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Bill Schmarzo

Bill Schmarzo

CTO, Dell EMC Services (aka “Dean of Big Data”)
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger 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. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide. Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata. Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications. Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

Democratizing Artificial Intelligence, Deep Learning and Machine Learning with Dell EMC Ready Solutions

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are at the heart of digital transformation by enabling organizations to exploit their growing wealth of big data to optimize key business and operational use cases.

• AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence (e.g. visual perception, speech recognition, translation between languages, etc.).
• ML is a sub-field of AI that provides systems the ability to learn and improve by itself from experience without being explicitly programmed.
• DL is a type of ML built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network.” A DL framework is SW that accelerates the development and deployment of these models.

See “Artificial Intelligence is not Fake Intelligence” for more details on AI | ML | DL.

And the business ramifications are staggering (see Figure 1)!

Figure 1: Source : McKinsey

And Senior Executives seem to have gotten the word.  BusinessWeek (October 23, 2017) reported a dramatic increase in mentions of  (more…)

Scientific Method: Embrace the Art of Failure

I use the phrase “fail fast / learn faster” to describe the iterative nature of the data science exploration, testing and validation process.  In order to create the “right” analytic models, the data science team will go through multiple iterations testing different variables, different data transformations, different data enrichments and different analytic algorithms until they have failed enough times to feel “comfortable” with the model that they have developed.

However an early variant of this process has been employed a long time: it’s called the Scientific Method. The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry is commonly based on empirical or measurable evidence subject to specific principles of reasoning[1] (see Figure 1).

Figure 1: The Scientific Method

The Scientific Method is comprised of the following components: (more…)

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