Episode 121

Every IoT product, system or environment I’ve worked on has pivoted. And I don’t mean slight pivots, I’m talking 180 degrees or more. Why? Because the Internet of Things is new. New to the product builder and new to their customers. Because of this newness and the planning fallacy, it’s important to take a highly iterative approach to development. In this episode of the IoT Business Show, I speak with Jeff McGehee about agile development and how to use it most effectively when building a new IoT product

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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

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Episode 119

I have to admit, when the first real deployments of LPWA or Low Power Wide Area Networks were being lit up, I wasn’t all that bullish on the competition: Mobile Operators in IoT. Wrong tech with the wrong business model… Or so I thought. Fast forward a couple of years to today and those behemoths are getting their acts together. In this episode of the IoT Inc Business Show, I speak with Patrice Slupowski about the Mobile Operator approach to IoT and I have to admit… I’m impressed

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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

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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

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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

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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

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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

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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.

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