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Analytics & AI

24 Mar

Posted at 06:00h in 1. IOT COMPONENTS, 2. SERVICE PROVIDERS, 4. MARKET, Analytics & AI, COMMERCIAL IOT, CONSUMER IOT, INDUSTRIAL IOT, INFRASTRUCTURE IOT, Vendor, Videos
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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 ...

19 Aug

Posted at 09:00h in 1. IOT COMPONENTS, Analytics & AI, COMMERCIAL IOT, CONSUMER IOT, INDUSTRIAL IOT, INFRASTRUCTURE IOT, Season 1 Podcasts, Vendor
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Episode 27

Descriptive analytics is nothing new, however IoT is applying evolutionary forces to make it adapt to unstructured sensor data and evolve into a mechanism of discovery rather than report generation. Tools that blend traditional business intelligence, analytical modeling and visualization now help data scientists discover the story behind the data which can lead to valuable insights for the enterprise.
Listen to this podcast (or read the transcript) with Dave Rubal about how to apply descriptive analytics to your Internet of Things ...

22 Jul

Posted at 03:35h in 1. IOT COMPONENTS, Analytics & AI, COMMERCIAL IOT, CONSUMER IOT, INDUSTRIAL IOT, INFRASTRUCTURE IOT, Season 1 Podcasts, Vendor
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Episode 26

OK, get ready for it, we’re going to get down and dirty with predictive analytics and when I say dirty, I mean the mathematics of the different forms of predictive models dirty. Geek fest? Yes, but close your eyes and extrapolate how predictive analytics can be applied to your situation. By understanding how it works you will also understand the limits of what it can and cannot do.
Listen to this podcast (or read the transcript) with Anil Gandhi and emerge with a better understanding of predictive analytics and how it really relates to real-time and descriptive analytics ...

08 Jul

Posted at 04:00h in 1. IOT COMPONENTS, Analytics & AI, COMMERCIAL IOT, CONSUMER IOT, INDUSTRIAL IOT, INFRASTRUCTURE IOT, IoT Platform, Season 1 Podcasts, Vendor
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Episode 24

In the Internet of Things there are generally three classes of analytics performed: real-time analytics done of the fly alerting you to anomalies; predictive analytics performed as a post process yielding a prediction and confidence level and descriptive analytics that reports on past, present or future data with visualizations that often result in the biggest insights.
Listen to this podcast (or read the transcript) with Shepherd Shi, as well as the steps that are taken before and after ...

09 Jun

Posted at 01:30h in 1. IOT COMPONENTS, Analytics & AI, COMMERCIAL IOT, CONSUMER IOT, INDUSTRIAL IOT, INFRASTRUCTURE IOT, IoT Cloud, IoT Platform, Season 1 Podcasts, Vendor
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Episode 22

How much IoT data should you keep? It’s not clear. The more data you keep, the more data transmission and storage costs you’ll incur. However thinning out your data store means throwing away potential future insights, potential answers to future questions and potential new information products to expand your business – all value generators unique to IoT products.
Listen to this podcast (or read the transcript) with Steve Stover about balancing the costs and the technology approaches to maximize your Internet of Things data value ...

08 Apr

Posted at 22:10h in 1. IOT COMPONENTS, 3. BUSINESS, Analytics & AI, COMMERCIAL IOT, INDUSTRIAL IOT, INFRASTRUCTURE IOT, Transportation, Vendor, Videos
1 Like


The biggest shift for traditional companies becoming IoT companies is transitioning into an Information Technology company. IoT technology isn’t plug ‘n’ play but it can be built. More difficult is changing a company’s culture to revolve around the information.
Watch this video (or read the transcript) for a primer on big data and the interplay between analytics, databases and cloud services ...

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