05 Apr Illuminating the Enterprise’s Dark Data with AI in IoT
s it just semantics or is there a real difference between artificial intelligence (AI) and analytics; between machine learning (ML) and AI; between deep learning (DL) and ML, and between analytics and DL? Well, it depends on how detailed you want to go. In this episode of the IoT business show I speak with Bret Greenstein about using AI in IoT and how it’s different from using analytics.
In this episode of the IoT business show I speak with Bret Greenstein about using AI in IoT and how it’s different from using analytics.
Bret is Global Vice President of Offerings for the Watson IoT unit – where he oversees IBM’s portfolio of products and industry solutions. Bret and his team help clients design, build, and operate connected things.
The data that drives digital transformation has traditionally come from all things IT. Now with the advent of IoT, there’s a lot more data. Collecting this lifeblood of digital transformation is the simple part. The hard part is transforming it into value. Dark data is the data the enterprise has collected but is not using. Assuming collection was driven by a data plan, there’s a lot of untapped value out there. Using analytics has got us only so far; will AI or maybe more accurately, ML get us any further? I’m not sure but we’re starting to find out in this, the first episode of our mini series on AI in IoT.
Here’s What We’ll Cover in this Episode
- Size of the IoT Enterprise market: the products and services they buy.
- Use cases where AI is being effectively used in IoT today.
- The concept of dark data and how AI can shine a light on it.
- Where AI fits into the technology stack.
- What you get when you buy AI.
- Comparing AI to analytics in the context of IoT.
- Best practices for managers who want to start using AI in IoT today.
Mentioned in this Episode and Other Useful Links
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