Nucleus Research published an ROI guidebook examining the benefits of deploying the Databricks Lakehouse Platform. Nucleus interviewed multiple Databricks Lakehouse customers and found the solution delivered an average ROI of 482 percent over a three-year period, with an average annual benefit of $30.5M and a payback period of 4.1 months.

“The mass adoption of data and AI have changed the way enterprises consume cloud computing, and legacy infrastructures are failing to keep pace with scaling data volumes. As enterprises modernize their analytics infrastructure, they also seek to consolidate systems to reduce complexity and streamline administrative efforts,” said Senior Analyst Alexander Wurm. “As a result, solutions like the Databricks Lakehouse Platform have risen to prominence for their ability to replace various legacy systems, including data warehouses, data lakes, and other specialized systems, while supporting diverse data applications, including SQL analytics, AI modeling, data transformations, governance, and more.”

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“As a result, solutions like the Databricks Lakehouse Platform have risen to prominence for their ability to replace various legacy systems, including data warehouses, data lakes, and other specialized systems, while supporting diverse data applications, including SQL analytics, AI modeling, data transformations, governance, and more.”

Key benefits highlighted in the guidebook include:

• User productivity improvements. With Databricks, organizations noted 49 percent improved data team productivity including time savings of 52 percent for data scientists, 51 percent for data engineers, and 45 percent for data analysts.
• Process improvements. Organizations deploying Databricks saw 48 percent data ingestion improvements, 33 percent ETL efficiency gains, 28 percent improved BI efficiency, and 60 percent more efficient MLOps.
• Infrastructure cost savings. Overall, customers of the Databricks Lakehouse Platform realized a $2.6M average annual infrastructure cost savings. An equipment manufacturer noted 30 percent processing cost savings by migrating to Databricks while another sports franchise experienced 4x compute efficiency relative to Snowflake. The organization’s data storage was also more efficient with Databricks’ medallion architecture, generating $96,000 in annual savings.
• Reduced process latency. Databricks customers saw an average processing latency improvement of 48 percent. One biotechnology company saw 75 percent reduced processing latency with individual ownership of projects and timelines. This organization also saw a 36-hour reduction in processing latency for weekend loads, allowing analysts and sales professionals to work on relevant data faster.
• Administrative cost savings. Organizations deploying the Databricks Lakehouse Platform experienced $1.1M average administrative cost savings. One organization noted $710,000 in annual administrative cost savings, including 50 percent less time spent on platform management. An equipment manufacturer saw $3.4M in annual savings and an organization in the e-commerce industry saved $480,000 in annual administrative costs with 20 percent administrative time savings.
• Accelerated time to production for data and AI projects. By using the Databricks Lakehouse Platform for large-scale data processing and model training and deployment, Nucleus found that organizations significantly shortened the time to production for their data and AI projects by 52 percent. A biotechnology company saw 2x faster time to production for its data and AI projects. Another organization in the e-commerce industry noted 1.8x faster time-to-live for AI use cases, including 60 percent reduced time-to-live for the organization’s NLP interface.

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