way. DataOps seeks to increase velocity, reliability, and quality of data analytics. It emphasizes communication, collaboration, integration, automation, measurement and cooperation between data scientists, analysts, data/ETL (extract, transform, load) engineers, information technology (IT), and quality assurance/governance. Implementation Toph Whitmore at Blue Hill Research offers these DataOps leadership principles for the information technology department: “Establish progress and performance measurements at every stage of the data flow. Where possible, benchmark data-flow cycle times. 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. Validate with the “eyeball test”: Include continuous-improvement -oriented human feedback loops. Consumers must be able to trust the data, and that can only come with incremental validation. Automate as many stages of the data flow as possible including BI, data science, and analytics. Using benchmarked performance information, identify bottlenecks and then optimize for them. This may require investment in commodity hardware, or automation of a formerly-human-delivered data-science step in the process. Establish governance discipline, with a particular focus on two-way data control, data ownership, transparency, and comprehensive data lineage tracking through the entire workflow. 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.” Events Data Opticon Data Ops Summit Data Ops Online Champion References Data analysis