Category Archives: Analytics

About Analytics

Gartner’s Magic Quadrant for BI and Analytics

Gartner

Right from the name change of Gartner’s usual Magic Quadrant for BI to include analytics system, this year’s report has a lot to cheer about. There is more clear definitions on what makes up Business Intelligence and Analytics systems. Its broken down into 3 categories: Integration, Infomartion Delivery and Analysis.

The image is self descriptive and more info on each vendor is available as apart of this 35 page report.

Cloud computing major storing content by 2016: Gartner

“Gartner predicts that worldwide consumer digital storage needs will grow from 329 exabytes in 2011 to 4.1 zettabytes in 2016,” the study said.

This includes digital content stored in PCs, smartphones, tablets, hard-disk drives (HDDs), network attached storage (NAS) and cloud repositories.

“Gartner predicts that worldwide consumer digital storage needs will grow from 329 exabytes in 2011 to 4.1 zettabytes in 2016,” the study said.

This includes digital content stored in PCs, smartphones, tablets, hard-disk drives (HDDs), network attached storage (NAS) and cloud repositories.

via Business Line : Industry & Economy / Info-tech : Cloud computing major storing content by 2016: Gartner.

101 – Data Mining and Predictive Analytics

In today’s world mining of text, Web and media (unstructured data) plus structured data mining, the term information mining is a more appropriate label. Mining a combination of these, companies are able to make the best use of structured data, unstructured text and social media. Static and stagnant predictive models of the past don’t work well in the world we live in today. Predictive analytics should be agile to adapt and monetize on quickly changing customer behaviors in our world, which are often identified online and through social networks.

Better integration of data mining software with the source data at one end and with the information consumption software at the other end has led to improvement in the integration of predictive analytics with day-to-day business. Even though there haven’t been significant advancements in predictive algorithms, the ability to apply large data sets to models and the ability to enable better interaction with business has led to improvements in the overall outcome of the exercise.

There is a great introduction to the world of data mining and predictive analytics here.

The Jargon of the Novel, Computed

Scholars in the growing field of digital humanities can tackle this question by analyzing enormous numbers of texts at once. When books and other written documents are gathered into an electronic corpus, one “subcorpus” can be compared with another: all the digitized fiction, for instance, can be stacked up against other genres of writing, like news reports, academic papers or blog posts.

One such research enterprise is the Corpus of Contemporary American English, or COCA, which brings together 425 million words of text from the past two decades, with equally large samples drawn from fiction, popular magazines, newspapers, academic texts and transcripts of spoken English. The fiction samples cover short stories and plays in literary magazines, along with the first chapters of hundreds of novels from major publishers. The compiler of COCA, Mark Davies at Brigham Young University, has designed a freely available online interface that can respond to queries about how contemporary language is used. Even grammatical questions are fair game, since every word in the corpus has been tagged with a part of speech.

More…

In Interview – Consider CloudHosting Your Business Intelligence

// Jaspersoft’s experience with more than 100 successful cloud BI deployments has made us realize that a partnership, best-of-breed approach to cloud BI is the best way to go. BI as a service through on-demand SaaS (News – Alert) deployments are generally singular offerings that are overstretched, offer limited flexibility, and generally need to be built from the ground-up, resulting in costly down-time and high implementation costs. One of the best practices that we’ve established from our multiple launches is that customers need to have a cloud hosting-enhanced BI solution with a lean framework. Jaspersoft’s lean architecture based on web-based open standards coupled with experts in cloud management and BI consulting results in a proven solution than can meet a myriad of business needs. ..

More from an interview with Karl Van den Bergh, vice president of product strategy at Jaspersoft.

Portrait Software Utilizes Analytics to Provide PA/DM

From Press Release -

Forrester evaluated the top nine predictive analytics and data mining (PA/DM) solution vendors across 53 criteria, segmenting them into the three categories including current offering, product strategy, and market presence. As a leader offering “mature, high-performance, scalable, flexible, and robust PA/DM solutions,” Portrait received the 3rd highest score for Product Strategy & the 6th highest score for Current Offering.”

Among the vendor products the Forrester(TM) Wave evaluated were Portrait Customer Analytics, Portrait Uplift Optimizer, and Portrait Self-service Analytics. According to the Forrester(TM) Wave, “Portrait provides a user-friendly, feature-rich PA/DM solution portfolio in support of real-time scoring, interaction optimization, uplift optimization, and campaign management for customer analytics.”

“Powerful customer analytics have always been the core driver of Portrait’s innovative marketing solutions, but analytics itself only takes you so far,” said Luke McKeever, CEO, Portrait Software. “Portrait’s ability to not only incorporate analytics but to action the insights they deliver enables us to provide our customers with highly intelligent solutions that help them operate as a customer-centric organization, differentiating them from their competitors while simultaneously improving their marketing ROI.”

Microsoft Unveils Apps for Crime-Fighting Data Mining

Once again, software is fighting crime. Microsoft unveiled a suite of tools and initiatives for law-enforcement groups “specifically designed to improve public security and safety,” the company said.
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It’s also the latest example of law enforcement officials arming themselves with better technology to help fight crime. The FBI, for instance, said that new database and data-sharing efforts have resulted in solving a number of difficult highway serial killings.

Gathering that data is key. That’s why Microsoft this week said it is giving a free tool to INTERPOL called the Computer Online Forensic Evidence Extractor (COFEE), an application that “uses common digital forensics tool to help officers at the scene of the crime.”

The company is working on a mobile version for future release, said Richard Domingues Boscovich, senior attorney for Microsoft’s Internet security program, told InternetNews.com in an e-mail.

A larger tool set for large-scale crimes is Microsoft Intelligence Framework, which is aimed at helping intelligence and law enforcement agencies coordinate information to detect and prevent terrorism, and to solve organized and major crime cases. The framework offers tools for storing and analyzing evidence and information across a variety of sources

From EarthWeb article.

Next Generation Healthcare Analytics

Over at The Health Care Blog, Deb Bradley, Vice President, Client Solutions at Verisk Health in Waltham, Massachusetts writes about some examples of the next generation healthcare analytics. Most of us who do analytics engineering as apart of our day jobs would agree, healthcare is on area where analytics should grow vastly. There is a lot of data that can be intelligently massaged to answer some of the most challeging health related questions.

Medical claims, pharmacy claims, lab values, HRAs, genetic markers, biometrics – the abundance of data is having an immediate impact on how analytics shape healthcare. Next generation analytics are bringing attention to health and wellness rather than disease-specific guidelines, and generating novel approaches to value-based medicine and care management.

Traditionally, analytics, such as predictive modeling, have been used to identify individuals for chronic care management and to set rates. New predictive models, however, include financial and clinical algorithms, which allow healthcare organizations to implement advanced ways to identify, manage and measure risk across and within a population.

SPSS Rebrands Its Analytical Offerings

The new version of the SPSS modeling product — the erstwhile Clementine — is now known as PASW Modeler 13; its text analysis product (formerly Text Mining for Clementine) is now PASW Text Analytics 13. SPSS says that, over the course of the year, the rest of the SPSS product line will update under the PASW umbrella — including Statistics and Data Collection.

David Vergara, director of product marketing for SPSS, explains that the change was intended to help customers and prospects understand what the products are doing and how each offering pieces together within the broader portfolio.

Aside from the name change, the new versions of SPSS products focus on usability — and not just for data experts. Wettemann says that SPSS has “recognized that moving beyond the data analyst audience is where you get the real power.” PASW Modeler 13 features a drag-and-drop interface, and functionality that will appeal to business users. Two integral updates include a “comments” tool, in which users can flag notes within the software, and automated data preparation. Data automation mitigates human error and avoids common issues in data quality.

From Destination CRM.

Data Mining Moves to HR

For most of its eight-year history, Cataphora has focused on digital sleuthing. The company hunts for statistical signs of fraud. But in the past few years, Cataphora has been dispatching its data miners into a new market: statistical studies of employee performance.

The trend, though early, is unmistakable, and it extends far beyond Redwood City. Number crunching, a staple for decades in the quantifiable domains of engineering and finance, has spread in recent years into marketing and sales. Companies can now model and optimize operations, and can calculate the return on investment on everything from corporate jets to Super Bowl ads. These successes have led to the next math project: the worker. “You have to bring the same rigor you bring to operations and finance to the analysis of people,” says Rupert Bader, director of workforce planning at Microsoft (MSFT).

Such a mission might have been laughable a decade ago. But as the role of computers in the workplace expands, employees leave digital trails detailing their behavior, their schedule, their interests, and expertise. For executives to calculate the return on investment of each worker, their human resources departments are starting to open their doors to the quants.

From Business Week, an insightful article on how value of each employee is determined by HR using Data Mining/Analytics.