Organisations have a wealth of information siloed in various sources. Pulling this data together for analytics, reporting and AI applications is one of the biggest obstacles to realising business value from data.
Ingesting all this data into a central data lake is often hard, in many cases requiring often-bottlenecked IT effort that requires data engineers to perform custom development, write scripts, schedule jobs, triggers and handle job failures. This approach does not scale and creates massive operational overhead.
- Reduce demand on IT and remove bottlenecks
- Create a controlled and governed centralised data lake
- Provide the foundation for rapid data analytics