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. 

Read more

• Read more about why data automation matters and how it works in our Guide to data automation
• Read about your data team can be happier and more productive in The human aspect of data automation
  
• Comparing us to ETL or ELT tools? Read more in our blog 

  Why ETL Tools are not enough for Agile Data Warehouse development and operation