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In calculus, the second derivative is the measure of instantaneous acceleration or velocity of a function of a function f. For example, the second derivative of the position of a vehicle with respect to time is the instantaneous acceleration of the vehicle, or the rate at which the velocity of the vehicle is changing with respect to time. The second derivative is a measure of how fast things are changing and in Leibniz notation, it looks like this: Where the last term is the second derivative expression[1]. On a graph, the second derivative corresponds to the curvature or concavity of the graph. The graph of a function with a positive second derivative bows downward (that is, is concave when viewed from above), while the graph of a function with a negative second derivative curves in the opposite way (see Figure 1). Figure 1: The Second Derivative or Rate of Instant... (more)

Can Design Thinking Unleash Organizational Innovation? | @ThingsExpo #IoT #M2M #BigData

I am so blessed with smart friends, and I am fortunate to count John Morley among that group. John recently introduced me to Stanford’s Design School (d.school). To say I was blown away would be an understatement. I was so jazzed by the one-hour tour that I have signed up for a 3-hour class (very expensive…costs like $12…my Starbucks coffee that will get me fired up for the class costs almost that much). So what about design thinking got me so jazzed? Three things immediately jumped out at me about the design thinking concept: The design techniques that the school teaches are very similar to the envisioning techniques that we use in our Vision Workshop consulting engagements at Dell EMC, so I’ve uncovered this opportunity to expand our Vision Workshop methodology with some new tools, tips, and techniques. There appears to be a very close correlation between the desi... (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)

Business Model Transformation | @CloudExpo #BigData #DigitalTransformation

Business Model Transformation and What it Means to the Data Industry I recently read an MIT Sloan Management Review article by Clayton Christensen’s recent book titled “The Hard Truth About Business Model Innovations.” While the article is full of great observations about business model transformation, the most important motivation for business model transformation is found at the end: “..our understanding of the business model journey allows us to see that, over the long term, the greatest innovation risk a company can take is to decide not to create new businesses that decouple the company’s future from that of its current business units.” We use the Big Data Business Model Maturity Index as a vehicle for engaging with our clients about how they can leverage data and analytics to transform their business models (see Figure 1). Figure 1: Big Data Business Model Ma... (more)

In 2014 Big Data Investments Will Account for Nearly $30 Billion - Eventually Accounting for $76 Billion by 2020 End

DALLAS, Aug. 21, 2014 /PRNewswire-iReach/ -- Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to R&D. Photo - http://photos.prnewswire.com/prnh/20140821/138541 "Big Data Market: 2014 – 2020 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts" Key Findings: In 2014 Big Data vendors will pocket nearly $30 Billion from hardware, software and professional services revenues Big Data investments are further expected to grow at a CAGR of nearly 17% over the next 6 years, eventually accounting for $76 Billion by the end of 2020 The market is ripe for acquisitions of pure-play Big Data startups, as competition heats up between IT incumbents Nearly every large scale IT ven... (more)

Big Data Storymap Revisited | @BigDataExpo #BI #IoT #M2M #BigData #Analytics

In January 28, 2013, we released the “Big Data Storymap.” Since releasing the storymap, we have gotten lots of positive feedback. It really seemed to work in highlighting the key aspects and approaches to achieving big data success. So I thought I’d take the opportunity to re-visit the storymap to see what we have learned over the past nearly 4 years – what we got right and what we need to tweak – to ensure that the storymap is as insightful and actionable to readers as ever (see Figure 1). Figure 1: Big Data Storymap Landmark #1: Explosive Market Dynamics The purpose of Landmark #1 was to highlight the market challenges that were necessitating a different approach to integrating big data (data and analytics) into one’s business (we used cute landmarks instead of phases to keep in the spirit of the storymap). In the original blog, we discussed how organizations th... (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)

Don’t Call #BigData Dead | @CloudExpo #IoT #AI #ML #MachineLearning

You can call me biased, or out of touch, but over the past two years, I’ve been reading articles and blogs about how Big Data is going away, dying, or already dead. So what changed? Is Big Data falling into Gartner’s dreaded trough of disillusionment? Did someone discover that predictive analytics could have a butterfly effect and change the course of history and hence, we should abandon these voodoo analytics practices? Did we figure out that we actually don’t have enough data to call it “Big” data? Did we already finish analyzing all the data and we’re all done? Or… are the people calling it dead really just a bunch of marketers who are looking move on to the next new thought leadership topic, so they write one final blog to make one final impression just before they abandon the topic? Bingo. I’m looking at you… industry pundits, thought leaders, and analysts. T... (more)

Data Is a New Currency | @CloudExpo #BigData #BI #AI #ML #Analytics

This guest blog is provided by Brandon Kaier (@bkaier). Brandon has more than two decades of experience in the IT industry as a transformational leader.  Kaier is responsible for setting the strategy, defining the service line offerings and capabilities as the Field CTO for the North America. These responsibilities include bringing net new products and solutions to market with a focus on the deployment of solutions enabling analytics and agile application development. Most recently Kaier held positions on Technical Architecture team and Strategy and Direction team for Dell EMC’s Data Lake solutions. Kaier also brings experienced based, Dell EMC IT best practices, Big Data and Transformational strategies to Dell EMC customers. “Data is the new Oil.” Has anyone not heard this phrase yet? This analogy was first presented by Clive Humby at the Association of National A... (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)