While DataOps is a process, it also requires critical capabilities within your data platforms to support it and deliver the promised efficiency, quality, and scale. Let's explore what DataOps is and five specific ways in which DataOps will increase your analytics ROI.
DataOps is an emerging new process in the data analytics world that applies DevOps concepts to data management for analytics. To downstream analytics and data science teams, DataOps promises to deliver the speed, efficiency, quality, and productionizing of data delivery for their analytics needs so they, in turn, can deliver timely answers to critical business questions to fuel effective decisions.
Explore the five objectives of DataOps: speed, increased output, higher quality, governance, and reliability.
Drill into the key features you need in your data platforms to deliver on the goals of DataOps.
"In many ways, both DevOps and DataOps borrow from lean manufacturing concepts. For all three, the objectives are faster production, greater output, higher quality output, and full reliability and predictability."
See the key capabilities in Datameer Spectrum that deliver on the DataOps process and objectives.
According to Wikipedia, DataOps is defined as:
An automated, process-oriented methodology used by analytic and data teams to improve the quality and reduce the cycle time of data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting and recognizes the interconnected nature of the data analytics team and information technology operations.
DataOps borrows many DevOps concepts, uniting software development and IT operations to bring speed, quality, predictability, and scale to software development. DataOps piggybacks on this to bring these same attributes to data analytics.
In many ways, both DevOps and DataOps borrow from lean manufacturing concepts. For all three, the objectives are faster production, greater output, higher quality output, and full reliability and predictability.
Dramatically more complex data landscapes and data flows have put great stress on data teams. Project backlogs have grown while analytics and business teams continue to wait for new data required for their analytics and often lack trust in the data they do receive. A Forrester Research study found a strong lack of trust in data and that much of the data captured by an enterprise goes unused (see below).
60% of executives are not very confident in their Data & Analytics insights
More than 60% of data in an enterprise goes unused for analysis.