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The Business Case for In-Memory Computing

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This article is the second in an editorial series that will provide direction for enterprise thought leaders on ways of leveraging in-memory computing to analyze data faster, improve the quality of business decisions, and use the insight to increase customer satisfaction and sales performance.

Data Access and Exploratory Data Analysis

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Enterprise data assets are what feed the predictive analytic process, and any tool must facilitate easy integration with all the different types data sources required to answer critical business questions. Robust predictive analytics needs to access analytical and relational databases, OLAP cubes, flat files, and enterprise applications.

Visualization of the Week: US Weather Trends

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This week’s visualization choice was selected because it really hit home for me living in Los Angeles. Just in case you’re not familiar with our plight here in Hollyweird, we have a case of perpetual summer and severe drought conditions which when coupled together have produced a rather harsh climate in which to live.

Big Data and Retail Banking

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This article is the second in an editorial series that has the goal to provide direction for enterprise thought leaders on ways of leveraging big data technologies in support of analytics proficiencies designed to work more independently and effectively in today’s climate of working to increase the value of corporate data assets.

insideBIGDATA Guide to In-Memory Computing

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In this new Guide to In-Memory Computing the goal is to provide direction for enterprise thought leaders on ways of leveraging in-memory computing to analyze data faster, improve the quality of business decisions, and use the insight to increase customer satisfaction and sales performance.

Predictive Analytics Software and R

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There is a vast array of predictive analytics tools, but not all are created equal. Software differs widely in terms of capability and usability — not all solutions can address all types of advanced analytics needs. There are different classes of analytics users — some need to build statistical models, others just need to use them.

insideBIGDATA Guide to Big Data for Finance

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In this new Guide to Big Data for Finance the goal is to provide direction for enterprise thought leaders on ways of leveraging big data technologies in support of analytics proficiencies designed to work more independently and effectively in today’s climate of working to increase the value of corporate data assets.

How Statistical Science Can Advance Big Data Research Projects

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A recently released American Statistical Association (ASA) white paper recommends a multidisciplinary approach comprised of statisticians, mathematicians, data scientists and relevant domain scientists to tackle the challenges of the federal government’s Big Data Research and
Development Initiative and similar private-sector projects.

Classes of Predictive Analytics

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This article is the third in an editorial series that will review how predictive analytics helps your organization predict with confidence what will happen next so that you can make smarter decisions and improve business outcomes..  It is important to adopt a predictive analytics solution that meets the specific needs of different users and skill sets from beginners, […]

Accenture Study Shows Satisfaction with Business Outcomes from Big Data

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Ninety-two percent of executives from companies that are applying big data to their businesses said they are satisfied with the results, according to new research by Accenture (NYSE: ACN).