Data transformation is a critical component of any IT modernization efforts taken on by an organization today. The digital user expects data providers to leverage data as a strategic asset and provide data to its citizens that facilitates oversight, increases overall effectiveness, and promotes transparency.
What Is DataOps?
DataOps is a methodology for automating and optimizing challenging disciplines of data management to deliver data through its lifecycle. It relies on the same collaborative cultural and innovative use of tooling that underpins Agile and DevOps foundations to balance control and quality with continuous delivery of data insights. Through the foundation of DataOps, organizations can accelerate new data services and products—enabling machine learning and intelligence at scale.
In this viewpoint, Booz Allen shares lessons learned from successful organizations and describes relevant principles to make the most out of DataOps within your organization.
Download the insight to read about the three pillars necessary for effective use of DataOps:
- Culture and organizational change
- Agile and DevOps foundations
- Tool selection and integration
