All of the action is at the edge of the network today, and that edge is increasingly being defined by the Internet of Things, both the consumer and industrial variety. The industrial IoT in particular has become a rallying cry for the telecommunications, energy, health care, and transportation industries, among others, that are now realizing a new challenge: making sense of the data that the devices at the network’s edge produce.
That challenge is opening up some lucrative opportunities in the job world. Jeff Healey, director of product marketing for Hewlett Packard Enterprise-owned Vertica, says that if you’re pursuing a career path in technology and have an analytical background, “you have a huge opportunity in the world of edge and IoT analytics.”
If you are at an early stage in your career or are thinking about ways to increase the marketability of your skills, it makes sense to think about the analytical side of the industrial IoT, a market that is projected to hit $150 billion by 2020.
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The growing importance of edge analytics
For those unfamiliar with this growing industry, edge analytics is a field that takes the often “chatty” data generated by equipment at the edge of the network and tries to make sense of it, using that information to predict device failures, improve performance, or make other business decisions. Because this data is rarely standardized, analyzing it can be complex. When thousands or millions of devices are involved—think about all the smart appliances now installed in the US, generating a constant information feed—that calculus becomes even more complicated.
Healey notes that the design of next-generation database management systems, combined with the analytical skills of today’s emerging data scientists, is making this kind of analysis possible at last. “With big data like this, the volume and variety of information coming in has created a need for a new architecture and database design. Databases like Vertica embed statistical functions right into the database, which makes it easier to analyze this kind of information without having to exit the data store,” says Healey.
We aren’t just talking about analyzing refrigerator sensor data to ensure that the world’s milk isn’t going bad. “We’re talking about industrial systems and equipment that have a high cost of downtime and a high cost of service, things that have been ‘connected’ in one way or another for 30 years,” says Healey. “The missing piece has always been around analytics. We’ve become good at collecting data in a remote location and sending it back, but we’ve been missing a way to store and make sense of that data, particularly with very large volumes of sensor data.”
Today’s edge analysts are likely to find work not just in heavy industry, but also in complex and data-heavy environments such as utility grids (where advance warning of trouble spots is critical) and insurance (where edge analytics is becoming a popular tool to detect fraud).
“Data scientists are increasingly looking for solutions to difficult problems,” says Healey. “They’re doing the work that consultants and MBAs used to do as companies look for ways to reinvent themselves.”
The education and skills needed in edge analytics
Healey likens today’s rush toward analytics as a bit like the original dot-com boom. “In the ’90s,” he says, “it was all about developers. But not everyone could learn C++ or Java, so scripting languages emerged that made things easier, and more people could get involved in development. Now, with IoT, we have tools emerging that are making this field more viable. If you know anything around analytics, you might consider a career path there. These jobs are in high demand and can command hundreds of dollars per hour. There’s never been a better time to embark on a career in IoT analytics.”
Healey notes that today’s more progressive MBA programs are beginning to implement data science coursework, melding computer science and programming skills with database tools that aren’t necessarily built on SQL. The focus now is on building statistical models, using tools such as Python and R, which give students the basics they need for a career in edge analytics. “A database like Vertica lets you work with languages like Python without having to know any SQL,” says Healey. “My advice for students is to focus on Python, R, statistics, and at least one core programming language.”
“Pursue SAS and SPSS,” Healey adds. “These are very polished packages around statistical programming. Everyone is using those across the board. And make sure you keep your portfolio of technologies up to speed.”
Ben Smith, product marketing manager for HPE, adds a note of caution, saying, “As you’re building your portfolio of skills, don’t invest a lot of time in fly-by-night technologies. You want to pick the ones that are going to endure.” Adds Healey, “Remember that Java was supposed to replace everything 20 years ago. Four years ago, Hadoop was going to take over all databases. Those things never happened.”
Working in edge analytics
Getting into a career in edge analytics doesn’t just mean crunching numbers to build predictive models for industrial equipment. An edge analyst may also consider a career path in data-driven software development (since, increasingly, those who do the programming are also those who analyze the data), data engineering, data visualization, or IoT architecture design. “If the Internet of Things becomes as big as everyone thinks it is going to be,” says Healey, “it will open up many other career paths.” Security, he says, is one of the biggest emerging sectors, as the data generated by edge analytics is becoming essential in ensuring that devices are uncompromised and free from intrusions.
Who’s hiring edge analysts? It’s not just traditional industry, says Healey, and workers who are interested in edge analytics beyond the world of industrial or consumer IoT will find that these skills have broad applicability in other industries and that career opportunities abound.
On Wall Street, Healey says, data scientists can “name their price” if they wish to put their skills to use analyzing financial markets. Banks are increasingly hiring analytics pros to handle risk management calculations and models. And in the more consumer-facing worlds of retail and gaming, web companies are now finding that analytics have become their lifeblood. “They do everything from look at how ads are performing and how well they are being monetized to what causes gamers to abandon their app,” says Healey.
All told, any business that has a potential for disruption—be it through a mechanical failure or customer behavior—is likely to be making use of edge analytics. Smith notes that this may not sound like the sexiest career path, as GE has cheekily recognized with its multimillion-dollar ad campaign designed to attract developers to do industrial work instead of trying to jump on the next Web 3.0 or mobile app bandwagon. “IoT is transforming some big industries,” Smith says. “It’s an exciting world.”
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