predictive

THis podcast discusses the Predictive Analytics & Accelerated Underwriting Survey Subcommittee of the Society’s Committee on Life Insurance Mortality and Underwriting Surveys report on the results of a survey designed to obtain feedback on several current hot topics in the industry, namely predictive analytics, accelerated underwriting and enhanced underwriting. This survey was sent to US and Canadian direct life insurance companies.

Jim Marrs tribute.

Adam shares his experience with scary, recently declassified technology.

New technology recovers sound from lost recordings.

Predictive Programming: 19th century novel predicts President Trump.

Seymour Hersh confirms Seth Rich contacted Wikileaks.

Franz Kafka, Rudolf Steiner, and Helena Blavatsky walk into a bar...

Prognos is a healthcare technology company using AI and advanced analytics. More than 7 Billion data points, 135 million patient files, and 500 proprietary algorithms put into play to drive early detection of chronic and ID-delayed diseases.

Merck is onboard with investment…Cigna has rolled it out…and we have their CMO Dr. Jason Bhan with us today.

In this episode, Dr. Steve and Dr. Bhan discuss:

• The power of Prognos' AI platform and risk-assessment engine
• Prognos vs. IBM's Watson
• A wide reach into life science, payer, and diagnostics industries
• Their bold new approach into early disease detection

Joe Barkai provides examples of this in the discussion and we talk about 3rd party service offerings that go beyond condition monitoring and move to predictive analytics. Other topics include the evolution of the workforce, how maintenance techs could become "citizen data scientists,"  IoT innovation, standards for data interpretation, and how manufacturer should think about investment in IIoT.

Meet your "Big Dreams? Big Data!" co-hosts and learn what ‘big data’ means to them – and, more importantly, why it matters to you!

vTapestry is a partnership between Christine Maxwell and Rebekah Nix dedicated to creating and delivering high-quality development tools and resources to support professionals in meeting the diverse challenges presented by a global community. vTapestry's combined expertise and experience in technology, education and publishing lend a unique capacity for realizing the potential of innovative ideas that lead to real-world solutions.

http://www.vTapestry.com

The Data Skeptic Podcast is launching a contest- not one of chance, but one of skill. Listeners are encouraged to put their data science skills to good use, or if all else fails, guess!

The contest works as follows. Below is some data about the cumulative number of downloads the podcast has achieved on a few given dates. Your job is to predict the date and time at which the podcast will recieve download number 27,182. Why this arbitrary number? It's as good as any other arbitrary number!

Use whatever means you want to formulate a prediction. Once you have it, wait until that time and then post a review of the Data Skeptic Podcast on iTunes. You don't even have to leave a good review! The review which is posted closest to the actual time at which this download occurs will win a free copy of Matthew Russell's "Mining the Social Web" courtesy of the Data Skeptic Podcast. "Price is Right" rules are in play - the winner is the person that posts their review closest to the actual time without going over.

More information at dataskeptic.com

In episode 17 you'll meet Jeff Revoy, CEO of Viralheat and Paul Sebastien, Chief Marketing Officer at Deutsche Telekom HBS / T-Mobile. You'll learn how T-Mobile launched a successful pilot program for a new B2B VoIP offering using Viralheat for predictive social analytics. 

This was the first time T-Mobile was attempting to launch a B2B product. Paul’s team embarked on comprehensive reach across multiple networks. Targeted keywords were established to determine sentiment about the T-Mobile brand. Additionally, Paul set out to identify customers who are not satisfied with their existing VoIP system. The Viralheat insights were integrated with Salesforce data and other look up data to identify relevant people for a pilot program. Paul intelligently selected a target list that resulted in 500 target customers for a pilot. Paul said “because they are so relevant and engaged, they are more than just customers, they became influencers.” 

Tune into this episode of the Social Business Engine podcast. Visit our show notes for a download of the T-Mobile / Viralheat case study using predictive social analytics at www.socialbusinessengine.com.