But not all data and AI strategies are created equal. Recent studies have shown that only one in three data and AI projects are successful. This is largely a result of legacy analytics investments that lack the scale, collaboration features, and modern big data and AI capabilities required to build and deploy advanced analytics products. With organizations struggling to deliver data-driven innovation, this begs the question: What enterprise-grade technologies should organizations invest in to help their data teams be successful? And how can organizations quantify the impact of these investments?
Delivering measurable value with Databricks
To help answer these questions, Databricks commissioned a Forrester Consulting study: The Total Economic Impact™ (TEI) of the Databricks Unified Data Analytics Platform. In this new study, Forrester examines how data teams — and the entire organization — can move faster, collaborate better and operate more efficiently when they have a unified, open platform for data engineering, machine learning, and big data analytics. Through customer interviews, Forrester found that organizations deploying Databricks realize nearly $29 million in total economic benefits and a return on investment of 417% over a three-year period. They also concluded that the Databricks platform pays for itself in less than six months.
More specifically, data teams interviewed for the study experienced the following key benefits from the Databricks Unified Data Analytics Platform:
Increased revenues by accelerating data science outcomes
Databricks customers achieved a 5% increase in revenues by enabling data science teams to build more — and better — ML models, faster. Additionally, Databricks democratized data access across the organization. This led to new users creating a diverse set of new analytics products such as recommendation engines, pricing optimizations and predictive maintenance models. All these innovations led to top-line growth.“We were looking for a unifying solution to put all of our technology and data together as well as democratize the data and Databricks gave us all these solutions”
Read Sevatec’s story
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 among analysts, data scientists, and data engineers, enabling data teams to work together more efficiently.“Notebooks allow multiple groups to work together from a single point. It eliminates having that swivel chair activity as people can work directly in the interface together.”
Read USCIS’ story
Significant cost savings retiring legacy analytics platforms
By migrating to Databricks, interviewed organizations were able to retire on-premises infrastructure and cancel legacy software licenses, resulting in millions of dollars of savings. Additionally, the management of the Databricks platform proved substantially easier than legacy environments. This enabled customers to reallocate IT resources to higher-value projects and reduce operational costs.“Databricks has enabled Comcast to process petabytes of data while reducing compute costs by 10x. Teams can spend more time on analytics and less time on infrastructure management.”
Read Comcast’s Story
Read the Forrester TEI study
With the Databricks Unified Data Analytics Platform, customers can now accelerate data-driven innovation, thanks to a unified, open platform for data science, ML, and analytics that brings together data teams, processes, and technologies.To find out more, download the full Total Economic Impact study for Databricks.
