Next-Generation Business Intelligence Already Used by Risk Analysis Teams
June 1, 2016
Ideas about business intelligence have certainly evolved with emerging technologies. Addressing this, an article, Why machine learning is the new BI from CIO, speaks to this transformation of the concept. The author describes how reactive analytics based on historical data do not optimally assist business decisions. Questions about customer satisfaction are best oriented toward proactive future-proofing, according to the article. The author writes,
“Advanced, predictive analytics are about calculating trends and future possibilities, predicting potential outcomes and making recommendations. That goes beyond the queries and reports in familiar BI tools like SQL Server Reporting Services, Business Objects and Tableau, to more sophisticated methods like statistics, descriptive and predictive data mining, machine learning, simulation and optimization that look for trends and patterns in the data, which is often a mix of structured and unstructured. They’re the kind of tools that are currently used by marketing or risk analysis teams for understanding churn, customer lifetimes, cross-selling opportunities, likelihood of buying, credit scoring and fraud detection.”
Does this mean that traditional business intelligence after much hype and millions in funding is a flop? Or will predictive analytics be a case of polishing up existing technology and presenting it in new packaging? After time — and for some after much money has been spent — we should have a better idea of the true value.
Megan Feil, June 1, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
The Many Applications of Predictive Analytics
September 29, 2015
The article on Computer World titled Technology that Predicts Your Next Security Fail confers the current explosion in predictive analytics, the application of past occurrences to predict future occurrences. The article cites the example of the Kentucky Department of Revenue (DOR), which used predictive analytics to catch fraud. By providing SAS with six years of data the DOR received a batch of new insights into fraud indicators such as similar filings from the same IP address. The article imparts words of wisdom from SANS Institute instructor Phil Hagen,
“Even the most sophisticated predictive analytics software requires human talent, though. For instance, once the Kentucky DOR tools (either the existing checklist or the SAS tool) suspect fraud, the tax return is forwarded to a human examiner for review. “Predictive analytics is only as good as the forethought you put into it and the questions you ask of it,” Hagen warns…. Also It’s imperative that data scientists, not security teams, drive the predictive analytics project.”
In addition to helping the IRS avoid major fails like the 2013 fraudulent refunds totaling $5.8 billion, predictive analytics has other applications. Perhaps most interesting is its use protecting human assets in regions where kidnappings are common by detecting unrest and alerting organizations to lock up their doors. But it is hard to see limitations for technology that so accurately reads the future.
Chelsea Kerwin, September 29, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
SAS Text Miner Provides Valuable Predictive Analytics
March 25, 2015
If you are searching for predictive analytics software that provides in-depth text analysis with advanced linguistic capabilities, you may want to check out “SAS Text Miner.” Predictive Analytics Today runs down the features and what SAS Text Miner and details how it works.
It is a user-friendly software with data visualization, flexible entity options, document theme discovery, and more.
“The text analytics software provides supervised, unsupervised, and semi-supervised methods to discover previously unknown patterns in document collections. It structures data in a numeric representation so that it can be included in advanced analytics, such as predictive analysis, data mining, and forecasting. This version also includes insightful reports describing the results from the rule generator node, providing clarity to model training and validation results.”
SAS Text Miner includes other features that draw on automatic Boolean rule generation to categorize documents and other rules can be exported into Boolean rules. Data sets can be made from a directory on crawled from the Web. The visual analysis feature highlights the relationships between discovered patterns and displays them using a concept link diagram. SAS Text Miner has received high praise as a predictive analytics software and it might be the solution your company is looking for.
Whitney Grace, March 25, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
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