I had the honor of taking part in an episode of the IoT show hosted by Allan Behrens on the topic of how analytics, AI, and machine learning can help provide more insights into business and manufacturing.
Below are some of the questions and answers that I covered during the show. You can get the full key learnings document here.
What’s new with analytics and the internet of things?
“There’s a massive change happening because of the wider ability and power of machine learning. In particular, it’s safe to say that every product, service, and internal process will automatically get better over time and as people use it, thanks to machine learning.
We tend to use analytics in the form of traditional business intelligence. We have an industrial process, and I use analytics to try to figure out how to improve that. But with machine learning a lot of that work can start being done by the algorithms themselves — and the more machines you have, the more data you have, the better the algorithms can start working and learning from any remaining exceptions. “
What are the new opportunities?
“I think there are some incredible new internet of things opportunities. The internet of things has always been about revealing processes that were previously invisible — using sensors to gather data to see and optimize a process that couldn’t before.
“But there’s been an explosion in the types of sensors you can use. For example, we are working right now with a company that is one of the leaders in palm oil, and they’re flying drones across their plantations, which take pictures, and then they are using image detection and machine learning to turn that into valuable information about how fast each tree is growing, whether it’s got a problem, changing color compared to other trees, etc. So it’s providing a level of visibility that was just never possible before. So it’s introducing this notion of internet of things to a lot more companies beyond traditional manufacturers.”
What is artificial intelligence, and how can I use it?
“Artificial intelligence is a “socio-technical” or marketing term. We generally use it to mean computers doing things that until now only people could do. It’s not really associated with a technology. There are various technologies that are associated with artificial intelligence, but the one that is generating most of the real possibilities right now is the advances in machine learning. Machine learning, in turn, is any algorithm that improves itself based on the data.
“The biggest thing that we’re finding is important is to take a step back from whatever you’re doing and rethink some of your processes. We’ve had a lot of success using design thinking methodology to rethink the customer experience, looking at every aspect of that customer journey and looking at how new technologies can help.”
How to start with your investments?
“There’s a lot of hype around machine learning, analytics, and artificial intelligence, but the key is to start small. The easiest applications for machine learning are wherever you find complex, repetitive decisions that are carried out hundreds or thousands of times a day. This includes things like preventative maintenance, logistics problems, etc. Those kinds of processes where you are using machine learning to increase efficiencies, to automate decisions that used to be handled by people, that’s typically the biggest opportunity.”
What are the challenges?
“There are a couple of things that jump to mind. The first is gathering and combining all of the right data. This has always been hard, but the volumes of sensor data and the data quality problems, and above all, trying to mix that sensor data with the information from more traditional enterprise systems in a way that is appropriately governed is really quite hard. There are new data pipelining systems where you can introduce governance more easily — consistent, auditable ways of moving information from one place to another.
“And the other thing that jumps to mind is that that once customers have worked through the technology issues, they realize they have a lot of business model issues, and those can be a lot harder to work out — getting people to pay in new ways, with new partners, figuring out who owns what, etc. That actually takes up a lot of our customers’ time.“
What are the future trends?
“What I’m fascinated by is this self-improving notion that I talked about earlier. For example, we’re working with city of Nanjing, in China. It’s very fast-growing. We first worked with them on a project to gather information from all of their taxis and use that to optimize the flow of taxis throughout the city. That was successful and they’ve now extended it to all the other public forms of transport. The city is full of cameras and sensors and all of that information gets fed into a big machine learning model and then it’s used to optimize the traffic flow in real time. And because it’s using machine learning, it’s getting better and better at it over time. So it can make adjustments to the timing of traffic lights, for example, based on the time of day, whether there’s a large sporting event, whether it’s a school holiday, etc. So the system is optimizing itself to an amazing extent. And that’s a glimpse of the future for every kind of business.”
“One of the big new opportunities is with design thinking, approaches that are really oriented around fast, agile prototyping, to test things out with small groups of people. You can do that much cheaper, in a more agile way than ever before. So you don’t have to embark on a huge big expensive project: you can come up with an idea and test it quickly, with a small audience, relatively cheaply.”
Where can people go for help and advice?
“I would urge people to look at best practices across other industries. One of the interesting things is that there’s much more blurring of industry boundaries. If you’re an oil and gas company, you can probably learn something from a luxury retailer now. It’s fascinating to what extent the technologies are transversal and people are doing ultimately very similar things in retail, or healthcare — so I’d go and read widely, don’t just stick to your own industry. At SAP we have a number of IoT accelerators that we have created with our customers around high-opportunity areas.
“And you should look internally, for people who are interested in moving the company forward in creative ways. Internal hackathons have been really interesting in every company that’s run them. You make it open to anybody who is interested. You look around for data sources. You ask people to suggest data sources and suggest uses for that data, and you bring them all together with some agile platform, typically cloud-based, to allow them to hack that data together, from the devices and display it as small applications. And just see where it goes.”