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The Missing Element in the Modern Data Stack: Automation

So you’ve adopted a modern data stack. But is your data stack truly automated? Or is it complex and fragmented?

You may have all the components of your stack in place, but that doesn’t mean it works well together. And in a time when it’s more important than ever to demonstrate ROI and get value out of your data, you need a data analytics stack that solves problems instead of creating new ones. Here are a few of the challenges you might be facing in your data stack today:

  • Utilizing a growing number of varied data sources
  • Ensuring data quality and accuracy at each stage (which becomes even more critical for AI/ML use cases)
  • Enabling team members to access data quickly and securely from a data warehouse or data lake

Automation can overcome these challenges. Yet automation is often missing from the data stack. In this Ventana Research perspective, see how you can better leverage automation in your analytics architecture for a data stack that works the way it should: seamlessly.


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