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Data Mining

Managing Big Data Integration and Security with Hadoop

September 2, 2015 by Jason Parms
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An open-source framework like Hadoop offers endless possibilities for development, and with a strong management group like Apache Systems behind it, one can expect increasing numbers of modules and technologies to integrate with Hadoop to enable your business to achieve its Big Data goals – and maybe even go significantly beyond what you can envision today.[read more]

Automate the Boring But Essential Parts of Your Data Warehouse

August 12, 2015 by Keith Peterson

To deliver on your company’s future demands for data and insights, you will need to maintain your existing data warehouse – and add the great new capabilities available with big data management and in-memory analytics. The real opportunity is in making those technologies work together smoothly with minimum effort and risk.[read more]

Challenges of Working with Big Data: Beyond the 3Vs

July 16, 2015 by Venky Ganti
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Among many challenges in working with big data, the 3V’s (Volume, Velocity, and Variety) have gotten a lot of attention. Googling yields many results worth reading. Almost all of these focus on technological challenges in managing and processing big data. In this post, I would like to highlight a different set issues that make working with big data challenging, even if the underlying infrastructure is admirably able to handle all three V’s.[read more]

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Demystifying Self-Service Data and the Future of Business Intelligence

July 1, 2015 by Marius Moscovici

Users can’t afford to send requests and wait days or weeks for a report that could already be outdated. To get the most out of the data your company collects, relevant data points need to be available to all business users when they need it.[read more]

10 Reasons Why Now Is the Time to Get into Big Data

June 12, 2015 by Rick Delgado

Big data is a key that'll explain specifically what your consumers really think about your product. Big data is even being used in medical research for companies that do personalized medicine or companion diagnostics, and need to analyze large amounts of biological data. You'll be able to use your insight to easily get a better picture of your customers based out of different geographic areas and belonging to different demographic groups.[read more]

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Redefining Loyalty Programs with Big Data and Hadoop

June 11, 2015 by Dave Mendle

A loyalty program should not be about points, rewards, or status. While these perks may attract consumers, they don’t foster loyalty. The focus of these programs should be to collect useful data that can be used to build relationships that benefit both the consumer and the brand.[read more]

More Than Just a Title: How to Identify a Data Scientist

May 27, 2015 by Linda Burtch

Data scientists apply sophisticated quantitative and computer science skills to both structure and analyze massive unstructured data sets or continuously streaming data, as well as derive insights from the data and prescribe action. The depth of their coding skills distinguishes them from other predictive analytics professionals, and allows them to exploit data regardless of its source, size, or format.[read more]

13 Retail Companies Using Data to Revolutionize Online & Offline Shopping Experiences

May 21, 2015 by Trips Reddy

Shopping experience. 

According to a report by McKinsey & Company, despite the e-commerce boom, brick-and-mortar stores will still account for approximately 85% of U.S. retail sales in 2025. While slow to catch up with the digital revolution, retail brands are starting to rethink business models and leverage technology to reinvent the in-store customer experience. Interactive digital displays, touchscreens, digital storefronts, magic mirrors, virtual dressing rooms and in-store kiosks (to order out-of-stock items) are transforming how consumers interact with products in physical stores.[read more]

Austinites Really Love Music & Kevin Durant is Kind of a Big Deal, So Says Data Science

May 8, 2015 by Kevin Safford

Let's talk a little bit about true audience segmentation, beyond naive demographics, splitting people into quartiles and deeper, by far, than simple queries and filters. At Umbel, the data ecosystem populating our Digital Genome allows us to create an emergent, rich understanding of the subtle patterns spread throughout and across any audience. Let's step through what I like to call the "data looking-glass" together.[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]

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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]

House of Cards and the Art of Working Backwards

March 10, 2015 by Sima Thakkar

Working backwards.

So if the consumer (and in this situation my friends) aren’t too concerned with the fact that the show was deliberately made based on viewing habits, but rather that they get to watch one of their favorites actors be directed by someone whose movies they also love, all while engaging in a nail-biting plot, why aren’t more companies working backwards and paying closer attention to what their customers want?[read more]

Business Analytics Error: Learn from Uber’s Mistake During the Sydney Terror Attack

January 24, 2015 by RK Paleru
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Uber.

Recently, as a sad day of terror ended in Sydney, a bad case of Uber’s analytical approach to pricing came to light - an “algorithm based price surge.” Uber’s algorithm driven price surge started overcharging people fleeing the Central Business District (CBD) of Sydney following the terror attack.[read more]

2015: The Year of IoT Pioneers, Analytics and Data Privacy

January 16, 2015 by Puneet Pandit

2015.

This past year welcomed widespread Internet of Things (IoT) adoption and hype, big data implementation, and growing concerns around data privacy and cloud deployment. As 2014 draws to a close, we look ahead with much anticipation for what promises to be a signature year for machine learning, predictive analytics, new IoT pioneers and a full overhaul in the distribution of the IoT.[read more]

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Business (NOT) as Usual: 3 Big Business Intelligence Predictions for 2015

December 30, 2014 by Ray Major

BI.

Each year, I work with dozens of clients in a quest to help them effectively implement their Business Intelligence systems. And each year, based on my experiences, I have put together a top 10 list of trends and predictions for the upcoming year.[read more]