Posts tagged big data
When Big Data Can't Predict

Most people think that in the age of big data, we always have more than enough information to build robust analytics. Unfortunately, this isn’t always the case. In fact, there are situations where even massive amounts of data still don’t enable even basic predictions to be made with confidence. This challenge of big data that can’t be used to predict seems like an impossible paradox at first, but let’s explore why it isn’t.

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Data Preparation: Is the Dream of Reversing the 80/20 Rule Dead?

I recently had someone ask me, “For years we’ve talked about changing analytics from 80% data prep and 20% analytics to 20% data prep and 80% analytics, yet we still seem stuck with 80% data prep. Why is that?” To explain, we need to differentiate between a new data source and/or a new business problem and existing ones we have addressed before.

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The Logical Step Past Analytics Is Cognitive

Many people and companies seem to think of “cognitive computing” as a separate area from analytics. Most large organizations today have significant analytical initiatives underway, but they think of the cognitive space as being an exotic science project. One executive told me, “We have no desire to win Jeopardy,” an allusion of course to the IBM Watson project from 2011. But cognitive computing is not just about Watson, and it’s not an exotic science project.

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Approach Big Data Analytics Like a Lego Kit

A few months back I was having a conversation with a colleague of mine, Brad Elo. We were discussing the importance of operationalizing analytic processes and the need for the use of repeatable and standardized components to enable success. As part of the discussion, Brad brought up a terrific parallel between Legos and analytics that clearly illustrates the importance of approaching the analysis of big data correctly.

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Analytics 4.0: The Scary Ago Of Automated Networks

I have written here here and there about “Analytics 3.0,” an environment in which companies combine big and small data at significantly greater scope and scale of analytics. And even more recently I’ve been working on a book about the third era of automation, in which smart machines take over many types of decision-making from knowledge workers (and, perhaps more importantly, about what the knowledge workers can do about it).

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The Hot New Tech Startup Is Uncle Sam

I’ve been hearing interesting rumors about a hot new tech services startup—on the East Coast for a change. In fact, it’s in Washington, D.C., and it’s financed by really deep pockets—the U.S. government budget. Your national government may have had some problems with IT in the past—a range of disasters come to mind—but now the US seems to have some of the best IT people in the country.

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Making Sense Of The Third-Party Data Economy

When I think of the data economy, I usually focus on either online firms like Google and LinkedIn, or large, established companies like GE and Monsanto that have invested in data and analytics-based products and services for their customers. These companies use their own data to develop their own products and services. But there are certain advantages from having third parties do this sort of work for you. They can provide independence, scale, and processes that are solely focused on helping to monetize your data.

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The Data Product Era Begins In Financial Services

I’ve been feeling that there is a sea change happening in what organizations do with data, and I now have confirmation of it at several financial companies. I heard, for example, that JPMorgan Chase, the nation’s largest bank by assets, established an “Intelligent Solutions” business unit to offer—among other things—products based on data and analytics to its customers.

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Plan For Big Data Like It's 2000

Remember 2000? Everybody was very happy with the fact that Y2K had passed without nary a glitch, and the e-commerce revolution was in full flower. Most of the attention went to e-commerce startups, but IT-oriented people my age and older also recall that many larger firms were attempting to embrace e-commerce. While I was only on the fringes of that movement, in many cases a consultant or academic like me would be brought in to help a company strategize about this important new capability.

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A Pitfall To Avoid When Funding Big Data Analytics

Today, the systems used to facilitate analytics within most organizations are owned by IT, which means that IT owns the budget to purchase and maintain the systems. Business units may help fund the systems via charge back models, but usually it is IT’s budget that gets hit directly with any system expenses. With the evolution of the way analytics are created and used, this can cause a big problem.

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Who Owns Your Data Exhaust

More than twenty years ago, consultants Stan Davis and Bill Davidson, in the book 2020 Vision, argued that a company’s “information exhaust” could be used to “informationalize” a business and turbocharge its performance. Their primary examples of this phenomenon were information companies—Quotron, TV Guide, TRW, and the like. They did argue, however, that any company in any industry had the potential to be informationalized by its data exhaust.

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Dueling Schooling On Big Data And Analytics

There’s been a lot of discussion about the shortage of quantitative analysts and data scientists in this world, and many people wonder where they will all come from. Today I have good news and bad news for you. The good news is that there are a rapidly growing number of educational institutions that are offering courses, concentrations, and degree programs in analytics and big data.

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Is Big Data Good Or Evil?

With all of the lawsuits working through the courts and all of the scary possibilities being discussed in the media, it has led some people to assume that big data is inherently evil. Once you believe that big data is evil, a natural response is to try and shut down the collection and analysis of big data to the maximum extent possible. While big data certainly has risks, it would be a classic case of throwing out the baby with the bathwater if the use of big data is shut down.

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Making Big Data Actionable: How Data Visualization And Other Tools Change The Game

Bill Franks, an IIA faculty member and Chief Analytics Officer for Teradata, was featured in a webinar discussing approaches to making big data more actionable and profitable by utilizing data visualization tools and strategies. The talk highlighted the important opportunities and level of insight that big data and analytics can provide organizations and shared how visualization tools can better support decision making and lead to discovery of new insights.

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Quick! Invest In This CBIMBDMLAAS Company!

We are living in a period where certain buzz words are repeated again and again. While there are always buzz words being repeated at any point in time, it seems that there are more buzz words active today than usual. Companies, from startups to well-known technology behemoths, are falling all over themselves to make sure that everyone knows they are a player in some of the spaces covered by the most popular buzz words.

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Big Data's Big Flip-Flop

It wasn’t too long ago that many people espoused the decline, if not death, of the SQL language and relational database technology in general. As a level set, remember that relational technology stores data into rows and columns and that the way to access relational data is through Structured Query Language (SQL). For a couple of years, there was a full frontal assault on relational approaches from the Hadoop and non-relational crowds.

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