Is Your Company a Data Management Leader or Laggard?
November 4, 2016
The article titled Companies are Falling Short in Data Management on IT ProPortal describes the obstacles facing many businesses when it comes to data management optimization. Why does this matter? The article states that big data analytics and the internet of things will combine to form an over $300 billion industry by 2020. Companies that fail to build up their capabilities will lose out—big. The article explains,
More than two thirds of data management leaders believe they have an effective data management strategy. They also believe they are approaching data cleansing and analytics the right way…The [SAS] report also says that approximately 10 per cent of companies it calls ‘laggards’, believe the same thing. The problem is – there are as many ‘laggards’, as there are leaders in the majority of industries, which leads SAS to a conclusion that ‘many companies are falling short in data management’.
In order to avoid this trend, company leaders must identify the obstacles impeding their path. A better focus on staff training and development is only possible after recognizing that a lack of internal skills is one of the most common issues. Additionally, companies must clearly define their data strategy and disseminate the vision among all levels of personnel.
Chelsea Kerwin, November 4, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Bye-Bye Enterprise Storage
October 19, 2015
Storage is a main component of the enterprise system. Silos store data and eventually the entire structure transforms into a legacy system, but BusinessWire says in “MapR Extends Support For SAS To Deliver Big Data Storage Independence” it is time to say good-bye to old enterprise storage. MapR is trying to make enterprise storage obsolete with its new extended service support for SAS, a provider of business software and services. The new partnership between allows advanced analytics with easy data preparation and integration in legacy systems, improved security, data compliance, and assurance of service level agreements.
The entire goal is to allow SAS and MapR clients to have better flexibility for advanced analytics within Hadoop as well as to help customers harvest the most usefulness our of their data.
Here is a rundown of the partnership between SAS and MapR:
“The collaboration makes available the full scope of technologies in the SAS portfolio, including SAS® LASR™ Analytic Server, SAS Visual Analytics, SAS High-Performance Analytics, and SAS Data Loader for Hadoop. Complete MapR integration delivers security and full POSIX compliance for use in “share everything architectures,” as well as enables SAS Visual Analytics to easily and securely access all data. With SAS Data Loader for Hadoop, users can prepare, cleanse and integrate data inside MapR for improved performance and then load that data in-memory into SAS LASR for visualization or analysis, all without writing code.”
Breaking away from legacy systems with old onsite storage is one of the new trends for enterprise systems. Legacy systems are clunky, don’t necessary comply with new technology, and have slow information retrieval. A new enterprise system using SAS and MapR’s software will last for some time, until the new trend buzzes through town.
Whitney Grace, October 19, 2015
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 Promises Unstructured Insight
July 10, 2015
Big data is tools help organizations analyze more than their old, legacy data. While legacy data does help an organization study how their process have changed, the data is old and does not reflect the immediate, real time trends. SAS offers a product that bridges old data with the new as well as unstructured and structured data.
The SAS Text Miner is built from Teragram technology. It features document theme discovery, a function the finds relations between document collections; automatic Boolean rule generation; high performance text mining that quickly evaluates large document collection; term profiling and trending, evaluates term relevance in a collection and how they are used; multiple language support; visual interrogation of results; easily import text; flexible entity options; and a user friendly interface.
The SAS Text Miner is specifically programmed to discover data relationships data, automate activities, and determine keywords and phrases. The software uses predictive models to analysis data and discover new insights:
“Predictive models use situational knowledge to describe future scenarios. Yet important circumstances and events described in comment fields, notes, reports, inquiries, web commentaries, etc., aren’t captured in structured fields that can be analyzed easily. Now you can add insights gleaned from text-based sources to your predictive models for more powerful predictions.”
Text mining software reveals insights between old and new data, making it one of the basic components of big data.
Whitney Grace, July 10, 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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