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Data Management
Leo Sadovy 0
Big Silos: The dark side of Big Data

The bigness of your data is likely not its most important characteristic. In fact, it probably doesn’t even rank among the Top 3 most important data issues you have to deal with.  Data quality, the integration of data silos, and handling and extracting value from unstructured data are still the most

Fiona McNeill 0
Seth Grimes with More on Text Analytics

Perhaps it’s the same for you - it’s getting harder to get to all the conferences I’d like to attend. One of the benefits of getting out there is a chance to learn about different perspectives in an industry. When someone has a broad perspective, particularly if they’ve been in

SAS Colombia 0
Los cuatro mitos de un CIO moderno

Un Chief Information Officer tiene ciertas características que lo hacen desempeñarse dentro de un rol único. Sin embargo, gracias a los retos que conlleva ahora la implementación de proyectos de analítica avanzada en las organizaciones, han surgido algunos mitos que rodean el rol de TI. Keith Collins, Vicepresidente Senior de

Analytics | Data Management
Suzanne Clayton 0
Be proactive. Be a trailblazer with data.

For many industries, big data analytics have opened numerous doors for more employees to be groundbreaking and to challenge the corporate status quo. Prior to big data technologies, risk taking behaviors were primarily reserved for provocative souls who stretched organizational boundaries to disrupt industries, such as airline revenue management. There were winners and losers

Klaus Fabits 0
Big Data? Ich kann es schon nicht mehr hören!

 „Wieder ein Berater, wieder ein Systemintegrator, wieder ein Outsourcer und wieder ein Software- oder Hardware-Hersteller, der mir erzählt, dass Big Data der Trend der Zukunft ist. Jeder erzählt mir, wie wichtig „Big Data“ für mein Unternehmen sind. Und wenn ich hier nicht zustimme, bin ich gleich ein Innovationsverweigerer. Die Nachfrage

Analytics
Jon Lemon 0
Four-step approach to government fraud detection

Every day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits,  health care, food stamps, tax collection,

Rick Wicklin 0
Resampling and permutation tests in SAS

My colleagues at the SAS & R blog recently posted an example of how to program a permutation test in SAS and R. Their SAS implementation used Base SAS and was "relatively cumbersome" (their words) when compared with the R code. In today's post I implement the permutation test in

Analytics | Fraud & Security Intelligence
John Stultz 0
Is predictive analytics misguiding your fraud detection efforts?

When it comes to fraud detection and risk mitigation, predictive modeling has earned a reputation as the “heavy hitter” in the realm of data analytics.  As our celebration of International Fraud Awareness Week continues, I would challenge our readers to ask themselves this question, “Is the reliance upon predictive analytics

Data for Good | Data Visualization
Tom Morse 0
Patient-centered health care in the new health economy

Today’s healthcare system is under tremendous pressure to reduce overall costs without losing track of the patient. Legislative changes and challenging economic realities make it increasingly difficult to deliver both improved outcomes and cost savings for the most complex patients. The Physicians Pharmacy Alliance (PPA) recognizes the changing healthcare landscape

Rick Wicklin 0
What is the coefficient of variation?

I sometimes wonder whether some functions and options in SAS software ever get used. Last week I was reviewing new features that were added to SAS/IML 13.1. One of the new functions is the CV function, which computes the sample coefficient of variation for data. Maybe it is just me,

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