Welcome back to the 2017 Definitive Data Operations Report MiniSeries! This week, we will be discussing the various factors driving…
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.
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.
At Nexla, we think of APIs are belonging to one of two categories: service or data. A service API is a building block for a developer, a way to hook into another application’s functionality. It’s how developers can build apps in Slack, or add google maps to a web app. Data APIs on the other hand are a bit more limited. They allow developers to pull data from a source and then use that data however they see fit. They don’t offer any additional functionality or services. And that’s why they’re facing extinction.
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.