Elsevier Labs needed a data platform to develop innovative natural language processing algorithms and models to help the product team create new scientific research tools. Prior to implementing Databricks, Elsevier Labs had been hampered by inefficient data movement, lack of code reuse, and difficulties in presenting results.
Databricks provided the unified content analytics capability sought by Elsevier Labs, enabling the team to gain significant productivity improvements and significantly increase the number of people working on the data.
- Faster time to complete projects: Team productivity improvement shortened typical time to complete projects from weeks to just a few days.
- Remove bottlenecks and broaden access to data: With Databricks, over 15 people contribute to developing content analysis algorithms instead of limiting the analysis to two or three specialists.