Data Governance Performance Metrics: Key KPIs and How to Measure

Updated November 15th, 2024

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Implementing a data governance program has become an increasingly important competitive strategy for most businesses. Strong governance increases accessibility and trust in your company’s data, which then leads to more usage and increased revenue.
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But: how can you tell if your data governance strategy is delivering the value it should? Organizations need solid governance metrics to track the value their systems are delivering.

This guide will show you why data governance metrics matter, key metrics to track, and how these metrics improve data quality and security.

Table of Contents #

  1. What are data governance performance metrics?
  2. Important data governance performance metrics
  3. Obstacles to gathering data governance metrics
  4. How to use data governance performance metrics
  5. Conclusion
  6. Data Governance Performance Metrics: Related reads

What are data governance performance metrics? #

Data governance metrics are Key Performance Indicators (KPIs) that assess the overall health and progress of your data governance program. They measure factors such as data quality, security, compliance, availability, and usage that both define your current baseline and enable tracking the success of new initiatives and improvements.

Insight into key data governance metrics is crucial for tracking the effectiveness of governance practices. The metrics themselves act as benchmarks that track data governance performance.

You may define benchmarks yourself or use industry baselines. No matter how you define them, data governance metrics and benchmarks establish a common baseline for all your teams across your organization.

Why you need metrics #


Governing data without metrics is like driving without a map. It’s easy to get lost and waste precious time moving away from your goals.

Without a data governance metrics, you can’t tell:

  • The original state of your data estate
  • If instituting a data governance program had any impact
  • If changing your governance program resulted in the intended effect

Important data governance performance metrics #

Data governance metrics can be broken down into five major categories:

  • Data quality
  • Data security
  • Data usage
  • Data compliance
  • Data governance training

Let’s look at each area and its associated metrics in detail. Keep in mind this isn’t an exhaustive list; you may have your own domain-specific measures.

Data quality #


Data quality metrics track whether your data can be trusted and communicate your general data health to stakeholders throughout your organization.

Data quality metrics quantify different aspects and dimensions of data quality, including:

  • Accuracy: Number of anomalies, anomaly distribution
  • Completeness: % of records with all required values
  • Consistency: % of records with potential mismatches between fields
  • Timeliness: Data freshness delta (i.e., time since last update from sources)
  • Validity: % of records not in correct format
  • Uniqueness: % of duplicates detected

Data security #


Data security metrics measure how well you’re protecting data from unauthorized access, whether by malicious actors or by accident. Data security metrics help keep you and your customers safe.

Security metrics can include:

  • Number of data breaches
  • Number of data leaks
  • Estimated financial damage of security incidents

Data usage #


Data usage measures which data assets are in use and how often they’re used. Using data usage metrics, you can optimize your data by identifying low-usage data, which you can either consolidate or obsolete to save money on data storage and processing costs.

Usage metrics may include:

  • Overall rate of data access and volume of data transferred (can signify an increase/decrease in data discoverability, amount of data governed)
  • Most utilized data assets
  • Most underutilized data assets (so-called “dark data”)
  • Average storage and processing costs per data product
  • Low-value data products (i.e., high storage/processing cost but low utilization)

Data compliance #


Data compliance measures your data’s adherence to industry standards and legal regulations. Data compliance metrics let you measure your company’s performance against best practices and peer organizations. Compliance metrics also help protect you from the costly fines arising from being out of compliance with legally mandated data management requirements.

Compliance metrics may include:

  • % of company data cataloged and governed
  • % of data assets tagged
  • Number of data objects correctly formatted
  • Number of potential compliance violations
  • Rate and cost of identified compliance violations

Data governance training #


Data governance training measures how well data governance is integrated into your organizational culture. These metrics show how actively you’re promoting and educating people about your data governance procedures and related tools - a key component of data governance adoption.

Data governance training metrics may include:

  • % of people trained
  • Training experience satisfaction
  • Training assessment scores and pass rate
  • Average time between initial training and re-training
  • % of people who need re-training
  • Training cost per employee
  • Training Return on Investment (ROI)

Obstacles to gathering data governance metrics #

There are two main reasons organizations struggle to gather meaningful metrics to track their data governance:

Inadequate governance strategy. The lack of a unified data governance strategy leaves an organization in the dark about its own data assets and with no sense of a forward direction around data governance. Without a clear direction, you can’t define metrics to check that direction.

To get past this obstacle, organizations need to define their governance strategy first, and once that strategy is set, define the initial metrics they will use to measure their progress. The strategy and metrics can be gradually rolled out by onboarding teams to the new program.

Inadequate tools. For example, without a data catalog, an organization can’t track its data assets or access them consistently. There may also be an abundance of data silos – teams knowing about their own assets, but not having that knowledge shared across the organization.

To defeat this obstacle, organizations need to identify the tools they need to support a successful, organization-wide data governance program. Once those tools are set up, teams can start working with the new tools as they are onboarded to the governance program.

How to use data governance performance metrics #

After you have put in place the strategy and tools for a data governance program in your organization, it is time to put your metrics to use for generating business value. There are four stages to utilizing a metric:

  • Defining metrics that matter
  • Gathering baseline metrics
  • Introducing changes or establishing programs
  • Measuring and assessing impact

Defining the metrics that matter #


First off, you need to define your metrics. Start small, with three to four key metrics, and widen your program as you get a sense of what is going on with your governance program. For example, if you are having issues with data quality, you might start by making an accuracy check, a data lag metric, and a trust survey.

Gathering baseline metrics #


Use a tool like Atlan to define, gather, and report the meaningful metrics you establish. Start by measuring for at least a month to determine where you stand. Once you get the picture, define some quantitative goals. You can look to industry standards to set target numbers, or you can target incremental improvements and recalibrate according to the progress of your program.

Introducing changes or establishing programs #


Now that you have your metrics and targets, it’s time to adjust your governance program toward the goals you want to achieve. No matter which goals you have, or how many, it is important to introduce changes one at a time to isolate a given change’s impact on your metrics.

For example, if you see security issues, you should consider releasing a new data classification framework and introducing a new access control technology. You might start with the technology and see how well your teams adjust to it before going with the new framework, potentially saving you subscription costs on a product that doesn’t fit.

Measuring and assessing impact #


Finally it is time to look at your indicators and compare the values from baseline before and status after instituting a new tool or tactic.

If your change didn’t have an impact, you need to do a root cause analysis to see what went wrong. Maybe the tool you introduced was too confusing, so people didn’t use it. Maybe the framework you introduced was too complicated so teams got overwhelmed. Regroup, come up with a new strategy, and start the cycle again.

If the change achieved your goal, that’s great — but don’t stop there. Create new metrics and targets, move your current targets higher, or expand your data governance program to achieve an even greater impact from the measures currently in place.

Whatever your next step is, data governance performance metrics guide you along your journey.

Conclusion #

Data governance performance metrics serve as guideposts for improving your governance program. By incrementally defining and gathering metrics, setting goals, making changes, and assessing the impact of those changes, your organization can move towards better data quality, trust, and safety.

Atlan’s modern data catalog offers everything you need to define performance metrics and bring them into your governance program. Atlan operates as a single source of truth for your data and governance metrics, so you can have your full governance program working in one place.

But don’t take our word for it. See how Atlan can streamline your data governance journey by scheduling a demo today.

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