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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 ...




Episode 50

As the technology of IoT evolves the concept of a distributed computing environment is becoming increasingly relevant. A key component of this architecture is fog computing, blending the cloud to the edge. Being able to access computing, networking and storage resources locally makes sense for many classes of IoT deployments.
Listen to this podcast (or read the transcript) where I speak with the bigwigs of the newly formed, OpenFog Consortium ...




Episode 49

In this episode Bruce recounts recent meetings with clients and discussions with IoT design houses to discuss the current state of the art in data analytics in IoT deployments.
Listen to this analysis episode (or read the transcript) with Bruce Sinclair for the reasons why data analytics isn’t usually considered by clients and why that’s OK ...




Episode 48

Building an IoT product line isn’t just for the big guys. Small and medium size manufacturers can transform their business into Internet of Things powerhouses by following the same IoT playbook – albeit on a smaller scale.
Listen to this podcast (or read the transcript) where I speak with Christian Shaffer about the steps his 20-person company took to unlock $2 million in recurring revenue ...




Episode 47


Listen to this analysis episode (or read the transcript) with Bruce Sinclair where he analyses the latest IoT news from a business perspective. In this episode Bruce tees off from the Apple CareKit announcement to discuss why ecosystems are so important and how they differ from IoT platforms ...


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 ...