We’re in the booming era of the internet of things – things are changing, and changing fast. IoT is touching almost everything in both the commercial and consumer worlds.
In parallel, the automotive industry is going through a major rebirth, in some cases kicking and screaming. Where the automakers (in their parlance, OEMs) have had over 100 years of self-centricity – and not having to deal with “ecosystems” other than their respective galaxies of suppliers and dealers – they’re now grappling with a sea of change being caused by, well, the IoT.
Some are already calling it the internet of moving things. And why not? We now have mavericks like Tesla bringing up highly sensor-laden, over-the-air-updating electric supermobiles that make the majority of land vehicles look like luddites. Every OEM, components supplier, and others in the automotive realm, are scrambling to find not only their place in this new world, but to posit their future relevance.
So here’s the issue
I talk to a lot of people about all of this.
In my day job, I speak regularly with major analytics and consulting firms, big tech companies, government agencies, you name it. Usually the discussion surrounds the issues of how to get the enormous volumes of IoT data being generated out of the silos and into some useful form.
Often that means performing a number of functions to get the data across sector-lines to other organizations, where it can be used for purposes that were not imagined when the sensors were installed in the first place. In other words, cross-sector data utilization. This has been done for decades with other forms of data, e.g. financial services, or how Facebook and Google sell (our) data to companies who apply clever algorithms to figure out what we might want to do, buy, watch, and more.
And here’s what I’ve observed: The organizational structures of most large firms are still divided along legacy lines. This is understandable, since those lines have been in place for a very long time. But something happens when you mesh the old guard (automotive) and the upstart (the IoT).
Of course you can argue that the IoT in some form has existed for decades, but we’re talking about what the progression of Moore’s Law has wrought in the modern day. Moore’s Law is salient because in the majority of organizations that have an IoT business practice, division, subsidiary, product or service line, etc., the origins often came from something to do with semiconductors. This is understandable, since the modern era of IoT – literally from the time the term first began floating around – started with devices, a.k.a. things, that were connected to the internet. For a long while, it’s been about getting things out there and connected.
So came the autos
In concert with the IoT showing up in non-mobile form, in environments and instances ranging from home thermostats to enormous factories, there’s been a gradual introduction of connected, microprocessor-based devices that are mobile. Cars are only one slice of this, but most visible as the “thing” that you’re most likely to interact with on a daily basis, unless your job happens to include rolling in road graders, farm combines or freight trains. Considering that newer cars contain up to fifty microprocessors, it’s getting serious. And they are (finally) becoming connected. To the internet. Yep, it’s starting to look a lot like automobiles blending into the IoT.
So why does this analysis matter?
For one thing, you can’t have self-driving vehicles without some form of connectivity. And LOTS of sensors. Take it to the next step, where you have autonomous, electrically-powered cars and trucks running around, and it’s a big moving IoT.
The traditionally important attributes of automaking, such as having the know-how to deal with design, development, manufacturing and servicing of internal combustion engines and volatile liquid fuel, start to fade. The replacements will be boxes of electronics. What’s left for the automaker to differentiate on are essentially the safety and ergonomic features. Again, to see what would happen if you approached this from the ground up, get a ride in a Tesla. A rolling box of electronics. A very darn cool one (and I might mention, scary fast).
So what about the old lines along which organizations are set up?
Exactly the point. It’s hard to be agile and progressive when you’re hamstrung by archaic structures. Tesla didn’t have any such baggage. Thus, if your company or agency or university intends to have a big part in how this all goes forward, it’s probably a good time for you to step back and take a serious look at structure.
For example, one big analytics firm that I know of still has their automotive and (semiconductor-born) IoT practices set distinctly from each other. Therefore when a car OEM, tier one component supplier, or any number of other clients want to work through their own forward business and structural plans, that firm has to send representatives from multiple practice areas, or fail to truly give clients what they now need.
So what now?
Combining what Netscape cofounder and hugely successful startup investor Marc Andreessen famously said, “Software is eating the world,” with another popular saying in tech, “Data is the new oil,” it’s obvious that everyone in every industry needs to be focusing a lot of attention on datafication.
This couldn’t be more true than in the automotive industry. It’s a definite shock to the system for almost everyone in the space, and they’re scrambling to deal with the changes. In fact, most of the big OEMs are starting to call themselves “advanced mobility” companies instead of automakers. Per the preceding, you can see what and why a realignment of practice areas, product lines, service offerings or whatever fits with your organization’s primary purpose, may be needed. And not just the outward representation, but how you’re structured internally.
It could make all the difference for you, down the road.