Saket Saurabh

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.

When we talk about exciting innovations like self driving cars, or ask Amazon’s Alexa to…

This first-of-its-kind survey asks data engineers and other data professionals about the State of Data Operations. The resultant report will provide a snapshot of the DataOps function in today’s companies. It will explore what is working, and where improvements need to be made to help companies most effectively use their data. Immediately after taking the survey, you’ll receive an assessment your company’s current DataOps based on our model and your responses.

In hundreds of conversations with customers, investors, and other data professionals, we’ve found that everyone believes they have heard the term before, but isn’t quite sure what it means, exactly. When asked to describe DataOps, most people intuitively understood it had something to do with moving data to the right place in the right format. To move the conversation forward, we need a clear definition we can all use. At Nexla, we believe:

DataOps is the function within an organization that controls the data journey from source to value.

We’re happy to announce that Nexla will be presenting at the Startup Showcase next week at Strata+Hadoop World in San Jose! It’s an honor to be selected along with other up-and-coming big data companies. The pitch-off takes place Tuesday, March 14 at 6:30pm.

And don’t miss our CEO Saket Saurabh’s talk on Wednesday at 2:30pm in the Solutions Showcase. Learn “How to Automate Data Operations so You Can Build Machine Learning and Advanced Analytics.” Come swing by our booth in the Innovator Pavilion.

When faced with a difficult question, mathematicians like to solve from first principles. Engineers get down to nuts and bolts. Most of us call it “going back to basics.” Letting go of assumptions and relying on fundamental concepts is a great way to find a clean, high quality solution to a difficult problem. As an engineer, I followed that approach in my work and as an entrepreneur, one of the first things the Nexla founding team did was to codify the core cultural values of our company. Those cultural values are our first principles, our nuts and bolts.

Film buffs will remember the above scene from Stanley Kubrick’s iconic Dr. Strangelove, in which President Muffley says, “Gentlemen, you can’t fight in here! This is the War Room!” The absurd, satirical line is oddly applicable to what’s happening today in machine learning teams. The machine learning folks shouldn’t be data wrangling- they should be focused on machine learning. But because we don’t often receive our data in a usable format, valuable time is spent transforming, moving, and cleaning data.

Automotive is the buzzing topic at CES 2017. That’s no surprise given the pace of innovation in vehicle connectivity and autonomy. But beyond the cool concept cars and live demos, CES discussed questions about safety, regulation, social impact, smart cities and how these new vehicles will change life and jobs as we know it. New vehicle technology has the opportunity to transform how we live, and save lives while doing it. But is the automotive industry ready for this challenge?

Welcome to the Nexla blog! We’re happy to debut our new blog and logo just in time for the AT&T Developer Summit and CES. We want to make it easier for companies to be able to collaborate with data, and for data engineers to do their jobs faster and with ease. We aim to simplify the complexity of data operations in the machine learning age. We wanted our logo to reflect this commitment to simplicity.