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When we’re talking about infrastructure IoT, such as airports, mass transit, pipelines and electric grids, the biggest IoT cybersecurity issue is passiveness – not being proactive enough with your infrastructure’s ongoing cybersecurity needs.
Watch this video (or read this transcript) to see Stuart Phillips discuss each of the top 3 cybersecurity issues in infrastructure IoT ...


Using the Internet of Things in retail virtualizes the shopping environment so it can be analysed to increase the store’s efficiency in converting foot traffic into sales.
Watch this video (or read this transcript) to see Benoit Cousin discuss how IoT is improving the physical shopping experience and the store’s relationship with its customer ...


Whether the Internet product is a discrete product, a system or infrastructure, more often than not, its end nodes are sitting in the wild – just waiting for a bad actor to get their hands on them. This physical accessibility introduces a new category of attack surfaces that need to be protected against.
Watch this video (or read this transcript) to see Vera Sell discuss edge device vulnerabilities in the Internet of Things and the best practices to follow to secure them ...


Yeah, data analytics can be complicated, but its application doesn’t need to be. Take discrete manufacturing for example. There’s lots of hype around more advanced analytic applications like predictive maintenance but that’s running before you even know how to walk. I know it doesn’t sound as sexy, but let’s start with operational efficiency.
Watch this video (or read this transcript) to see William Sobel discuss data analytics in manufacturing and why to start easy before tackling sexy predictive maintenance ...


Big data analytics is generally answering questions about the past, whereas streaming analytics answers questions about the present. But what if you could bring the two together and answer questions about the present based on what happened in the past?
Watch this video (or read this transcript) to see Eric Tran-Le discuss a state of the art analytics ...


Until recently we could perform Internet of Things computations in four general areas: We could compute in an external cloud, which means on one or more servers in a data center somewhere remote. We could compute “on prem”, which means on one ore more servers in the enterprises’ local network. We could compute in the fog, which means on a gateway in the OT (Operational Technology) network or on a router or switch or some other network node in the IT (Information Technology) network. Or we could compute within in the IoT device or product, which means on an on-board embedded device.
Watch this video (or read this transcript) to see Jurgo Preden discuss the state of the art in Mist computing ...


Traditional System Integrators (SIs) are setting their sights on integrating the systems of the Internet of Things. And why not; due to networking inoperability and the immature state of IoT platforms and their corresponding ecosystems, for the foreseeable future most enterprises deploying IoT are going to need a helping hand.
Watch this video (or read the transcript) video to see Jayraj Nair discuss the ins and outs of working with a System Integrator (SI) on your Internet of Things project ...


The key to value creation in the Internet of Things is the model. The model is used by both the app and analytics. It quantifies the value proposition, so the better the model, the higher the value. Developing these models in traditional markets is time consuming enough but given the volume, velocity and variety of IoT data, the load on the IoT data scientist can be overwhelming. Enter machine learning or ML for short. Machine learning can augment the skills of the data scientist by helping to select the algorithms or weighted ensemble of algorithms that provide the underlying structure for the model.
Watch this video (or read the transcript) video to see Rob Patterson discuss how machine learning is being used to help create and maintain Internet of Things models ...


A solid route to take when developing a consumer Internet of Things product is concept -> ideation -> proof of concept -> prototype -> video -> crowdfunding -> MVP -> beta with early investors -> product. Harvesting your vested customers to become true fans is an incredibly powerful way to take a product to market and doesn’t necessary sidestep traditional funding sources. One of the reasons I like it most however is because it follows my launch philosophy of Design -> Sell -> Build - especially important in IoT.
Watch this video (or read the transcript) to see John Mein discuss the details of how to start with crowdfunding to launch a successful consumer IoT product and company ...


In the data science of IoT there’s no one size fits all data model. Each situation needs to be analysed separately by your data scientist. Then the output too, needs to be custom tailored to your customer - internal or external. This output, often in the form of a dashboard, is critical in aiding the identification of value with your data. Therefore, iterate on its design as often as you do on the design of the IoT product.
Watch this video (or read the transcript) to see Christian Mastrodonato discuss standing up an Internet of Things Pilot from a Data Scientist’s perspective ...