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Data Mining

From Master Data to Master Graph

October 6, 2015 by Peter Perera

Today’s CRM and Master Data Management (MDM) technologies don’t enable complete customer knowledge. In fact, they unwittingly turn customer focus into customer tunnel vision. We need an epistemic graph database - a context-aware master graph - to make possible richer, fuller customer stories and expanded 360-degree views for total awareness.[read more]

An Introduction to ElasticSearch

September 28, 2015 by Zygimantas Jacikevicius

Search engines are now an integral part of people’s everyday lives. We are used to having access to information at the click of a button. However we rarely think how much work goes into this ability to search for information. Search engine software has become extremely advanced in recent years, now using complex algorithms to provide the most relevant information with predictive search and search suggestion capabilities.[read more]


Apache Spark Use Cases

September 28, 2015 by Jim Scott

Sure, Apache Spark looks cool, but does it live up to the hype? Is there anything you can actually do with it? Actually, there are some pretty cool use cases going on right now.[read more]


Game-Changing Real-time Uses for Apache Spark

September 23, 2015 by Jim Scott

Apache Spark, hosted on Hadoop, is great for processing large amounts of data quickly, but wouldn’t it be even better if you could process data in real time? If your business depends on making decisions quickly, you should definitely consider the MapR distribution, which includes the complete Spark stack including Spark Streaming.[read more]

Forecast Product Demand with Confidence

September 9, 2015 by Keith Peterson

Is your company using demand forecasting in your planning process? And are you happy with those results? Based on our interviews with hundreds of companies worldwide, we have found that among midsize manufacturers and distributors, many still use error prone spreadsheets or forecast based on historical sales data.[read more]

Managing Big Data Integration and Security with Hadoop

September 2, 2015 by Jason Parms

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

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]


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]


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]


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]