Databricks, the Data and AI company, today released a commissioned global study conducted by Forrester Consulting, which reveals that an enterprise cohort of Databricks customers realized nearly $29 million in total value, driven by revenue, productivity and cost savings. Additionally, these same customers experienced a 417 percent return on investment (ROI) over three years and a payback in under six months when they deployed Databricks’ Unified Data Analytics Platform. This value is driven by a combination of revenue acceleration, data team productivity and cost savings associated with retiring legacy infrastructure.
The Total Economic Impact™ study provides companies with a tool to identify the potential financial impact the platform can have on their business. The study analyzed the cost, benefit, flexibility, and risk factors associated with Databricks’ Unified Data Analytics Platform. The full study “The Total Economic Impact of the Databricks Unified Data Analytics Platform” is available at: https://databricks.com/p/whitepaper/forrester-tei-study
According to Forrester, enterprises view AI and machine learning (ML) as both the biggest threat and the biggest opportunity for the future success of their businesses. To support data teams’ ability to innovate faster, organizations must democratize data, restructure teams to bring data science and business expertise closer together, and re-architect technology stacks to benefit from the scale of the cloud. The Total Economic Impact™ study uncovers how companies can do this faster and more effectively with the Databricks Unified Data Analytics Platform. Using Databricks, customers can tap innovations like AI-powered personalized product recommendations, supply chain forecasting, manufacturing defect detection, and fraud prevention that help maximize top-line growth and reduce operating costs.
Data teams interviewed for the study experienced the following key benefits of Databricks’ Unified Data Analytics Platform:
Increased Profits by Accelerating Data Science Outcomes: Customers reported an increase in revenues by 5%. By realizing the combination of more – and better – machine learning models with Databricks, data scientists were able to spend more time iterating on new innovations.
According to a Vice President of Data Science at a global media company, “Before, we had to wait two weeks for a process to finish before we could analyze and iterate on a ML model. Now, the same job takes an hour. We can iterate over it multiple times a day, allowing us to really understand the outcomes of our research, tweak it, and make a new model without interruption.”
Improved Productivity of Data Teams: Databricks improved customer productivity of data scientists and data engineers by 25% and 20%, respectively. Customers shared that the improved data management capabilities enabled data teams to spend less time searching for and cleaning data, less time creating and maintaining ETL pipelines, and more time building analytics and ML models to drive meaningful business outcomes. Databricks also helped remove technical barriers that limited collaboration between analysts, data scientists, and data engineers, enabling data teams to work together more efficiently.
Infrastructure Savings: By migrating to Databricks, organizations experienced a lower total cost of ownership and cost savings of millions of dollars from retiring legacy on-premise infrastructure.
“Our customers have proven the business value that can be generated when an organization combines data and AI on one platform,” said Ali Ghodsi, cofounder and CEO at Databricks. “We believe the Total Economic Impact study is validation that this unified approach enables data teams to innovate faster and delivers meaningful business value to our customers.”