DataOps

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.

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.

Today Nexla is announcing a solution for companies to run Machine learning models in their own data centers. Nexie is an ingenious piece of hardware designed by Nexla which brings machine learning into your own data center. The device can be powered on in any data center across the globe. Nexie can connect with existing storage solutions via multiplexed universal ports. Nexie comes with two ports, In & Out. The In port allows you to receive data, Out port outputs the results of the model. Multiple Nexies can be joined together to create portable clusters!

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.

The Internet Archive, also known as the Wayback Machine, has been capturing snapshots of the World Wide Web for over 20 years. But a new effort to save a specific type of Internet content- scientific data- has emerged among some scientists and professors. Groups like the Penn Program in Environmental Humanities and its offshoot Data Refuge are committed to preserving “…the facts we need at a time of ongoing climate change.”

These groups encourage others to download and store public scientific data. They’ve acted as a catalyst for groups around the country to host data meetups. At Nexla, we are committed to building tools that make it easier to collaborate with data. In that spirit, we’d like to share two of the methods we’ve found for archiving important data sets.