Best Practices 3 Min Read

The Next Level of Data and Analytics Maturity with DataOps

Best Practices 3 Min Read
By: Steven Wastie

This summer, the Unravel team had the opportunity to work with the Eckerson Group, a research firm focused on the needs of business intelligence and analytics leaders.

Our most recent project with the Eckerson Group allowed us to work closely with BI thought leaders, Wayne W. Eckerson and Julian Ereth. The result is the firm’s latest research, “DataOps: Industrializing Data and Analytics—Strategies for Streamlining the Delivery of Insights.”

DataOps is an integrated approach for delivering data analytics solutions that uses automation, testing, orchestration, collaborative development, containerization and continuous monitoring to constantly accelerate output and improve quality. The purpose of DataOps is to accelerate the creation of data and analytics pipelines, automate data workflows and deliver and operate high-quality data analytics solutions that meet business needs as fast as possible.

Today, the conditions for DataOps are prime as organizations grapple with immature data and analytics pipelines, which demands for a new approach. Enter the methodology of DataOps, which builds on modern principles of software engineering, supported by big data testing tools supported by millions of records from real-world DataOps jobs.

The “DataOps: Industrializing Data and Analytics,” report dives deeper into the definition of DataOps and its holistic approach of bringing stakeholders together to improve quality, increase speed, and establish a culture of continuous improvement. In addition to core principles and the idea of analytics pipelines as the heart of DataOps, several case studies will be introduced and the report will help you:

  • Increase the value proposition of data and analytics by industrializing processes
  • Establish a culture of continuous improvement and collaboration
  • Speed up processes and increase quality by providing streamlined data analytics pipelines via deep levels of automation and testing
  • Support the management and orchestration of heterogeneous technologies and stakeholders
  • Increase visibility in complex data landscapes and assure a stable and efficient operation of applications and infrastructure
  • Operationalize data science to provide more value to the business

We’re pleased to make the full report available to our Unravel Data community. And if you’re looking for additional information about how Unravel is simplifying DataOps in a number of ways, I encourage you to check out this recent video we did with Eckerson, which demonstrates how we help data teams reliably operationalize their Data Pipelines in production.