Be more productive and agile
According to Gartner, data automation makes each person in your data team 4 times as efficient compared to standard ETL, ELT and other data integration tools. It also applies to every change and new data-related project.
Why it matters
- Data integration typically counts for 80% of costs and time for any analytics, AI and ML project, it makes sense to do it as efficiently as possible
- Selling in data & analytics initiatives to your management board is easier if the MVP is done in days and weeks instead of months and years
- There is shortage of data engineers so each person must contribute as much value as possible
- A key challenge with analytics, AI and ML is getting access to trusted and quality assured data, much of this work can be automated
- A data platform must not only be efficient to build, but to maintain and change, data automation is built with this in mind
- Minimizing the hurdle of new data & analytics projects makes it easier to try out opportunities within AI and ML
How it works
- Imagine all methodology, code snippets, tools, established practices, and learnings from both software development and data engineering delivered as a software.
- It is more specific and provides more guidance and standardization than data integration tools, and ETL and ELT tools.
- Your team works on a higher abstraction level by configuring metadata that generates code, rather than having to write all the code yourself.
- Since the automation is metadata driven, it helps you keep control of end-to-end dependencies which simplifies impact analyses, runtime optimizations, error detections and reduces risks of human errors.