SAS® Advanced Analytics Scores Double Win in Analyst Report - MarketWatch Nice article on SAS in Business Wire: CARY, N.C., Jun 25, 2014 (BUSINESS WIRE) -- SAS was a double victor, earning exceptional scores in the recent Advanced Analytics: The Hurwitz Victory Index Report . The research firm crowned SAS a victor in both areas of its new advanced analytics study: customer experience strength and go to market strength. Customers gave SAS top marks for a range of statistical functionality, also scoring SAS “highest” in quality of support, technology, tools, performance and scalability. The report said SAS differentiates itself from other advanced analytics vendors in several other ways: Multiple delivery options, including on-site and private or public cloud. A “factory” approach to the analytics life cycle, enabling customers to manage data, explore relationships, and build, deploy and retrain models without data movement. Ability to scale analytics with massively parallelized, distributed architectures for complex problems. Distributed in-memory processing for speedy, stable, concurrent data discovery and advanced analytics. “SAS owes its decades-long leadership in advanced analytics to its vision and determination to deliver what customers need to meet increasingly complex business challenges,” said Judith Hurwitz, President and Chief Executive Officer of Hurwitz & Associates. “SAS has invested heavily to enable analysis of more data with Hadoop and in-memory analytics. And by delivering easy-to-use software, SAS hands the considerable power of its analytics to business users and others less technical than data scientists.” The Hurwitz research evaluated 10 advanced analytics vendors. Another three declined to participate. The report noted the enthusiasm of SAS customers surveyed. SAS customers gave high marks to the quality of customer support and the overall performance and scalability of the product. According to Hurwitz: “SAS customers interviewed for the Victory Index rate customer satisfaction very high and cited a strong user community. Customers agreed that SAS had strong data preparation abilities, including data cleansing and identifying outliers. In addition, SAS’ depth and breadth of statistical functionality stood out. SAS users felt that the package provided them with nearly every operation a data scientist could need." Key SAS products and categories included in the Hurwitz report included SAS® Visual Analytics , SAS Analytics , SAS High-Performance Analytics , SAS In-Memory Statistics for Hadoop , SAS Scoring Accelerator and SAS Text Analytics . To learn how to find new business opportunities, drive profitability and manage risk, please read Manage the Analytical Life Cycle for Continuous Innovation: From Data to Decision. ... View more

Network World just published an article 10 Big Data Startups to Watch. "The startups... were chosen based on a mix of third-party validation (VC funding, named customers), experience (pedigree of the management team), and market potential (how unique is the product; how much pent-up demand is there for this sort of solution; how well are they positioned competitively)." The companies listed include Sumo Logic, Ayasdi, Feedzai, Cloud Physics, BloomReach, Altiscale, Pursway, PlaceIQ, MemSQL, and Couchbase. ... View more

Rafał Wojdan's paper: Text Mining Reveals the Secret of Success: Identification of Sales Determinants Hidden in Customers' Opinions   Abstract:   Nowadays, in the Big Data era, Business Intelligence Departments collect, store, process, calculate, and monitor massive amounts of data. Nevertheless, sometimes hundreds of metrics built on the structured data are inefficient to explain why the offered deal sold better or worse than expected. The answer might be found in text data that every company owns and yet is not aware of its possible usage or neglects its value. This project shows text mining methods, implemented in SAS® Text Miner 12.1, that enable the determination of a deal's success or failure factors based on in-house or Internet-scattered customers' views and opinions. The study is conducted on data gathered from Groupon Sp. z o.o. (Polish business unit) - e-commerce company, as it is assumed that the market is by and large a customer-driven environment. ... View more

I just attended the SAS Analytics Conference in Orlando Florida during which I gave a presentation on implementing the SAS Content Categorization Studio at USAID.  I was able to pass on the very good news that we were able to put our Geographic Descriptor profile into production with more in the pipeline. My talk was based on my experience with implementing the SAS Content Categorization from planning to production. As such, I did not talk too much about writing profiles or code, but more on project management and workflow integration. If you are interested, I have attached the PowerPoint presentation that I used. I attended a number of other presentations as well as the three-day course on Data Mining, all of which were interesting and useful. I would recommend the Data Mining course to other people who do not have statistical backgrounds but would like to get a handle on some of the techniques that are used in Text Mining. Gordon Linoff, the instructor was quite good at explaining these ideas in an accessible way. The most exciting thing for me was being introduced to the new SAS Contextual Analysis tool. I had been saying to some of the other attendees at the conference that I wished for a way to blend the capabilities of both the SAS Content Categorization Studio and the SAS Text Mining and there it was in the SAS Contextual Analysis tool just being unveiled at the conference. I didn’t see a page for it on the SAS site, so I do hope that you SAS folks will let us know when we can see more about this intriguing development in the SAS product line. ... View more

Hi all, Could someone tell me what kinds of algorithms are used with the SAS Content Categorization? I do not want to see the exact formula, just the names of the types of algorithms used such as K-nearest neighbor, decision trees, naive Bayes, greedy search, etc. Thanks in advance. ... View more

I just read an article in eDiscovery Times about Beyond Recognition: BeyondRecognition Ranked as Top Disruptive eDiscovery Technology to Watch in 2012 : eDiscovery Times The author makes the point that OCR technology has not improved in the last 5 to 10 years, but that the Beyond Recognition (BR) application allows much more sophisticated technique of "glyph clustering." I'm interested in this as we have to OCR all of our documents that have been digitized from paper. When the paper copy is not in good condition, it can be very difficult to get a good image and therefore accurate representation of the text. Has anyone used BR and what has been your experience with it? ... View more

Seven tricky sentences for NLP and text mining algorithms - AnalyticBridge Posted by Mirko Krivanek on Text Mining - AnalyticBridge I thought that these were very interesting. "A land of milk and honey" becomes "A land of Milken Honey" (algorithm trained on Wall Street Journal from the 1980's where Michael Milken was mentioned much more than milk) "She threw up her dinner" vs. "She threw up her hands" "I ate a tomato with salt" vs. "I ate a tomato with my mother" or "I ate a tomato with a fork" Words ending with -ing, e.g. "They were entertaining people" "He washed and dried the dishes", vs. "He drank and smoked cigars" (in the latter case he did not drunk cigars) "The lamb was ready to eat" vs. "Was the lamb hungry and wanting some grass?" Words with multiple meaning (e.g. a bay can be a color, type of window or body of water) I would add to the above 7, words that are often used interchangeably, but are intended to mean two different things. For example in the Development Experience Clearinghouse, evaluations are intended to be used to describe documents that analyze either the performance of a project or the impact a project has made on a sector or geographic location. Assessments are supposed to be documents that analyze the conditions of a particular sector or geographical location before a project or program takes place. And yet, the terms are often used indiscriminately within the documents themselves. A human can look at the document and discern if it is an assessment or an evaluation, but it's very difficult to write rules for the SAS Content Categorization Studio to parse the differences. What linguistic challenges do others have when writing profile rules or texting mining algorithms? ... View more