Saket Saurabh

Welcome to our new interview series, “Let’s Talk DataOps!” DataOps (Data Operations) is an emerging function that controls the flow of data from source to value. This includes integrating with data sources, performing transformations on the data, converting formats, and writing or delivering data to its required destination so it can be analyzed, inputted into a model, or surfaced for end users. DataOps also encompasses the monitoring and governance of data flows, while ensuring security and scale.

In this new series we’ll be interviewing the leading thinkers in Data Operations, to discuss the state of DataOps from their point of view. Learn about what they do, their biggest challenges, and how they are utilizing DataOps to drive their businesses. For our inaugural interview, we spoke with Jon Loyens, the Co-Founder and Chief Product Officer of data.world. As a long-time technology executive in Austin, Jon has lived the rise in the trend of data and analytics as a democratizing force. In a past life, he was the VP of Engineering for Traveler Products at HomeAway, and before that, a VP of Engineering and Director of the Labs group at Bazaarvoice.

As summer comes to an end, it’s time to hang up our beach towels and…

Nexla is joining forces with Looker to empower companies to derive even more value from…

Welcome to the final segment of our 2017 Definitive Data Operations Report Mini Series! Teamwork…

Welcome back to the 2017 Definitive Data Operations Report MiniSeries! This week, we’re covering key…

Welcome back to the 2017 Definitive Data Operations Report MiniSeries! This week, we will be…

The chart above examines which functional areas within an organization plan to hire in DataOps. Unsurprisingly, 79% of those respondents in IT said they will hire in DataOps. The majority of Data Science, Engineering, and Analytics respondents also have plans to hire, which should come as no surprise.

What is surprising however, is that 53% of those who work in other functions also have plans to hire in DataOps. This “Other” category includes Product Management, Marketing, and even areas like Finance. This suggests business owners in almost all functional areas have the need to integrate, process, and derive value from data.

At Nexla, we are constantly thinking about the challenges of data operations for cross-company data. We’ve spoken with hundreds of companies about the unique effort required to send and receive data across company lines. But few benchmarks exist in the market for companies looking to learn from best practices. We decided to investigate.

This first-of-its-kind survey asked over 300 respondents about how they derive value from data. We surveyed data professionals from 40 different industries, with tenures ranging from two years to more than ten. In this post, we summarize some of the key benchmarks that emerged from the study. You can read the full report here, and a brief summary in this post.

Nexla is a next-generation data operations platform that manages secure inter-company data collaboration at scale. Our launch comes on the heels of the company’s $3.5 million seed funding round led by Blumberg Capital with participation from Storm Ventures, Engineering Capital and Correlation Ventures.

We finally get to share the good news that Nexla has been selected as a contender for TechCrunch Disrupt NY Startup Battlefield!

We’re honored to be joining the 19 other startups competing for the Disrupt Cup. Nexla will be pitching tomorrow, Tuesday, May 16. Hope to see you there or on the live stream!

Nexla’s CEO Saket Saurabh speaks about the five core values that define the culture of…

Data operations is the pick axe in the AI gold rush. Without the right data and the equipment to mine it, the promise of AI for many companies will be left unrealized. This is especially true for companies in finance, retail, healthcare, and more where you create value with the algorithms and analysis you do on data and not how you access and manage it. These companies would be wise to work with trusted software partners to build up their data operations teams. The data challenges that come with AI can’t be solved by more job listings. We’re going to need real technology to help our data engineering kings and queens process the next 180 zettabytes.