There are many steps involved in buying data governance tools. Obviously, getting approval and funding for a purchase and then evaluating products are major ones, but justifying the need for the software is the first step. Part of that process includes understanding what you need the technology to do, as there's a wide range of functionality within the realm of data governance tools. It's also imperative that you ensure that your organization is ready for a governance tool. If it isn't, the software you purchase likely will become shelfware.
Let's first take a look at a few different scenarios where these tools are often used. If your company can identify with any of the following use case examples, you're on a trajectory to acquiring data governance software.
Your organization is in a highly regulated industry. The majority of data governance programs in recent years have been initiated because of increased regulatory oversight, which creates the need to establish formal, auditable oversight of data internally. An organization in this position should be looking to better manage regulated data and trace its use and accuracy in order to avoid compliance issues and reduce the costs of compliance efforts. Therefore, governance policies need to be defined and programs established. Regulatory drivers also imply a need to implement workflow processes and controls.
In addition, documentation of data lineage is required, and often, organizations need to develop glossaries of critical data elements that are regulated -- knowing where to find documents helps lower compliance-related data discovery expenses. Taken as a whole, these various needs touch on all three categories of data governance software: program and policy management tools, data quality tools, and tools that support traditional data management functions. If your data governance program is being driven by compliance requirements, you can be assured that your company is a good fit for governance tools.
Your organization needs to create a master data "golden copy." There are various scenarios where an organization must consolidate all of its data on a key subject area or topic to achieve a business goal -- or simply to sustain its market position. The need to make business data consistent and drastically improve a lot of bad data habits embedded in business operations arises in association with initiatives such as next-generation enterprise resource planning efforts, streamlining of research and product development cycles, and deeper marketing interaction with customers.
For example, companies can use commonly defined data elements to enhance development workflows and reduce time-to-market on new products. Improvements in customer data quality will hopefully increase retention and, ultimately, market share. Clearing up semantical differences across business functions triggers the need for common data definitions and descriptions, standards, lineage and reference data. These efforts point organizations at data quality and traditional data management tools to support data governance, reference and master data management.
Your company uses advanced analytics and big data technologies. It seems that every corner of IT intersects in some way with analytics, and data governance is no different. The scenario applying to data governance software is simple: In spite of the inherent benefits of the current generation of advanced analytics software, most organizations discover they don't have a strong grasp on common data meanings, data access standards, adequate privacy protections or even a reasonable means of navigating the various data stores that hold information for analysis. Data governance is crucial in developing those aspects of an analytics program, particularly the essential governance functions within the traditional data management category -- for example, data definitions, lineage and privacy.
Are you ready for data governance software?
Even if you can identify with the above use cases, you also must ensure that your organization is ready to use a data governance tool, as readiness is a huge factor in the decision-making process and the success of a data governance program. Too often, tools are acquired well before they're needed, or the organization isn't prepared to successfully deploy the software it buys.
You should assess: Why is the tool desired? What are the business drivers of the planned purchase? In addition to deployment, are you prepared for training, end-user support and monitoring of the tool's usage? While many components of a data governance program will evolve over time, several preparations need to be made before tool acquisition is a smart move.
Another key aspect of readiness to buy data governance tools is being well aware and informed of both internal functionality requirements and the capabilities offered by different data governance vendors. Vendors are often criticized over their products lacking functionality that they never intended to support in the first place, because it's addressed by other tools or not part of the data governance functions provided by the vendor.
No one size fits all on data governance
Understanding the specific scenarios where data governance will provide business benefits to your organization dictates functionality requirements, which then help determine which tool category -- or categories -- you should be looking into. You'll then need to either prioritize your requirements or consider acquiring multiple tools. While most vendors strive to provide a full spectrum of data governance support, or partner with others to achieve that, there's currently no one tool that will meet all data governance requirements.
Many companies look for tools that support administration of a data governance program and provide workflow and glossary functionality; they then integrate that software with existing data modeling environments. Alternatively, many potential buyers of governance tools will consider upgrading their current data management tools and incorporating newer data governance features into their most recent releases.
You also have to be ready to do some explaining to corporate and business executives who are weighing possible purchases of data governance tools. For example, if automating workflows is important to your proposed data governance program, you need to be able to clearly explain what workflow really is and what tool-supervised collaboration entails. Many organizations don't truly understand how to make effective use of workflow and collaborative mechanisms.
Similarly, if a data glossary is a key piece of your governance plan, it may be necessary to explain the importance of a common data context and the distinctions between data as fact and data as reference. Data lineage issues drive much of the glossary and semantics management requirements for data governance tools. Or if data quality is a priority for the data governance team, you should be prepared to explain the importance of tracking and managing remediation of data quality issues.
Selecting a data governance tool is a multidimensional process, as there's no simple path to matching functions to features. Only after you understand how the tool will be used and the value you need it to provide can you examine the various categories of tools that can meet these needs.
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