DataOps is Surging!

Our friends at Nexla have released their second annual Data Operations Report, and it contains some great insights into the state of the DataOps. It’s awesome to see more companies – both end-users and vendors – getting behind this movement. DataOps is a term that Tamr’s CEO Andy Palmer coined in 2015, and we’ve literally written the book (or at least the first few chapters) on the subject. You can download a preview edition of Getting Data Operations Right here.

From Nexla’s study, it’s clear that for many enterprises, there is a vast gulf between what they’d like to be doing with all of their data and what they’re actually able to do. The report highlights that data professionals are spending, on average, a paltry 14% of of their time on analysis. Lower value tasks, such as finding, curating and preparing data sets, eat up the rest of their time. It’s no surprise that 70% of respondents wish they had more time to improve current processes, but they’re clearly swamped with all of that data munging. The least popular activity for respondents was data cleaning- by far.

DataOps companies like Tamr and Nexla know that automating key tasks in data pipelines is crucial to reducing the workload on individual data professionals and ensuring clean, unified data that is widely available for a variety of use cases.

We’re at a unique moment in time for data engineering, and DataOps is emerging in response to the converging forces of movement to the cloud, the increased ability of modern technology to scale, and the opportunity presented by treating data as an asset. It’s thrilling to see that Nexla’s survey reinforces those core trends: nearly 50% of respondents indicated that the majority or entirety of their data is in the cloud, and 73% of respondents indicated they are investing in additional data professionals in the coming year.

It’s fantastic to see DataOps picking up steam. As we know from work with our own customers, it’s going to be a key part of enterprises’ strategy to pay down their data debt and start using data as a competitive advantage.