Key Considerations for Selecting Data Governance Tools

Author: Corey Biehl

Data governance, when implemented effectively, is a transformative capability enabling organizations to effectively streamline operations, drive revenues, meet obligations, and improve customer experiences through data. It involves the alignment of people, process, and technology to maximize and protect the value of organizational data assets.

The first step in data governance is to establish the processes and procedures to effectively manage data in an enterprise to ensure accuracy, fidelity, consistency, and completeness. Once the processes are established, you can turn your attention to the enabling tech capabilities that can accelerate, automate, and streamline the operational aspects of data governance.

There is a myriad of tools in the marketplace with defined capabilities to support data governance objectives. Typically, an enterprise requires a collection of core functions to enable a holistic capability. Understanding the capabilities – and being able to reconcile your specific use cases, business priorities, and enterprise’s maturity – will enable you to make an informed decision on the vendor and tool to start with in your data governance tooling journey.

Data governance tools help to streamline the processes and procedures to ensure data is well-defined and understood; owned and managed; cleansed and usable; and accessible in a timely and appropriate manner.

Core Capabilities for Holistic Data Governance Tools

When evaluating data governance tools, the common core capabilities required for holistic governance include:

• Policy Management: Govern with established standardized processes and consistent policies. Helping to align your organization around data, policies may include: data retention, usage / application, access / permissions, personal data handling, etc.
• Data Quality: Improve your data-driven decision making by assessing and cleansing your data assets. Keeping data complete and accurate as it moves through the organizational value chain is key to promote trust in data-driven actions and decisions.
• Master Data Management: Increase operational efficiency and standardization in data across the enterprise. Having standard definitions for your organizational “nouns” (e.g., Customer, Product, etc.) is vital for holistic, 360-degree views of your business.
• Metadata Management: Promote a data-driven culture by making data easily understandable and accessible to the people who need it across your business. Knowing what data is available, what it means, and where to find it is most often the biggest challenge for citizen data workers.
• Data Security & Privacy: Retain customer trust and safeguard your data assets. How effectively you protect your customers’ (and other organizational) data can have a monumental impact on your business – from protecting revenue (brand reputation) to avoiding costs (regulatory obligations and penalties).

[Read More: Creating a Single Source of Truth with a Governed Data Lake]

Key Considerations

It can be an overwhelming task to select the right tools for your organization. Solutions range from all-encompassing product suites to niche tools for specified purpose. To maximize the value of your investment and pick the right technology for your situation, the below questions should be answered:

1. Business Challenges: What current pain points can be solved with data governance and what are the impacts to the business?
2. Scalability and Fit: What is the business strategy for the next 12- to 18- months? Does data governance (and the related investment) fit with where we are going?
3. Direct and Tangible Benefits: What are the net positive soft and hard benefits for the enterprise? How can we qualify such an investment?
4. Available Budget: How much can we invest – direct cost and resource support – for these tools?
5. Organizational Maturity: What steps should we take in advance to achieve value from our investment? What are the change management considerations needed to promote effective understanding and usage of these technologies?

As you begin to glean the answers to some of these questions, the required capabilities may become clearer. Based on our experience, an effective implementation could be sequenced as follows:

• Prioritize: Assess the business challenges that exist and tie to specific capabilities to help resolve. If there are multiple capabilities required, score the capabilities, or needs against business challenges to down select priorities.
• Finalize: Once you gain agreement on the capabilities, focus on core requirements, and sequence based on needs vs. wants and nice-to-haves. Bringing in key stakeholders who will be end users or consumers to validate is a useful exercise to ensure benefits are recognized by multiple business groups versus a singular stakeholder.
• Evaluate: Perform a vendor selection process to evaluate tools and fit based on business challenges, scalability and fit. If possible, stand up a light proof-of-concept to confirm compatibility in the current ecosystem.
• Acquire: Based on how tools/vendors perform in the prior phase, begin negotiation on the commercial aspects for licensing and maintenance. Work closely with procurement to ensure your business is leveraging relationships and discounts where applicable.
• Pilot: Once a tool is selected, begin the implementation and configuration phase. Identify specific use cases to highlight specific capabilities tied to the business challenges identified in the assessment portion of the project. Prove out the technology, refine the processes, then expand to other use cases.

In our work, we have the privilege of working with many of the leading tools across the data governance spectrum in a variety of situations. Our experience has driven home the importance of starting with understanding your organization’s current state and vision – that is, all of the considerations listed above – in order to drive business value quickly through your data governance efforts.

Corey Biehl is a technology leader in RevGen’s analytics and insights practice. He is passionate about designing and developing data & analytic solutions that make a difference.