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Episode 120

It amazes me that there are companies out there still intent on building their own middleware software to collect and transport sensor data to the cloud. This middleware is called the IoT platform and at last count, there were over 300 for sale. In this episode of the IoT Business Show, I speak with Jonathan Cobb about why it’s always better to buy or lease an IoT platform than to build it on your own. And I’ll give you a one-word hint: scale...




Episode 118

Using external data in addition to your internal data is strategic. Besides public microservices, business systems and IoT products, an emerging source of external data is the datamart, otherwise known as the data marketplace or data exchange – places where you can buy and sell data on the open market. But should you sell your data and are there any other options to do so?
Listen to this podcast (or read the transcript), where I speak with Tim Panagos about setting up your own datamart and the inherent value in doing so - even if you don’t plan on selling anything at all...




Episode 117

I have to admit, when I think datamarts, I think about buying data and when I think data monetization, I think of data as a means to monetize an IoT product or service. But what about being a seller? That is, monetizing data by selling it directly on the open market?
Listen to this podcast (or read the transcript), where I speak with Didier Navez about the mechanics of being a buyer and a seller of data...




Episode 116

Opensource software is mainstream. But opensource data? Yeah, and it can be found in a datamart near you. The thinking is, if you make your data freely available for all to use; all will improve it. It will be made more consumable, you will get feedback on how to use it and perhaps counterintuitively, you will learn how to value it.
Listen to this podcast (or read the transcript), where I speak with Adam Mayer about applying the opensource ethos to data within the data exchange...




Episode 115

One of data science’s dirty little secrets is the time spent data wrangling, that is accessing the data and then transforming it into a form compatible with your data tools and chosen analytical and learning models. Most agree this can take a full 75-80% of data science time.
Listen to this podcast (or read the transcript), where I speak with Adam Mayer about data access and form when shopping at your local datamart...




Episode 114

To truly use the Internet of Things in its fullest capacity – more than connected and more than smart – we look beyond our internal sources of data and venture onto the Internet to seek out external sources of data that when mashed with our sensor data, creates more valuable information. This can be done piecemeal by connecting directly to microservices, but a new source has popped up lately.
Listen to this podcast (or read the transcript), where I speak with Carl Rodrigues about data marketplaces and how to shop there for value...




Episode 110

AI today is getting better at learning. In fact, learning is what differentiates what we call AI from advanced analytics. Machine learning algorithms minimize an error function by autonomously and iteratively adjusting their model variables. But what’s next for AI and machine learning?
Listen to this podcast (or read the transcript), where I speak with James Canton to hear predictions on where the predictive technology of machine learning will go in the Internet of Things....




Episode 109

Convoluted Neural Networks or CNNs are a type of AI typically used in computer vision to process images, but they are also applicable to process the time series data we typically get from sensors in IoT.
Listen to this podcast (or read the transcript), where I speak with Simon Crosby about how these CNNs can be used to make predictions about the future and reduce the massive amounts of data we collect to just the important stuff....




Episode 108

AI and in particular, deep learning, is a powerful tool for uncovering useful relationships within data; but once found, can’t explain what they mean. Contrast this with humans, armed with tribal knowledge and more traditional analytics, who understand the data relationships but just can’t find as many of them.
Listen to this podcast (or read the transcript), where I speak with Drew Conway about how to find the balance between man and machine when looking for data value....




Episode 107

If you’re like me, before I started digging into AI, it all seemed so mysterious. Not only how it worked but also how it was put to work. But when thought of as a subset of analytics things come into focus – fast.
Listen to this podcast (or read the transcript), where I speak with Curtis Seare about the tools and frameworks used to incorporate AI into IoT projects....