Automated Business Analytics with Automic

What we offer > Data Automation > Automated Business Analytics with Automic

Reliable and up-to-date business analytics are crucial for decision makers to deliver world-class business performance, see ‘over the horizon’ and stay one step ahead of the competition. Fast, reliable data warehouses are key for delivering these business-critical reports to business users at the right time, anywhere.

Ensuring that information is available in a timely and accurate manner for strategic, tactical and operational decisions can be a challenge. The amount of data that needs to be collected across business units, applications and external sources is growing exponentially. More and more applications are hosted “as a service” in the cloud and within Big Data environments such as Hadoop. And timely data-processing reporting must also be monitored to meet service agreements demanded by the business.

Increase Agility and Speed for your Business Analytics

Business analysis is faster with Automic. Our solution automates the end-to-end data warehousing process—from enterprise applications and ETL tools to databases and Big Data platforms—ensuring the timely delivery of trusted BI reports to your business users.

Automic Data Automation puts analytics at your fingertips, orchestrating the end-to-end data process across traditional and cutting-edge applications and technologies. Integrated file transfer capabilities, out-of-the-box adapters for enterprise data warehousing and business intelligence applications like Informatica and SAP Business Objects, and integrations into big data technologies like Hadoop and in-memory databases enable you to automate the data processing lifecycle—from source, processing and archiving to analytics and reporting.

Benefits

  • Increases agility and efficiency through integration for enterprise data warehousing and business intelligence applications
  • Saves time and reduces risk using integrated file transfer capabilities
  • Automates the data processing lifecycle via integrations with databases, in-memory databases and big data technologies
  • Reduces EDW implementation times and provides the framework for promotion to the production environment
  • Enables data scientists and others to quickly build complex Hadoop workflows, reducing development time and effort

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