Nucleus Research has released a ROI guideline that examines the advantages of installing the Databricks Lakehouse Platform. Nucleus interviewed several Databricks Lakehouse customers and discovered that the solution provided an average ROI of 482% over a three-year period, with an average yearly benefit of $30.5M and a payback period of 4.1 months.
“The widespread adoption of data and AI has altered how enterprises consume cloud computing, and legacy infrastructures are struggling to keep up with growing data volumes.” “As enterprises modernise their analytics infrastructure, they seek to consolidate systems in order to reduce complexity and streamline administrative efforts,” according to 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 specialised systems, while supporting diverse data applications, such as SQL analytics, AI modelling, data transformations, governance, and more.”
Key benefits highlighted in the guidebook include:
- Increased user productivity: Organisations saw a 49 percent increase in data team productivity with Databricks, including time savings of 52 percent for data scientists, 51 percent for data engineers, and 45 percent for data analysts.
- Process enhancements: Databricks users saw 48 percent better data ingestion, 33 percent better ETL efficiency, 28 percent better BI efficiency, and 60 percent better MLOps.
- Savings on infrastructure costs: Customers using the Databricks Lakehouse Platform saved an average of $2.6 million per year on infrastructure costs. An equipment company reported a 30% reduction in processing costs after shifting to Databricks, while another sports brand saw a 4x increase in computing efficiency over Snowflake. With Databricks’ medallion design, the organization’s data storage was also more efficient, resulting in $96,000 in annual savings.
- Process delay has been reduced: Customers of Databricks saw a 48 percent reduction in processing latency. With individual responsibility of projects and timeframes, one biotechnology company witnessed a 75% reduction in processing latency. This organisation also experienced a 36-hour decrease in processing delay for weekend loads, allowing analysts and sales personnel to work on relevant data more quickly.
- Administrative costs are reduced: Organisations that used the Databricks Lakehouse Platform saved an average of $1.1 million in administrative costs. One company saved $710,000 in annual administrative costs, including 50% less time spent on platform management. An equipment company saved $3.4 million per year, and an e-commerce organisation saved $480,000 per year in administrative costs while saving 20% on administrative time.
- Time to production for data and AI initiatives has been sped up. Nucleus discovered that by leveraging the Databricks Lakehouse Platform for large-scale data processing, model training, and deployment, organisations reduced the time to production for their data and AI initiatives by 52%. A biotechnology company reported a twofold reduction in time to production for its data and AI projects. Another e-commerce company reported 1.8x faster time-to-live for AI use cases, including a 60% reduction in time-to-live for the company’s NLP interface.