How to Measure ROI on Data Discovery

Our team wrote this article about how to measure the ROI on data discovery as a tool to help teams justify the costs of data discovery and the benefits to management. You can check out the full article here: https://www.secoda.co/blog/measure-roi-on-data-discovery and calculate your own ROI on data discovery here: https://www.secoda.co/measuring-data-debt

TLDR

Solving data discovery starts by getting on top of your team's data debt. Data debt is a type of technical debt that is created when teams don’t catalogue, clean and categorize or organize their data. It drags down productivity and costs the organization in compute costs. This costs teams time and effort, but too many times, data debt is difficult to measure. There are two primary drivers that drive the cost of data debt and lead to understanding your need for a data discovery tool:

Discovery time: Data discovery time is the amount of time that it takes for your data engineering and analytics team to find the right data, understand what it means and use it to analyze the data request.

Organization time: This is the amount of time spent cleaning, documenting and organizing data to make it legible for other employees

You can measure the financial impact of data debt by looking at how much money it costs your team to discover and organize the data. We recommend calculating data debt as the hours spent discovering + organizing data * average cost per hour.

By having an idea of the cost of data debt, teams can more easily calculate their return on investment for a data discovery tool. Without the existing baseline, it’s much more difficult to get buy-in from the managers controlling the budget for your team. We hope this helps.