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Latest read: Big Data Using smart big data

There is nothing boring in the established insights and support for Big Data Using smart big data analytics and metrics to make better decisions and improve performance.
Big Data Using smart big data analytics and metrics to make better decisions and improve performanceRead just one ‘big data’ book and  you’ve read them all right? Not so fast. Big Data: smart big data analytics reveals how a well planned understanding of your business can better embrace select data sets for your company, organization or school to not only remain competitive but thrive in the new global economy.

For all of the Big Data blogs, books and white papers that I have read Big Data – smart big data analytics by Bernard Marr is one of the better written books. Many will benefit from this knowledge.

Bernard Marr’s challenge continues to be what most senior managers do not understand about Big Data. And he does an admirable job in chapter six: Transforming Business. There are so many examples of how Big Data can actually re-define your objectives, but many are surrounded by a sales approach I recall from work Apple’s Michael Mace. Michael was Director of Competitive Analysis at Apple and eloquently addressed FUD at a sales conference in the 90s. The same lessons apply today regarding Big Data. FUD is Fear, Uncertainty & Doubt.

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Latest read: Need, Speed, and Greed: How the New Rules of Innovation Can Transform Businesses, Propel Nations to Greatness, and Tame the World’s Most Wicked Problems

Every company and school needs to add Need, Speed, and Greed: How the New Rules of Innovation Can Transform Businesses, Propel Nations to Greatness, and Tame the World’s Most Wicked Problems to their mandatory reading list.
Need, Speed, and Greed: How the New Rules of Innovation Can Transform Businesses, Propel Nations to Greatness, and Tame the World's Most Wicked ProblemsVajay Vaitheeswaran really understands the need for innovation, change and embracing new ideas in order for America to survive and thrive into the future.

This is especially true for those in aging markets like the auto industry and higher education.

Need, Speed, and Greed is divided into three sections: Why Innovation Matters, Where Innovation is Going, and How to win in the Age of Disruptive Innovation.

This is cover-to-cover reading for everyone. I really looked deeper at the closing chapter Can Dinosaurs Dance. While applied to the American auto industry, think about the strides made by Elon Musk and Google, the application of dramatic change fits quite nicely into many universities around the country.

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Latest read: Too Big to Know

Too Big to Know: Rethinking Knowledge Now That the Facts Aren’t the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room by David Weinberger is an amazing look at how vast amounts of knowledge in our digital world has changed our ability to not only comprehend data, but how data and the internet rewire how our brain’s process information.
Too Big to KnowIn a way this book is about networks of knowledge stored in databases and in people. So what happens to all the knowledge and expertise we now confront? Outside of it being somewhat accessible on the net, the large amounts of data are forcing us to reimagine data infrastructure.

This is pushing development of large “big data” solutions that will have the ability to process and dashboard results that are more easily “digestable” for larger and larger groups of people across the spectrum.

Weinberger confirms that there is so much data, information & knowledge today for the first time in our collective history that no single person can process it all. And that is not always a good thing. He stated “We see all too clearly how impotent facts are in the face of firmly held beliefs. We have access to more facts than ever before, so we can see more convincingly than ever before that facts are not doing the job we hired them for.”

And at the same time accounting for human nature – access to more data will only reinforce the worse as illustrated by Cass Sunstein: “Studies have shown that when people speak only with those with whom they agree, they not only become more convinced of their own views, they tend to adopt more extreme versions of those views.” And now you know the rest of the story.

Too Big to Know reveals in chapter eight how we are managed today. In the past we learned about Jack Welch of GE. He was the final, top decision maker. But today with wikis, blogs and mobile technology GE’s strategic plans are made from the bottom up: “The CEO of General Electric could be entirely off the grid, but still GE’s engineers, product managers, and marketing folks are out on the Net, exploring and trying out the ideas that affect their branch of the larger decision tree.” This is the ‘wikipedia’ approach. This is also something Weinberger acknowledges in Don Tapscott’s work Wikinomics.

Finally, I could not agree more with Weinberger’s example (Chapter five) regarding a marketplace of echoes. He describes the impact of David Halberstam‘s award winning book The Best and the Brightest. (my review here) Halberstam attempted to explain how the Kennedy White House, full of highly educated, dedicated men (McGeorge Bundy, George Ball, Chester Bowles, Robert McNamara) could have failed so badly in Vietnam. Their efforts are now very distant, recalled most often as an analogy to our country’s worst mistake. But Halberstam’s question remains deeply unsettling: How did the best and the brightest get us into the ‘hell’ of Vietnam? If these men, so well educated and worldly, erred so badly, how can we trust the advice of lesser men?

No better lesson on diversity than our failure in Vietnam. This is a very good book.

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Latest read: The Signal and the Noise

The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t by Nate Silver is another great book that completely absorbed my attention. This book offers in sights to many audiences. From weather forecasts, the Wall Street financial crisis in 2007, playing poker to even understanding and identifying signal relationships regarding the attacks on Pearl Harbor and New York on 9/11.
The Signal and the NoiseThe Signal and the Noise offers readers Silver’s insights on Bayesian thinking. Actually the book applies Bayesian in all the books lessons.

He nudges us to remember this when applying predictions in our own professions. Actually Nate’s study of predictions affects just about everything we do in life.

The strongest lesson for me is about understanding data-driven models can lead to tragic outcomes. He warns us about noisy data and Big Data that can set off false readings with horrific consequences. This alone makes this book a pretty important read.

Silver’s chapter on baseball and references to Michael Lewis‘s bestseller

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Latest read: Reliability Assurance of Big Data in the Cloud: Cost-Effective Replication-Based Storage

While focused on the task of generating data for astrophysics Reliability Assurance of Big Data in the Cloud is a worthy read when focused around designing cloud service contacts.
Reliability Assurance of Big Data in the CloudThe work of authors Yang, Li and Yuan surround capturing big data reliability, and measuring disk storage solutions including from noted cloud vendors.

Their work at Centre for Astrophysics and Supercomputing at Swinburne University of Technology focused on reducing cloud-based storage cost and energy consumption methods.

They also share the impact of multiple replication-based data storage approaches based upon Proactive Replica Checking for Reliability (PRCR). That was very interesting in their research data gathering.

I found Reliability Assurance of Big Data in the Cloud also supports moving data into the cloud across advanced research networks including Internet2.

Processing raw data inside the data center impacts network models (based upon available bandwidth) in their work. Their research gathers and stores 8 minute segments of telescope data that generates 236GB of raw data. By no means in the petabyte stage (yet) but it still sets a solid understanding of contractual demands on big data cloud storage.

My interest peaked around impacts developing knowledgeable contracts for cloud services. Their background regarding data gathering and processing should influence procurement contract language. This is even more applicable when applied to petabyte data sets and the SLAs regarding data reliability requirements. Never leave money on the table when scaling to the petabyte range. Must read for purchasing agents and corporate (and university) CPSMs.