You're likely to encounter some of these characters on your digital transformation journey. Here's who to be on the look out for, and how to work more productively with them.
Getting ahead of the IoT skills gap before it's too late
The Internet of Things is coming fast, and it will drive demand for some IT skills like never before, warns David Foote, partner and chief analyst at the research and analysis firm Foote Partners. In part one of our three-part interview, Foote explained how a lack of IT skills holds most organizations back, and how they need to develop the skills they need from within. In part two, he explains what IT leaders must do now to make sure they have the skills they need to take advantage of IoT.
The Enterprisers Project (TEP): What is the relationship between big data and the Internet of Things?
Foote: IoT is big data on steroids. Everything you touch now records something to reflect how you use that device or that piece of clothing. Everything is now monitoring everything else, which means that we have a huge amount of unstructured data.
IoT is all about transforming business and business processes, but we have a little bit of time to prepare. It's like this tidal wave coming at us. When you see a tidal wave, you're pretty much dead because you're probably not on high ground. But if someone says that five days from now there's going to be a tidal wave, that's more useful.
TEP: So what should IT organizations be doing now to get ready for that tidal wave?
Foote: Assuming there's a skills gap for IoT, let's identify which skills we're talking about. The device or "Things" side of IoT is about circuit design and circuit controllers. It's data scientists, they're at the middle of all big data and data management. It's also network engineers, electrical engineers, AI, and cybersecurity people.
The Internet side of IoT, I think people understand pretty well. But I think the greatest opportunity is in connecting the two. That requires a lot of designers, product designers, user interface people, business intelligence, and people who can do predictive analytics. You're working with SQL, relational databases, machine learning, and machine-to-machine communication. They're predicting that 40 percent of all transactions will be between machines, not between machines and people.
TEP: So companies should be hiring now to fill that anticipated gap?
Foote: When you look at adoption rates in technology and you can see there's going to be a cresting of demand in a particular area, the question is do you have the expertise and leadership in-house to get in front an issue, and will you satisfy the skills demand once we get there? Let's say you have a one-to-two-year lead time. The problem I've seen is that most companies do a very poor job of training people even when they have that lead time. They're not good at identifying and filling skills gaps with internal candidates. But because the labor market is tight in this area, they're being forced to learn how to do that.
TEP: Speaking of the labor market, many of the positions you're describing are big data positions. And data analytics and data science have been considered a hot job skill for years. So it's surprising that your research shows demand for these skills has risen and fallen, with average compensation for big data expertise actually decreasing three years ago. What's going on here?
Foote: A lot of companies got into big data and fell flat on their faces because they didn't realize that getting value out of big data requires a lot of transparency and sharing. That's one reason these flat, young companies do so well at it. They don't have big military-model hierarchies that older companies have. There's a lot of natural sharing and transparency that goes on. That isn't what happens in larger, siloed organizations, where there's hoarding.
So many companies weren't getting enough ROI on the money they were spending because they weren't culturally set up to take advantage of big data. Another problem is that managers, especially when they're baby boomers, are not accustomed to data-driven decision-making. Not everyone is like Jeff Bezos. A lot of managers are used to making decisions by trusting their own instincts and experience.