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Data = Opportunity: But Are You Monetizing Information?

May 28, 2015 by RK Paleru
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In 2001, Gartner analyst Doug Laney, coined the term “big-data”, to articulate the impending explosion in Volume, Variety, and Velocity (“the 3 Vs”) of data in society. Much has been talked since about big-data. Today the term big-data is primarily used to describe “how” technology can handle these large and disparate sets of data, not “what” can be done with big-data.[read more]

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Will You Always Save Money with Hadoop?

May 27, 2015 by Tamara Dull

The Big Data MOPS Series.

If you answered “yes” to the question posed in the title, you’re right. Because if you’re talking about the open source Apache Hadoop project (and any related open source project) , you can download the software for free, take advantage of the free licensing, and run it on low-cost commodity hardware.[read more]

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A Big Data Cheat Sheet: What Executives Want to Know

May 21, 2015 by Tamara Dull
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The Big Data MOPS Series. 

What can Hadoop do that my data warehouse can’t? The short answer is: (1) Store any and all kinds of data more cheaply and (2) process all this data more quickly (and cheaply). The longer answer is: They say that 20% of the data we deal with today is structured data. I also call this traditional, relational data. The other 80% is semi-structured or unstructured data, and this is what I call “big” data.[read more]

All Is Not Lost: Finding Value In Marketing Attribution Data

May 15, 2015 by Bill Franks

In my last blog, I laid out some facts that call into question the extensive effort many organizations put into attributing individual customer sales to individual marketing touch points via common attribution methods. To summarize, Suresh Pillai, head of Customer Analytics & Insights for Europe at eBay, showed that all reasonable attribution algorithms led to effectively the same aggregate credit to each marketing lever and also the same credit as a random method.[read more]

How Nonprofits Like the YMCA Use Data to Maximize Community Impact & ROI

May 9, 2015 by Trips Reddy

Maximize impact.

The biggest truth about data is that it only has real value if it changes the way organizations act and think. In today’s data-driven world, having the right data can help nonprofit organizations become more transparent and accountable, helping them demonstrate to donors and members that their programs and initiatives are achieving objectives. Here’s how nonprofits like the YMCA are already using data to look for trends and insights, and even identify and find solutions to problems that don't exist yet, but might be just around the corner.[read more]

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Why Returning $1 Trillion to Shareholders is a Bad Idea

May 4, 2015 by Paul Barsch

Risky Business column.

With creaking IT infrastructures and under-investment in other areas such as plants, equipment, employee training and more, excessive share buybacks aren't just a flawed strategy; they are a dangerous one for the future health of companies across the globe.[read more]

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The Data Lake Debate: Conclusion (With Apologies to the Rolling Stones)

April 30, 2015 by Jill Dyché

The Data Lake Debate.

In an homage to the Rolling Stones, I blithely suggested that if you try sometimes, you get what you need—be it more funding, access to third-party data, a more effective executive sponsor, or a Hadoop distribution provider. It’s not an easy decision, but I’d call the data lake debate a draw. After all, when it comes to the verdict on whether or not a data lake is a worthwhile investment, the success stories will start to emerge. In the meantime, I’m happy to watch those stories unfold. Time, after all, is on my side. Yes it is.[read more]

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The Data Lake Debate: Pro Delivers Final Rebuttal and Summary

April 27, 2015 by Tamara Dull

Okay, this is where the rubber meets the road. I have three minutes (or ~450 words) to respond to Anne’s final statement and summarize why I still believe a data lake is essential for any organization to take full advantage of its data. Let’s get started![read more]

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The Data Lake Debate: The Final Word from Negative

April 22, 2015 by Anne Buff
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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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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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The Data Lake Debate: Pro Cross-Examines Con

April 6, 2015 by Tamara Dull

The Data Lake Debate.

As to be expected, Anne, your arguments against building a data lake are both persuasive and passionate. You’ve made some great points, my friend, but you’re making this way too easy for me. Before I jump into my rebuttal [my next post], I’d like to clarify a few things that you brought up. I’ve boiled it down to three questions. What say you?[read more]

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The Data Lake Debate: Negative Puts a Stake in the Ground

April 1, 2015 by Anne Buff
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The Data Lake Debate.

While the idea of a data lake sounds like fun, don’t go jumping in just yet. There are critical factors to consider before taking the plunge and saying that A data lake is essential for any organization to take full advantage of its data. Not only is a data lake not essential for any organization, a data lake may in fact be detrimental for those who do so prematurely.[read more]

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Data Lakes and Network Optimization: What’s Next for Telecommunications and Big Data

March 31, 2015 by Sameer Nori

Telecommunication. 

Relational data warehouses served communications service providers well in the past, but it’s time to start thinking beyond columns and rows. Unstructured data will be the fuel that powers risk management and decision-making in the near future. And to use all sorts of data to its fullest potential, we need new ways of storing, accessing and analyzing that data.[read more]