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2017 State of Analytics Adoption Report

The annual State of Analytics Adoption Report by Logi Analytics provides insights for executives, product managers, and technology leaders on how broadly and deeply users are adopting business intelligence and analytics tools. Survey respondents included members of IT teams who provide analytics tools to end users, as well as the end users of BI and […]

Advanced Analytics, The Modern Marketer’s Best Friend

Download our free white paper to get a global understanding of how analytics are shaking up marketing and what you can do about that!

Architecting for Access: Simplifying Analytics on Big Data Infrastructure

Whether you’re upgrading your current solution or rolling out a brand new platform, planning and executing an analytics workload today requires answering many tough questions.
This eBook from O’Reilly shares:
• How to choose between a data lake or analysis on the fly
• Tips on finding front-end tools that delight users
• Evaluations of hundreds of permutations of technology stacks
• Advice on how to make data your endgame, not opinion

Three Reasons In-Cluster Analytics is a BIG DEAL

Recent technology advances within the Apache Hadoop ecosystem have provided a big boost to Hadoop’s viability as an analytics environment.
You have a lot of data in Hadoop and you’re looking to analyze it. You don’t have to continue bumping up against the limits of the database you’re moving the data into—or how much of it you can afford to use. This whitepaper addresses how you can leverage the power of the cluster you already have in place, expanding and accelerating what you can do while saving you time and money. This is a big deal, it meets a huge demand, it shows how rapidly the technologies have evolved and it delivers on one of the most significant unmet promises of big data analytics.

Embedded Analytics: Beyond Build versus Buy

Embedded Analytics has officially arrived: more and more businesses are looking for a way to provide embedded analytics solutions for their customers. If you are considering doing this you have several things to consider. Do you build it yourself? Do you purchase a data platform that will do it for you?

In this paper, we offer you several ideas as you consider your project.
– Time to market
– Return on investment
– Organizational fit
– Customization and adaptability

Predictive maintenance: How data can increase production

Predictive maintenance involves gathering targeted data for analysis, the results of which will help anticipate potential failures before they occur. Companies opt for this type of maintenance to avoid predictable incidents and repair equipment, assembly lines, or machinery with minimum impact on their operations. “Having to repair a faulty product is disastrous for a manufacturer’s brand image. But shutting down […]

InsideBIGDATA Guide to Healthcare & Life Sciences

This technology guide provides an overview of the utilization of big data technologies as an emerging discipline in healthcare and life sciences. It explores the characteristics of this business strategy and the benefits of leveraging big data technologies within these sectors. It also touches on the challenges and future directions of big data and analytics in the healthcare and life sciences industries. To learn more download this insideBIGDATA guide.

2016 Data Science Salary Survey

In this fourth edition of the O’Reilly Data Science Salary Survey, the input was analyzed from 983 respondents working in the data space, across a variety of industries— representing 45 countries and 45 US states.

insideHPC Guide to Successful Technical Computing

This paper offers those considering HPC, both users and managers, guidance when considering the best way to deploy an HPC solution. Three important questions are suggested that help determine the most appropriate HPC design (scale-up or scale out) that meets your goal and accelerates your discoveries.

Big Data Analytics Meets Big Data

Wal-Mart handles more than a million customer transactions each hour and imports those into databases estimated to contain more than 2.5 petabytes of data.
Radio frequency identification (RFID) systems used by retailers and others can generate 100 to 1,000 times the data of conventional bar code systems.
Facebook handles more than 250 million photo uploads and the interactions of 800 million active users with more than 900 million objects
(pages, groups, etc.) – each day.
More than 5 billion people are calling, texting, tweeting and browsing on mobile phones worldwide.
Organizations are inundated with data – terabytes and petabytes of it . To put it in context, 1 terabyte contains 2,000 hours of CD-quality music and 10 terabytes could store the entire US Library of Congress print collection . Exabytes, zettabytes and yottabytes definitely are on the horizon .
Data is pouring in from every conceivable direction: from operational and transactional systems, from scanning and facilities management systems, from inbound and outbound customer contact points, from mobile media and the Web .
According to IDC, “In 2011, the amount of information created and replicated will surpass 1 .8 zettabytes (1 .8 trillion gigabytes), growing by a factor of nine in just five years . That’s nearly as many bits of information in the digital universe as stars in the physical universe .” (Source: IDC Digital Universe Study, sponsored by EMC, June 2011 .)
The explosion of data isn’t new . It continues a trend that started in the 1970s . What has changed is the velocity of growth, the diversity of the data and the imperative to make better use of information to transform the business .
The hopeful vision of big data is that organizations will be able to harvest and harness every byte of relevant data and use it to make the best decisions . Big data technologies not only support the ability to collect large amounts, but more importantly, the ability to understand and take advantage of its full value .