The Role of DataOps in Building a Data-Driven Organization

  • The democratization of Data (Technology): They built a custom data warehouse called Singularity, which was able to run ad hoc queries in 32 seconds. Moreover, they started using data in a programmatic fashion, thereby bringing in the power of machine learning.
  • Personalization (Process): Shifted their focus on behavioral data to drive customer engagement via its Customer Relationship Management (CRM) system.
  • Data-Driven Culture (People): Analytic teams started to fine-tune their operational performance by self-reflecting, at regular intervals, on feedback provided by their customers, themselves, and operational statistics.

The ‘Data Challenge’ in Today’s Digital Era

The Roots of the DataOps Approach

So, What is DataOps Approach?

A Successful DataOps Practice

  • People: Define rules for an abstracted semantic layer. Ensure everyone is “speaking the same language” and agrees upon what the data (and metadata) is and is not.
  • Process: Design process for growth and extensibility. The data flow model must be designed to accommodate volume and variety of data. Ensure enabling technologies are priced affordably to scale with that enterprise data growth.
  • Technology: Automate as many stages of the data flow as possible including BI, data science, and analytics.

DataOps Approach: Use Cases

Dashboards and Reports

Data Science

Data Warehousing

Conclusion

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