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Big Data

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5 Tips for Streamlining the Supply Chain

April 24, 2015 by Glenn Johnson

Supply chain.

Supply chain efficiency helps an organization reduce costs and speed time-to-market. Fifteen years into the new millennium, it is clear to see that advances in technology, increasing globalization and rising customer expectations have made streamlining the supply chain more important than ever.[read more]

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Learn from Carnegie Mellon’s School of Data Management Hard Knocks

April 23, 2015 by Jeff Brown

Big data error.

The school mistakenly sent acceptance emails to hundreds of hopefuls who applied to Carnegie Mellon’s prestigious Master of Science in Computer Science program. A few hours later, an email with a much different tone arrived in their inboxes. A school statement read: “This error was the result of serious mistakes in our process for generating acceptance letters.”[read more]

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The Data Lake Debate: The Final Word from Negative

April 22, 2015 by Anne Buff

The Data Lake Debate.

Well, it seems you took the gloves off this time, Tamara. I appreciate the valiant effort and your passionate belief in the Hadoop ecosystem. However, given your revisit to the definition of the data lake and clarifications about Hadoop, I find it important to repeat the resolution we are debating: “a data lake is essential for any organization to take full advantage of its data”. We are not debating whether a data ecosystem is essential – just the data lake.[read more]

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Big Data: How Netflix Uses It to Drive Business Success

April 21, 2015 by Bernard Marr

The Big Brand Guru.

Legendary Hollywood screenwriter William Goldman said “Nobody, nobody – not now, not ever – knows the least goddam thing about what is or isn’t going to work at the box office.” He was speaking before the arrival of the internet and Big Data, and since then, the streaming movie and TV service Netflix has based its business model on attempting to prove him wrong.[read more]

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A Better Way to Model Data

April 21, 2015 by Mark Hargraves

Data modeling.

Over 8 years ago, the Spider Schema Data Model was created to provide an easier way to model OLTP data into a supported OLAP data model with the advantages of the OLTP data model. Over the last 8 years this data model has been proven out and is: faster at data processing, uses less storage space, is more flexible, and provides full support for not only OLAP, but OLTP, and Big Data.[read more]

5 Unusual Ways Businesses Are Using Big Data

April 17, 2015 by MIKE20 Governance Association
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Parking lot.

Big data is where it’s at. At least, that’s what we’ve been told. So it should come as no surprise that businesses are busy imagining ways they can take advantage of big data analytics to grow their companies. Many of these uses are fairly well documented, like improving marketing efforts, or gaining a better understanding of their customers, or even figuring out better ways to detect and prevent fraud.[read more]

Big Data Buzz or Big Data Fuzz?

April 16, 2015 by Ben Edge
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The buzzword Big Data has gathered an increasing amount of momentum over the last few years. In fact the number of organizations who have implemented a big data or data discovery solution has increased from 58% to 73% over the last two years. More and more organizations have innovated to include a big data offering in their portfolio in an attempt to grab a slice of the proverbial pie.[read more]

Exploratory Analysis with Excel

April 16, 2015 by Alex Bankoff

Analysis with Excel.

When it comes to data science, we often find that people first start out learning using Microsoft Excel. Inevitably, as students progress, Excel starts to appear quaint as they transition to more powerful systems like R or Python. After a while, however, a lot of people end up coming back and realizing that maybe Excel isn't so bad after all.[read more]

Digital Reasoning Goes Cognitive: CEO Tim Estes on Text, Knowledge, and Technology

April 13, 2015 by Seth Grimes

Tim Estes.

Digital Reasoning CEO Tim Estes sees cognitive computing -- data, applications, analytics, and machine learning algorithms -- as the providing the brains to drive global enterprises. Cognitive is a next computing paradigm, responding to demand for always-on, hyper-aware data technologies that scale from device form to the enterprise.[read more]

5 Steps to Datafy Your Business and Be Successful

April 13, 2015 by Mark van Rijmenam

Datafy!

A great example of datafication is the quantified-self and the wearables trend, of which the most-talked about is probably the Apple Watch that is about to become available. The Apple Watch will enable users to generate massive amounts of data about their personal lives, analyse what’s happening, combine it with other data sources and obtain new insights about their personal life.[read more]

Intuitive Reasoning, Effective Analytics & Success: Lessons from Dr. Jonas Salk

April 11, 2015 by Mike Urbonas

Salk.

Just days away, April 14, 2015 will mark the 60th anniversary of the Salk Polio Vaccine. On that day in 1955, it was publicly announced that human trials confirmed Dr. Jonas Salk’s vaccine provided effective protection from the polio virus. By 1957, new polio cases fell by 90% from epidemic levels just five years earlier. A fascinating interview with Dr. Salk on the Academy of Achievement website sheds light on his key personal attributes and values - vital for success in any line of work. And the best analytic tools will play a leading role in fostering that success.[read more]

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The Data Lake Debate: Pro Delivers First Rebuttal

April 10, 2015 by Tamara Dull

Data Lake Debate.

In my opening argument, I defined the data lake as a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. I also mentioned that a data lake can take on different shapes and sizes, and provided these examples: A single data lake; or a data lake with multiple data ponds—similar in concept to a data warehouse/data mart model; or multiple, decentralized data lakes; or a virtual data lake to reduce data movement.[read more]

Not All Hadoop Users Drop ACID

April 8, 2015 by Paige Roberts

ACID.

Hadoop users have all had to give up ACID and settle for the new standard, BASE, as a general rule, but like so many things in the data wrangling industry, that’s changing fast. This may come as a shock to a lot of current Hadoop users and database users considering making the switch to Hadoop, but using Hadoop doesn’t mean you have to give up your ACID habit.[read more]

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Beyond Automation: Streamlining Business Processes with Custom Software

April 8, 2015 by Kendall Wyman

Beyond automation.

Custom software can be a game-changing solution for many organizations, but you can’t just throw applications at a business process problem and expect it to go away. Learn how custom business applications can improve your organization's operations by finding and addressing gaps in your current processes.[read more]

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How Big Data and Analytics Are Changing People Management Forever

April 7, 2015 by Bernard Marr

The Big Data Guru column.

Will employees resent analysis of their day-to-day activities? Some certainly will. But as I said before it will depend entirely on how it is implemented. In short, there are far more useful, and less provocative, uses for employee data collection and analysis, than enforcing discipline over who takes the most bathroom breaks.[read more]