William Schmarzo

About Me

William Schmarzo is Chief Technology Officer, Dell EMC Global Services Big Data Practice at EMC. He is the author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”. He’s written white papers and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives.  He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course.

The Number One IOT Challenge: Use Case Identification, Validation and Prioritization

There is a bounty of business use cases from which the business can choose in order to monetize their IOT efforts. The best approach is to build out your IOT Business Strategy with one use case at a time. In this manner, not only do you incrementally build out your IOT analytic, data, technology and architecture capabilities, but this enables the organization to build upon the work of previous use cases – to capture, share and refine the IOT data and analytic assets that are key drivers to IOT monetization.

Monetizing the Internet of Things (IoT)

Creating an IoT Monetization roadmap should be the top priority for any IoT initiative.  Take the time to identify, validate and prioritize those use cases with the key business stakeholders and constituents to ensure that you are focused on the right use cases in the right order. There is no value in generating and collecting the data if you don’t have a plan for how to monetize that data.

Back to the Future: 2018 Big Data and Data Science Prognostications

2018 will continue to see the continuing march of economics that drive innovation and market adoption of Big Data, Data Science, Machine Learning and Artificial Intelligence.  It’s a great time to be in the data and analytics business, and 2018 will just reinforce that!

Winning the Artificial Intelligence War

What are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others fumble the future? Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.

Driving AI Revolution with Pre-built Analytic Modules

Analytic Modules are pre-built engines that can be assembled to create specific business and operational applications.  They produce pre-defined analytic results or outcomes, while providing a layer of abstract that enables the orchestration and optimization of the underlying machine learning and deep learning frameworks. One example of an IoT analytic modules would be Anomaly Detection.  Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. A number of different machine learning techniques can be used to help flag and assess the severity of detected anomalies.

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