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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 ...




Episode 56

Black hat, white hat… gray hat? What does it all mean? In this context, the different colored hats refer to the different approaches to testing the cyber security of your IT, or in our case, IoT infrastructure.
Listen to this podcast (or read the transcript) where I speak with Paul Jauregui about pen testing and other things you need to know about when working with an external security assessment firm ...




Episode 55

Smart products are roughly 10x the cost of dumb or regular products and that’s a problem. It’s for good reason though, to make these products smart requires a lot of tech and infrastructure that needs to be paid for somehow. This will only be solved by new business models and not the types of business models we’re used to in enterprise IoT.
Listen to this analysis episode with Bruce Sinclair where he discusses the issues of pricing smart products and new business models that can help ...




Episode 54

IoT consumer products are more expensive than their traditional counterparts, often by order of magnitude. Not good but makes sense - the tech needs to be financed. But however innovative the products are, higher pricing will cause a headwind. Part of the problem is consumer IoT products are still being sold with traditional product business models.
Listen to this podcast (or read the transcript) where I speak with Nate Williams about the opportunity to innovate consumer IoT business models ...


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 ...




Episode 53


With pundit projections in the billions of devices and trillions of dollars, these are heady times for IoT. But there are challenges – big challenges in our way. This keynote presents how we get from where we are today to an outcome-based economy, realizing the business promise of IoT.
Listen (or read the transcript) to Bruce Sinclair’s opening keynote address at Internet of Things World 2016 on IoT business and technology and the outcome-based economy ...




Episode 52

It’s a symbiotic relationship – the big corporation, in search for that innovative edge, and the small startup, in search for support to get their ideas off the ground. Both have what the other needs – desperately. Coming together they form one type of ecosystem. To be players, all IoT companies – big and small – must be part of one or more ecosystems.
Listen to this podcast (or read the transcript) where I speak with the folks at TechrIot about their approach of bringing IoT companies together ...