Text Analytics 2014: Q&A with Fiona McNeill, SAS


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I post a yearly look at the Text Analytics industry — technologies and market developments — from the provider perspective. This year’s is Text Analytics 2014.

To gather background material for the article, and for my forth-coming report Text Analytics 2014: User Perspectives on Solutions and Providers (which should be out by late May), I interviewed a number of industry figures: Lexalytics CEO Jeff Catlin, Clarabridge CEO Sid Banerjee, Fiona McNeill of SAS, Daedalus co-founder José Carlos González, and Tom Anderson of Anderson Analytics and OdinText. (The links behind the names will take you to the individual Q&A articles.) This article is —

Fiona McNeill, SASText Analytics 2014: Q&A with Fiona McNeill, SAS

Fiona McNeill is Global Product Marketing Manager at SAS, co-author of The Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World. The following are her December, 2013 Q&A responses:

1) How has the market for text technologies, and text-analytics-reliant solutions, changed in the past year? Any surprises?

Text analytics is now much more commonly recognized as mainstream analysis, seen to improve business decisions, insights and helping drive more efficient operations.   Historically, those of us in this field spent time gaining mindshare that text should be analyzed (beyond analysis of sentiment, mind you) – and over the past year this has shifted to best practice methods of describing the ROI from text analytics to upper management.  This demonstrates common recognition within organizations that there is value in doing text analysis in the first place. And now the focus has shifted to how best to frame that value for senior stakeholders.

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The ease of analyzing big text data (hundreds of millions or billions of documents) has also improved over the past year, including extensions of high-performance text mining (from SAS) to new distributed architectures, like Hadoop and Cloudera.  Such big content technologies will continue to expand and we can expect functionality to extend to more interactive and visual text analytics capabilities over the coming year.

2) Do you have a 2013 user story, from a customer, that really illustrates what text analytics is all about?

We can speak to customer applications that illustrate what text analytics is all about, not mentioning names unfortunately.   One is a retail client, that recognized text data as a rich source, addressing a wide range of initial business challenges – from real-time digital marketing, bricks-and-mortar risk monitoring, automatic detection of issues and sentiment from customer inquiries, internal problem identification from on-line help forums, improve web purchases with more relevant content, improving predictive model scores for job candidate suitability, and more. This SAS customer understood that text data is everywhere, which means that analysis of text data will help them better answer whatever business question they have.

Another customer is a manufacturer, who strategically understands that the power of text analytics and how it improves collaboration, communication and productivity within an organization. As such, they wanted an extensible platform to address all types of text documents. They also had a wide-range of written languages that they needed to integrate into existing search and discovery methods, in order to provide more accurate and more relevant information across their entire business. This SAS customer understood the innovation that can come when resources are freed from searching, and when they are empowered with finding the answers they need and when they need it, creating an organization with “The Power to Know.”

We have a European customer announcement [that came] out in February, focused on leveraging WiFi browsing behavior and visitor profiles to create prescriptive advertising promotions in real-time to in-store shoppers. This is big data, real-time, opportunistic marketing – driven by text insights and automated operational decision advertising execution. In other words, putting big text insights to work – before the data is out of date.

3) How have perceptions and requirements surrounding sentiment analysis evolved? Where are sentiment capabilities heading, in your view?

It is no longer necessary to explain why sentiment analysis is important, it’s been largely accepted that customer, prospect and the public perception an organization is useful to understand product and brand reputation.  Historically, there was a focus on how well these models worked. It’s gradually being understood that there are tradeoffs between precision and recall associated with sentiment scores, at least in some domains.  Acceptance it appears (and as with any new modeling technique), has occurred within the bounds of applicability to adding previously unknown insight into the context of comments, reviews, social posts and alike.  To that end, and when a generalized methodology is used, as is the case at SAS, the sentiment polarity algorithm is evolving to examine an even broader set of scenarios – from employee satisfaction, author expertise, mood of an individual, and so forth.  Sentiment appears to be headed to the structured data analysis realm – becoming a calculated field that is used in other analysis – like predictions, forecasts, and interactive visual interrogation. And as such, identifying the ROI of sentiment analysis efforts is expected to become easier.

4) What new features or capabilities are top of your customers’ and prospects’ wish lists for 2014? And what new abilities or solutions can we expect to see from your company in the coming year?

At SAS, all software development is driven by our customer needs – and so products you see coming from SAS are based on what they told us require to solve business challenges and take advantage of market opportunities. For text analytics, our customers continue to want to more interactive text visualizations – to make it even easier to explore data to both derive analysis questions and to understand the insights from text results. They want easier methods to develop and deploy text models. Our customers also want more automation to simplify the more arduous text related tasks, like taxonomy development. They want to easily access the text, understand it, the sentiment expressed in it, extract facts and define semantic relationships – all in one, easy-to-use environment. They don’t want to learn a programming language, spend time and resource integrating different technologies or use multiple software packages.  We’ve responded to this with the introduction of SAS Contextual Analysis – that will, by mid-year 2014 expand to provide an extremely comprehensive, easy-to-use and highly visual environment for interactively examining and analyzing text data. It leverages the power of machine learning and includes with end-user subject matter expertise.

We will also continue to extend technologies and methods for examining big text data – continuing to taking advantage of multi-core processing and distributed memory architectures for addressing even the most complex operational challenges and decisions that our customers have. We have seen the power of analyzing big data with real-time data-driven operations and will continue to extend platforms, analytic methods and deployment strategies for our customers. In October, 2013, we announced our strategic partnership with SAP – to bring SAS in-memory analytics to the SAP HANA platform. You’ll see our joint market solutions announced over the coming year.

5) Mobile’s growth is only accelerating, complicating the data picture, accompanied by a desire for faster, more accurate, and more useful, situational insights delivery. How are you keeping up?

With a single platform for all SAS capabilities we have ability to interchange a wide range of technologies, which can easily be brought together to solve even the most complex analytic business challenges, for mobile or other types of real-time insight delivery. SAS offers a number of real-time deployment options, including SAS Decision Manager (for deploying analytically sound operational rules), SAS Event Stream Processing Engine (for analytic processing within event streams), SAS Scoring Accelerator for Hadoop (as well as other big data stores – for real-time model deployment), and real-time environments for analyzing and reporting data – that operate on mobile devices, such as SAS Visual AnalyticsSAS also has native read/write engines and support for web services, and as mentioned above, we have recently announced strategic partnership with SAP for joint technology offerings bring the power of analytics to the SAP/HANA platform.

We are constantly extending such capabilities, recognizing that information processing is bigger, faster and more dependent on well-designed analytic insight (including that from text data) than ever before. This growing need will only continue.

6) Where does the greatest opportunity reside, for you as a solution provider? Internationalization? Algorithms, visualization, or other technical advances? In data integration and synthesis and expansion to new data sources? In providing the means for your customers to monetize data, or in monetizing data yourselves? In untapped business domains or in greater uptake in the domains you already serve?

Given our extensive portfolio of solutions, SAS continues to invest in technology advances that our customers tell us they want to address the growing complexities of their business.  This includes ongoing advances in algorithms, deployment mechanisms, data access, processing routines and other technical considerations. We continue to expand our extensive native language support, with over 30 languages and dialects already available in our text analytics products. Additional languages will be added as customer needs dictate.  And while we already offer solutions to virtually every industry, we continue to further develop these products to provide leading edge capabilities for big data, high-performance, real-time analytically-driven results for our customers.  You’ll see us moving more and more of our capabilities to cloud architectures.  For SAS, another great opportunity is the production deployment of analytics to automate, streamline and advance the activities of our customers. You’ll continue to see announcements from us over the coming year.

7) Do you have anything to add, regarding the 2014 outlook for text analytics and your company?

At SAS, text data is recognized as being a rich source of insight that can improve data quality, accessibility and decision-making. As such, you’ll see text-based processing capabilities in products outside of the pure-play text analytics technologies.  And because of the common infrastructure that has been designed by SAS – all of these capabilities are readily integrated, and can be used to address a specific business scenario.  We will continue to extend text-based processing and insights into traditional predictive analysis, forecasting and optimization – as well as new solutions that include text analysis methods, and updates to existing products, like SAS Visual Analytics and our upcoming release of a new in-memory product for Hadoop (release announcement pending).   

From a foundational perspective, text-based processing continues to be extended throughout our platform, with pending linguistic rules augmenting business and predictive scoring in real-time data streams, with extensions to analytically derived metadata from text and more.  And given the nature and recognition of text and what it can bring to improved insights, you’ll also see our industry solutions continue to extend the use of text-based knowledge.

Thank you to Fiona! Click on the links that follow to read other Text Analytics 2014 Q&A responses: Lexalytics CEO Jeff Catlin, Clarabridge CEO Sid Banerjee, Daedalus co-founder José Carlos González, and Tom Anderson of Anderson Analytics and OdinText. And click here for this year’s industry summary, Text Analytics 2014.


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