How To Choose Data Governance Software

Published on September 20, 2024

Selecting the right data governance software is a critical decision that can significantly impact your organization’s efficiency, compliance, and overall data management strategy. A misstep in this process could lead to wasted time, financial loss, and operational setbacks.

Once you've gathered a comprehensive list of requirements through stakeholder discussions and internal evaluations, it's important to approach the decision strategically. Below, we outline key factors to consider when evaluating data governance solutions, along with common pitfalls to avoid, ensuring that your choice aligns with both your immediate needs and long-term goals for data management success.

Data governance software: key considerations

Ease of use and user adoption

The success of your data governance program will depend on how easily the software is adopted by end users. Choose a solution that is intuitive and easy to navigate, as this will play a big role in ensuring widespread adoption and maximizing ROI. User-friendly software encourages consistent use and engagement, which is critical for long-term success.

If you can “try before you buy,” take advantage of this before purchasing the software. This will enable you to gather feedback from your key stakeholders to validate the tool's usability and generate support for its deployment.

Budget and scalability

Budget constraints often play a major role in data governance software decisions. To stretch your budget and qualify more vendors, break down your requirements into immediate and long-term needs. Start by focusing on what’s absolutely necessary to demonstrate value in the first few years. For example, determine how many users and data sources are required within the tool in the initial phase and plan for future expansions. This approach helps you minimize upfront costs while proving ROI early, securing additional funding for further licenses and expansions down the road.

Deployment options: SaaS vs. self-managed

Another key factor is whether to opt for a SaaS solution or manage the software in-house. SaaS offerings generally have lower upfront costs and are easier to maintain, as they don’t require additional computing resources or personnel to manage updates. On the other hand, self-managed solutions offer more control but can be resource-intensive (particularly as the size of your data landscape grows). Consider the long-term impact on cost and maintenance before making a decision. In many cases, SaaS is more cost-effective without sacrificing security.

Vendor partnership and support

When selecting data governance software, it is important to evaluate not only the product’s features but also the vendor’s ability to act as a long-term partner. Implementing such software is often not a plug-and-play process, especially if your infrastructure includes legacy systems or custom-built applications. In these cases, a strong partnership with your vendor can help bridge gaps and provide creative solutions during implementation. To assess this, ask for customer references within your industry. Vendors are usually willing to connect you with existing customers to share their experiences and success stories.

Expert services and best practices

Check if the vendor provides expert services as part of their offering. Implementing a successful data governance program requires deep industry knowledge and best practices. Vendors with expert services can offer valuable insights into what strategies work, helping you to achieve faster value and ROI. This can be especially useful in programs that heavily rely on people and process changes, where the right guidance can make a significant difference.

Integration capabilities

Ensure that the data governance software integrates smoothly with your existing data intelligence platforms, such as Business Intelligence tools and data quality solutions. Seamless integration will enable a more unified approach to managing, analyzing, and governing your data, which can lead to greater insights and operational efficiency.

Alignment with business needs and future growth

Begin your evaluation with a list of prioritized requirements, focusing on your immediate needs. However, don't just assess the software's current capabilities—consider its future roadmap and how it aligns with your long-term data governance strategy. Engage with the vendor to understand if their product development plans match your evolving priorities. This future-proofing will help ensure the software continues to meet your organization’s needs as it grows.

Cutting-edge technology

Finally, consider whether the software leverages advanced technologies like Generative AI to enhance user experience and efficiency. Tools that incorporate AI can significantly streamline tasks, making it easier for users to search for and access the data they need. This can be a major differentiator, especially in fast-paced environments where time is a critical resource.

Common mistakes when selecting a data governance solution

Overlooking people and process

One of the most common mistakes in implementing data governance is assuming that purchasing software alone will solve all governance challenges. Data governance requires more than just tools; it necessitates a strong focus on the people and processes that support the program. A successful governance framework is built on the active participation of employees who understand the importance of following proper protocols and using the software to enhance—not replace—their governance responsibilities.

Not engaging your key stakeholders

It’s essential to recognize that data governance software should complement, not eliminate, the need for human oversight. Establishing governance protocols and processes requires active collaboration, discussion, and stakeholder buy-in.

Governance software cannot replace the critical human elements of decision-making, approvals, and accountability. Involving people in the creation and implementation of these protocols ensures alignment with business goals and a more effective governance program.

Trying to do too much, too soon (and deviating from the plan)

A common pitfall in implementing data governance programs is deviating from the original plan. It is important to stick to the list of prioritized requirements that you established at the start. Trying to implement or automate every feature immediately can overwhelm your team and resources. Focus on addressing your most critical needs in the first year, and gradually build out the program over time.

Over automating governance tasks

Automation can enhance efficiency, but automating processes for the sake of automation can be counterproductive. Not all aspects of governance should be automated, especially those that require human judgment and careful oversight. Always evaluate whether automating a task adds real value or risks losing important human involvement.

How Alation can help

Alation is a data intelligence platform that helps organizations find, understand, and manage their data assets. Alation makes it easy to govern data by capturing how data is being used and the applicable policies and data controls. Alation helps organizations take control of their auditing and regulatory processes, and establish governance processes to ensure that data is trustworthy.

Curious to see how Alation can help you implement data governance that’s good for business? Book a demo with us today.