Gartner, the world's leading information technology research and advisory company, defines DataOps as follows:
“DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organisation. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models and related artifacts. DataOps uses technology to automate the design, deployment and management of data delivery with appropriate levels of governance, and it uses metadata to improve the usability and value of data in a dynamic environment.”
DataOps is a technological orchestrator at the service of your project. The promise of DataOps is therefore to improve the quality and optimise the delivery cycle of data projects to reduce the time-to-market of analysis and prediction. In this publication, Talan offers you an introduction to this discipline that our Data Intelligence experts implement to support their clients’ data initiatives and improve performance.
Through client case studies and feedback from our teams, you will discover:
1. What is DataOps?
- DataOps in a 100% data-centric world
- DevOps and DataOps: what synergies and differences?
- The main principles of DataOps
- DataOps for efficient Data projects
2. DataOps in the prism of the Talan approach
- Talan: a team of data experts, trained in DataOps
- The DataOps loop
- Quality and community: two fundamental pillars of DataOps projects
- Why and how to adopt a data project integration approach?