applications and are aligned with strategic goals as such—which makes them ideal for facilitating the businesses in its new role as a data steward. As previously mentioned, there are numerous vendors that offer data management tools with governance capabilities such as Business Intelligence vendors like SAS or Information Builders. Vendors such as SAP. IBM. Informatica and others have a host of tools with governance capabilities from a variety of different products, yet lack the singular unifying platform that can provide overall strategic alignment of Data Governance.

Those coming the closest to offering such a platform include Global IDs and Collibra, the latter of which was identified by Forrester as provisioning “an inmarket tool that provides a data governance 2.0 environment specifically for data stewardship and operations.” The Forrester Wave: Data Governance Tools. Q2 2014 report identifies IBM and Informatica as in the process of integrating their governance products into a unified platform. More importantly, it defines the goals of such tool as being business friendly and accessible, and capable of providing enforcement of governance from a strategic perspective to further an organization’s goals.

Perhaps the most important aspect of elite Data Governance is measuring the results of such governance. Much like every other aspect of the enterprise pertaining to data related processes, Data Governance should actually enhance them and help to achieve business and organizational objectives. A part of this function of Data Governance involves creating action that is aligned with those objectives. It is equally vital that organizations have some way of measuring the results of governance—whether that means reducing regulatory or discovery issues, increasing the number of customers or revenue, or expanding the business in tangible ways. The specific way that an organization is able to measure the results of its Data Governance program will vary according to its particular aims. Yet it is of immense value to attempt to measure (if not quantify) those results. Those that are less than what are expected or needed will provide areas for improvement in the governance process.

Additionally, it will be useful to measure the output of Data Governance programs in terms of business objectives by helping to codify data in two different ways. The first of these is in-system data or data prior to its subjection to governance procedures, while the second is data that has been subjected to governance protocols and functions as a means of providing insight to the business. Analyzing data according to these two stratifications can provide greater sapience into the relationship between governed data and the results of that data, by simply indicating how the governance process has altered the data and its usage.

Graduating from the Basics

With Big Data a reality and the purveyor of the burgeoning Internet of Things. Data Governance is no longer a luxury for enterprises and is a crucial prerequisite for engaging in business processes in today’s data driven world. It is pivotal that organizations have some semblance of the basics of Data Governance—which involves taking active measures for data quality, lifecycle management, metadata concerns, stewardship, privacy issues, and security concerns.

However, in order to remain competitive and to ensure that governance programs are adding value to the enterprise—in much the same way it is necessary to ensure that other facets add value—it is essential to align governance with business objectives, and provide the means for empowering the business to take a more active role in governance. Ideal governance requires more than mere Data Management tools, and should incorporate a unified platform that one can extend throughout the enterprise. Developments in governance tools are gradually reflecting this necessity. Incorporating these measures assists the enterprise in determining quantifiable results from governance programs, which should operate at the core of their business strategies.

White, A. Judah, S. (2014). Gartner’s three rings of information governance help you prioritize different types of data. www.gartner.com .


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