How to Measure the Value of a Data Catalog

Data discovery enhances data governance, compliance, and operational efficiency, measured by user adoption, cost savings, and compliance, driving business value.
Last updated
May 2, 2024

Discovery is a critical piece of scaling data-driven initiatives - so how can you tell if you’re nailing it?

Despite data team best efforts, many companies still spend upwards of 30% of their time trying to find and understand the context around their business’ data, whether that data can be trusted, and how to leverage it correctly. Organizations that have robust data discovery programs have a significant competitive advantage. So how do you know where your company stands when it comes to the business value and success of your data catalog initiative?

As with any good analysis, measuring success involves looking at some data. Before we talk about how to measure data discovery, let’s first define what it is, and how it can help impact your business.

What is data discovery?

Data discovery, when viewed through the lens of data governance, refers to the process of identifying, understanding, and cataloging available data assets within an organization. This process is a foundational element of data governance as it ensures that data across the organization is accurately identified, classified, and managed in line with data governance policies and standards, to ensure it can be safely used by data consumers across an organization.

How does data discovery drive impact?

Data discovery drives impact across an organization in many different ways, here are some tangible examples:

Data Inventory and Cataloging

Data discovery aids in creating a comprehensive inventory of data assets. This includes documenting where data resides, its format, its lineage, and how it's related to other data. This data catalog or inventory serves as a foundation for data governance efforts, ensuring that data assets are well-documented and easily accessible to authorized users.

Data Classification and Metadata Management

Through data discovery, organizations can classify data based on its sensitivity, regulatory requirements, and business value. Metadata management, a key part of data governance, is closely tied to data discovery as it involves tagging data with relevant information (like data ownership, data quality, and usage constraints) which is critical for effective governance.

Compliance and Regulatory Requirements

Data discovery is crucial for identifying and managing data that is subject to regulatory requirements. By understanding where sensitive or regulated data is stored and how it is used, organizations can implement appropriate controls to ensure compliance with laws like GDPR, HIPAA, and CCPA.

Risk Management

Identifying and classifying data through discovery helps in assessing and mitigating risks related to data privacy, security breaches, and data quality issues. Data governance frameworks rely on this process to establish policies and procedures that protect data assets.

Data Quality and Integrity

Data discovery processes help in assessing data quality and integrity. By understanding the sources and lineage of data, governance initiatives can implement standards and practices to ensure that data is accurate, consistent, and reliable.

Data Accessibility and Usage

A key goal of data governance is to make data accessible to those who need it while restricting access to unauthorized users. Data discovery supports this by providing insights into data availability, relevance, and usage policies, which helps in defining data access controls and permissions.

Data Lifecycle Management

Data discovery informs data lifecycle management by identifying how data evolves, from creation to deletion. Understanding this lifecycle is crucial for implementing policies around data lineage, retention, archiving, and disposal in compliance with governance policies.

Data discovery is a critically important process that underpins the ability of data teams to establish governance control, improve data quality, and drive value from data assets.

Implementing a framework to measure the value of data discovery

Knowing how important data discovery projects can be, it is also critical to measure the success of these projects to optimize efforts. This can be done in a few ways, both from direct and indirect impact:

  1. User adoption of a data discovery platform
  2. Cost savings attributed to the project
  3. Improvements in operational efficiency
  4. Speed to onboarding and efficiency of new hires
  5. Improved “Data Trust” score
  6. Measuring compliance adherence nkjjn

User adoption of a data discovery platform

KPI: User Engagement Metrics (e.g., Searches, Queries)

User adoption serves as a pivotal KPI for data teams due to its direct correlation with maximizing the value of organizational data. 

High user adoption indicates that the investments in data infrastructure and analytics tools are yielding returns, validating data accessibility and usability. Additionally, it reflects a positive shift toward a data-driven culture, where decisions are informed by insights, and fosters cross-functional collaboration and analysis. Tracking user adoption enables data teams to identify training needs, facilitate continuous improvement, and build trust in data quality. Ultimately, it provides a tangible metric to demonstrate the impact of data efforts, justifying ongoing investments and reinforcing the role of data in driving organizational success.

Objective: Assess the level of user engagement within the data catalog. Increased user activity indicates successful adoption. Analyze the frequency and type of interactions to identify popular datasets and areas requiring improvement. A data catalog with built in analytics for popularity of resources can help measure this easily. 

Cost savings attributed to the project

KPI: Cost Reduction across your data pipeline

In the face of challenges associated with scaling a data team and a modern data stack - cost savings are imperative to track as a KPI. 

Teams are navigating the complexities of maintaining efficient pipelines, preventing surprise bills, avoiding slow run times, and minimizing downtime. With the growing emphasis on cost containment in the wake of ballooning infrastructure costs, data teams need to recognize the need for a proactive approach to managing expenses. This includes making early investments in data cost containment strategies to mitigate financial strain, ensuring that the team operates with optimal efficiency while aligning with overall budgetary goals. Cost Savings as a KPI underscores the team's commitment to responsible data management and financial stewardship in a dynamic and evolving data landscape.

Objective: Quantify the cost savings achieved through the implementation of the data catalog. Consider factors such as monitoring compute cost, reducing the amount of costly data assets, reduced support tickets, and minimized manual data handling.

Improvements in operational efficiency

KPI: Operational Efficiency Metrics (e.g., Time Saved, Resource Utilization)

Metrics like time saved and resource utilization provide tangible insights into the impact of data initiatives, optimizing productivity and resource allocation. This not only leads to cost savings and improved ROI but also enhances agility, enabling the team to meet business objectives more effectively. Streamlining workflows contributes to a positive user experience, ensuring the data team operates at its full potential, delivering value to the organization in a cost-effective and responsive manner.

Objective: Assess the impact of the data catalog on operational efficiency. Measure time saved in data-related tasks and evaluate resource utilization to gauge the catalog's contribution to overall operational effectiveness - for example, taking a closer look at how an AI-enabled data catalog can save your team time with writing documentation and descriptions.

Speed to onboarding and efficiency of new hires

KPI: Average Time for New Hires to Reach Full Productivity, Onboarding Time

Prioritizing onboarding processes is essential for a data team's efficiency. Efficient onboarding ensures rapid acclimation of new team members, minimizing disruptions and accelerating their meaningful contributions. Tracking these metrics directly impacts team efficiency, project timelines, and overall morale. Data catalogs infinitely improve the onboarding experience for new hires, by giving them a way to self serve their questions.

Objective: Measure the efficiency of the onboarding process by tracking the time it takes for new team members to become fully productive in their roles. A shorter time-to-productivity indicates an effective onboarding program.

Improved “Data Trust” score

KPI: User Satisfaction Surveys, User Feedback

Measuring User Satisfaction as a KPI provides a direct indicator of how well the data catalog meets the needs and expectations of its users. Positive user feedback indicates successful adoption, effective usability, and overall acceptance of the data catalog within the organization. 

Additionally, user satisfaction metrics help identify pain points and areas for improvement, enabling the data team to iterate on the implementation, enhance user experience, and address any challenges promptly. 

Objective: Collect user feedback through surveys to measure satisfaction levels with the data catalog. High user satisfaction indicates successful adoption and perceived value.

Measuring compliance adherence

KPI: Compliance Violations and Penalties

Measuring Compliance Adherence is essential for ensuring that your organization maintains adherence to data governance policies and regulatory requirements. Tracking compliance KPIs helps to identify and address potential risks and gaps in data management practices, ensuring that sensitive information is handled securely. 

Objective: Monitor the reduction in compliance violations and associated penalties. A successful data catalog should contribute to better adherence to data governance policies and regulatory requirements.

Measurement is Critical to Driving Success

Without measuring the performance of your data discovery project, it is impossible to know whether you are delivering on the objectives you set out to achieve. Following the steps above will provide robust measurement of your data discovery efforts, enabling you to report back to leadership on the business impact, drive further optimizations and improvements across your business, and help you to allocate resources to the most critical areas.

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