Gartner Blog Network

Svetlana Sicular
Research Director
3 years at Gartner
21 years IT industry

Svetlana Sicular has a uniquely combined experience of Fortune 500 IT and business leadership, product management at world-class software vendors, and Big Four consulting. She primarily handles inquiries in the areas of data management strategy, ...Read Full Bio

Two Main Questions about Big Data

by Svetlana Sicular  |  April 16, 2015

More and more people describe the speed of big data technologies in dog years. In the lightning-fast cadence (“fail fast”), considerations for actual technical professionals who are deploying big data solutions fall into cracks. Vendors look at abstract “customers.” Big data ecosystem talks about “data scientists.” A new term “data engineer” is bubbling to the […]

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Return of the Data Scientist

by Svetlana Sicular  |  March 27, 2015

I am convinced, data science was not born in 2008 — it’s just the term “data scientist” which has been coined that year.  I wrote a blog post Data Scientist – Mystified in 2012. At the time, data science was mystified, glorified and made mathematics sexy. I liked what it did to math, but I […]

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What’s Next for Big Data?

by Svetlana Sicular  |  December 23, 2014

Shortly after joining Gartner, I noticed subtle magic in the air. Not Magic Quadrants – they were too obvious. It was I told you so repeated by many analysts on many occasions. It seemed almost mystical – how could they know? At some point, I caught myself saying I told you so too — it […]

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Big Data Is Entering the Age of Aquarius

by Svetlana Sicular  |  October 23, 2014

Suddenly, I realized: fluids are in, animals are out. The big data ecosystem has given up on its elephants, impalas and pigs in favor of aquatics.  Perhaps, the shift started with “data lakes,” or, perhaps, data lakes just reflected the state of big data (pun intended). Or maybe, Cascading was the one that signified the […]

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Where Art Thou Hadoop?

by Svetlana Sicular  |  October 14, 2014

The Hadoop ecosystem is like a kaleidoscope, where particles keep colliding, tumbling and forming mesmerizing patterns created by the reflections. My research note What Matters When Comparing Hadoop Distributions is finally out. I’ve been writing it for four months. As soon as I felt it was ready, there were some principle points that I had […]

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Gartner’s Big Data Definition Plays Out in Sports

by Svetlana Sicular  |  August 28, 2014

As a Gartner analyst, I am fortunate to frequently meet amazing people. Qaizar Hassonjee from Adidas is not only one of them, but one of the most memorable ones among the amazing people. He is at the heart of miCoach, including miCoach Elite, the system developed in partnerships with the top soccer players, coaches and teams of the […]

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Big Botched Data

by Svetlana Sicular  |  July 29, 2014

Everybody talks about successes in big data. And everybody is curious about failures.  Today, I want to illustrate some typical causes of big data project failures with real-life examples, no company logos to show, sorry.  I’ll give not necessarily “fail fast” scenarios, but also the uneventful and painful “fail slow.” Let’s start with the amazing […]

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5+ Big Data Companies to Watch

by Svetlana Sicular  |  June 17, 2014

People often ask me if there is a magic quadrant for big data. There isn’t. What we have is a Hype Cycle for Big Data with abundance of big data technologies, some of which are just nascent, some are on the plateau of productivity, and some, like Hadoop distributions, are in the unrightfully dreaded and […]

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The Era of Data

by Svetlana Sicular  |  June 3, 2014

In 2009, a CRM icon Tom Siebel was attacked by a charging elephant during an African safari. Ominously, this was exactly the time of changing epochs signified by another elephant, Hadoop. It was not obvious back in 2009 that the era of CRM came to the end and the era of data began. This very year of 2009, […]

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Big Data Quantity vs. Quality

by Svetlana Sicular  |  May 22, 2014

Increasing adoption of big data technologies brings about the big data dilemmas: Quality vs. quantity Truth vs. trust Correction vs. curation Ontology vs. anthology Data profiling, cleansing or matching in Hadoop or elsewhere are all good but they don’t resolve these dilemmas.  My favorite semantic site Twinword pictures what a dilemma is. You get the picture. New technologies promote sloppiness. […]

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