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What Tomorrow's Business Leaders Need to Know About Machine Learning Sometimes I write a blog just to formulate and organize a point of view, and I think it’s time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, especially tomorrow’s business leaders (tip for my next semester University of San Francisco business students!). Machine learning is a key capability that will help organizations drive optimization and monetization opportunities, and there have been some recent developments that will place basic machine learning capabilities into the hands of the lines of business. By the way, there is an absolute wealth of freely-available material on machine learning, so I’ve included a sources section at the end of this blog for folks who want more details on machine lea... (more)

Key to Data Monetization | @CloudExpo #IoT #BigData #Analytics #AI #DX #DigitalTransformation

Many organizations are associating data monetization with selling their data. But selling data is not a trivial task, especially for organizations whose primary business relies on its data. Organizations new to selling data need to be concerned with privacy and Personally Identifiable Information (PII), data quality and accuracy, data transmission reliability, pricing, packaging, marketing, sales, support, etc. Companies such as Nielsen, Experian and Acxiom are experts at selling data because that’s their business; they have built a business around gathering, aggregating, cleansing, aligning, packaging, selling and supporting data. So instead of focusing on trying to sell your data, you should focus on monetizing the customer, product and operational insights that are gleaned from the data; insights that can be used to optimize key business and operational processe... (more)

What Is #DigitalTransformation? | @ThingsExpo #AI #DX #IoT #SmartCities

For a phrase that’s being thrown around a lot recently, what does “Digital Transformation” really mean? When someone says that they want to digitally transform their business, what does one really mean, why do they want to do it, and should they approach this “digital transformation” process? First off, let’s start with a definition. If we don’t know what we are trying to achieve, then how do we know how to get there? Or to quote the famous Greek philosopher Yogi Berra: “If you don’t know where you are going, you’ll end up someplace else.” In a recent blog “How To Achieve Digital Transformation,” I stated with the following definition of Digital Transformation: “The coupling of granular, real-time data (e.g., smartphones, connected devices, smart appliances, wearables, mobile commerce, video surveillance) with modern technologies (e.g., cloud native apps, big data ... (more)

Peter Principle and #BigDatas | @ThingsExpo #AI #ML #DX #IoT #IIoT #M2M

Wikibon just released their “2017 Big Data Market Forecast.” How rosy that forecast looks depends upon whether you look at Big Data as yet another technology exercise, or if you look at Big Data as a business discipline that organizations can unleash upon competitors and new market opportunities. To quote the research: “The big data market is rapidly evolving. As we predicted, the focus on infrastructure is giving way to a focus on use cases, applications, and creating sustainable business value with big data capabilities.” Leading organizations are in the process of transitioning the big data conversation from “what technologies and architectures do we need?” to “how effective is our organization at leveraging data and analytics to power our business models?” We developed the Big Data Business Model Maturity Index to help our clients to answer that question; to be... (more)

Demystifying #DataScience | @CloudExpo #BigData #AI #ArtificialIntelligence

[Opening Scene]: Billy Dean is pacing the office. He’s struggling to keep his delivery trucks at full capacity and on the road. Random breakdowns, unexpected employee absences, and unscheduled truck maintenance are impacting bookings, revenues and ultimately customer satisfaction. He keeps hearing from his business customers how they are leveraging data science to improve their business operations. Billy Dean starts to wonder if data science can help him. As he contemplates what data science can do for him, he slowly drifts off to sleep, and visions of Data Science starts dancing in his head… [Poof! Suddenly Wizard Wei appears]: Hi, I’m your data science wizard to help alleviate your data science concerns. I don’t understand why folks try to make the data science discussion complicated. Let’s start simple with a simple definition of data science: Data science is a... (more)

Golden State Warriors Analytics Exercise | @BigDataExpo #BigData #Analytics

For a recent University of San Francisco MBA class, I wanted to put my students in a challenging situation where they would be forced to make difficult data science trade-offs between gathering data, preparing the data and performing the actual analysis. The purpose of the exercise was to test their ability to “think like a data scientist” with respect to identifying and quantifying variables that might be better predictors of performance. The exercise would require them to: Set up a basic analytic environment Gather and organize different data sources Explore the data using different visualization techniques Create and test composite metrics by grouping and transforming base metrics Create a score or analytic model that supports their recommendations I gave them the links to 10 Warrior games (5 regulation wins, 3 overtime losses and 2 regulation losses) as their star... (more)

Azure Stack Hybrid Cloud From @DellEMC | @CloudExpo @Azure #AI #ML #DX

Dell EMC Announce Azure Stack Hybrid Cloud Solution Dell EMC have announced their Microsoft Azure Stack hybrid cloud platform solutions. This announcement builds upon earlier statements of support and intention by Dell EMC to be part of the Microsoft Azure Stack community. For those of you who are not familiar, Azure Stack is an on premise extension of Microsoft Azure public cloud. What this means is that essentially you can have the Microsoft Azure experience (or a subset of it) in your own data center or data infrastructure, enabling cloud experiences and abilities at your own pace, your own way with control. Learn more about Microsoft Azure Stack including my experiences with and installing Technique Preview 3 (TP3) here. What Is Azure Stack Microsoft Azure Stack is an on-premise (e.g., in your own data center) private (or hybrid when connected to Azure) cloud pl... (more)

Economic Value of Data (EvD) Challenges | @BigDataExpo #BigData #Analytics

Well, my recent University of San Francisco research paper “Applying Economic Concepts To Big Data To Determine The Financial Value Of The Organization’s Data And Analytics Research Paper” has fueled some very interesting conversations. Most excellent! That was one of its goals. It is important for organizations to invest the time and effort to understand the economic value of their data because data has a direct impact on an organization’s financial investments and monetization capabilities. However, calculating economic value of data (EvD) is very difficult because: Data does not have an innate fixed value, especially as compared to traditional assets, and Using traditional accounting practices to calculate EvD doesn’t accurately capture the financial and economic potential of the data asset. And in light of those points, let me share some thoughts that I probably... (more)

Natural Language Processing | @BigDataExpo #BigData #Analytics #DataScience

“Apophenia is the propensity to see patterns in random data.”  We encounter it all the time in the real world. Examples include gamblers who see patterns in how the cards are being dealt or investors who imagine patterns in the movement of certain stocks, or basketball fans who believe that their favorite player has the “hot hand.” But apophenia has no place in the world of data science, especially when data science is trying to help us make better decisions about critical things such as the quality of healthcare, where to allocate police resources, ensuring that our airplanes operate effectively or making investment decisions that determine our retirement readiness. Understanding the differences between epiphany (a sudden, intuitive perception of or insight into the reality) and apophenia (the perception of or belief in connectedness among unrelated phenomena) is ... (more)

Is #Blockchain Enabler of Data Monetization? | @CloudExpo #BigData #FinTech

Special thanks for the help on this blog to the coolest, most hip group of industry experts that I have ever met: the Pathfinders. The Pathfinders is an elite forces group of master system engineers inside of Dell EMC who tackle our customers’ most difficult and inspiring challenges. I am honored to be part of that club! Suppose an autonomous vehicle learns of a more efficient route and wants sell this knowledge to other autonomous cars for a fee (using blockchain to handle machine to machine transaction). Suppose the autonomous vehicle could start to monetize itself; to self-fund its own operations and the acquisition of goods and services such as gas, repairs or vehicle upgrades (using blockchain to conduct commerce).  Now suppose the autonomous vehicle could couple real-time analytics of vehicle performance and maintenance with real-time bidding for maintenance... (more)

Difference Between #BigData and Internet of Things | @ThingsExpo #IoT #M2M

A recent argument with folks whose intelligence I hold in high regard (like Tom, Brandon, Wei, Anil, etc.) got me thinking about the following question: What does it mean, as a vendor, to say that you support the Internet of Things (IoT) from an analytics perspective? I think the heart of that question really boils down to this: What are the differences between big data (which is analyzing large amounts of mostly human-generated data to support longer-duration use cases such as predictive maintenance, capacity planning, customer 360 and revenue protection) and IoT (which is aggregating and compressing massive amounts of low latency / low duration / high volume machine-generated data coming from a wide variety of sensors to support real-time use cases such as operational optimization, real-time ad bidding, fraud detection, and security breach detection)? I don’t beli... (more)