Call for Speakers… on the Business Value of Language Technologies (Dec 4-5, Brussels)

LTaccelerateLogoDoes your organization seek to make sense of social, online, and enterprise data? If so, a new, European, conference is for you.

LT-Accelerate is designed to help businesses, researchers, and public administrations discover business value via language technologies. With your participation — as a speaker, panelist, workshop presenter, or attendee — LT-Accelerate will be a great conference. Please consider submitting a
proposal in response to our Call for Speakers, preferably by August 15, 2014.

LT-Accelerate takes place December 4-5, 2014 in Brussels. I am developing LT-Accelerate jointly with LT-Innovate, the forum for Europe’s language technology industry. You may be familiar with my Sentiment Analysis Symposium conference (which I will continue in the US) or know my work as an industry analyst, or perhaps you follow LT-Innovate. Together, with your participation, we will make LT-Accelerate an outstanding event where you will meet, learn from, and network with fellow business innovators, academic and industry thought leaders, solution providers, and consultants. LT-Accelerate is about opportunity, unique in Europe.

What sort of speakers are we seeking? (And who should attend, speaker or not?)

Whether you are a business visionary, experienced user, or technologist, please consider proposing a presentation or panel. Submit your proposal now.

Choose from among the suggested topics or surprise us. We invite speakers who will focus on customer experience, market research, media measurement, financial services, life sciences, security, or social intelligence — on applications such as interaction and workforce analytics, digital analytics, social listening and engagement, trading strategies, and survey analysis. Our program will extend to technologies for text and sentiment analysis, social network analysis, and big data. But don’t let this list limit you. We see opportunity in innovation!

Please do propose an LT-Accelerate presentation, workshop, or panel, and if you have questions, do get in touch with me (coordinates below) or LT-Innovate’s Philippe Wacker, at phw@lt-innovate.eu. And follow us on Twitter at @LTaccelerate.

I hope to see you in Brussels in December!

Clarabridge Gets Engaged: A Report from the C3 User Conference

The Clarabridge Customer Connections (C3) conference has gotten to be a thing for me. There can’t be more than a handful of people, beyond company co-founder and CEO Sid Banerjee and me, who have attended all six C3s, dating back to the fall of 2008.

The conference name reflects what the company does: Clarabridge facilitates customer connections for its own corporate clients. C3 themes have evolved over the years, tracking Clarabridge’s shift away from self-identifying as technology provider and toward a business-solutions focus. In a 2008 C3 release, Clarabridge proclaimed itself “the leading provider of text analytics software [for] customer experience management (CEM).” This year, I don’t believe I heard even one use of the term “text analytics” in the company’s conference presentations. Instead, customer experience and the customer journey occupied center stage, as they have for several years (my 2010 C3 reporting), expanding this year to a broadened message. CEO Sid said it this way: “We’ve crossed the chasm from measurement to engagement.”

Surely, the (re-)defining moment took place April 8, just weeks before C3, when Clarabridge announced its acquisition of Market Metrix, “the leading Enterprise Feedback Management (EFM) platform for the hospitality and gaming industries.” (EFM is a fancy name for survey systems for customer service and satisfaction, market research, and employee engagement.)

Clarabridge’s journey hasn’t been completely linear, however. C3’s big theme for 2013 was Intelligent Customer Experience. A year ago, Sid said, “ICE is really about integrating all the content, from all the sources, and applying intelligence to the multichannel content.” This year? No mention of Intelligent Customer Experience at C3, and the term is hidden away in nooks on the company’s Web site. I infer that while analytical intelligence — the ability to discover relevant insights in data — is necessary, it’s not a market differentiator. Indeed, in a briefing for analysts, Sid talked about two types of CEM company, those with text analytics, and those that are seeking to add the technology to their portfolios.

My take-away is that Clarabridge is competing on factors other than tool intelligence. The differentiators I see, and hear about from users, include ease-of-use, business-function and industry adaptation, and the ability to serve as a data hub via easy import of data from survey and social analytics tools.

Yet Clarabridge’s evolution, accelerated by the Market Metrix acquisition, puts Clarabridge on a competitive collision course with partners that include Confirmit and Verint. And sometimes those partners’ own steps put them in competition with Clarabridge, for instance, Verint, with its January 2014 acquisition of Kana, and the ever-tightening integration of social-intelligence platform Radian6 into Salesforce Marketing Cloud. Yet there are always new, complementary partners to develop, notably social-intelligence vendor Sysomos and speech-analysis provider Voci Technologies. The Voci partnership, by the way, will surely further test the Clarabridge-Verint partnership. The Sysomos link-up should last longer, so long as the companies operate in different spheres, noting that like Clarabridge, Sysomos has its own, robust natural-language processing technology.

Henry Edinger, chief customer officer at Travelers

Henry Edinger, chief customer officer at Travelers, sees customer insights everywhere

These partnerships are most often about data. A given customer will license software and services from multiple providers, and each system — each “touchpoint” or “channel” — generates data. In an “omni-channel” world, technology overlap is tolerable when the customers demand that their providers play nice (the Confirmit case), or the providers don’t (yet) compete (Clarabridge and Sysomos and also OpinionLab).

Consider the case of insurer Travelers, represented at C3 by Chief Customer Officer Henry Edinger, who works in a dynamic environment where “customer insights are everywhere,” where “everywhere” encompasses the leading social platforms and also solution/data providers Confirmit, Qualtrics, J.D. Power, and Lieberman Research Worldwide in addition to Clarabridge. Edinger spoke at C3 about an “outside-in approach,” where customer perceptions, interactions, and emotions create the customer experience. Analysis delivers insight but isn’t strictly by-the-numbers. Edinger described Travelers’ rewriting customer correspondence, replacing lawyer-crafted gems such as “I am writing to confirm that I will be handling the above referenced auto claim” with text that is such to be more-welcome to customers, “It was a pleasure speaking with you…”

Other Clarabridge customer, partner, and analyst presentations provided quite interesting, complementary industry insights.

On the best practices side, Jeff Mango talked about looking to Voice of the Customer data collection and analysis to surface problems, and Voice of the Employee for answers. Makes sense. Human judgment rules, even in the big data era, and human employees will understand customers in ways that machines can’t. Jeff also talked about the omni-channel challenge, the desire to know and link everything said everywhere. At Verizon, “we look at the right data sources at the right time, because we have 50.” You can’t do it all, all the time. But my favorite thought of Jeff’s was his linking customer business value and emotion.

Liz Spencer & Elizabeth Rector of Cisco talk text-infused analytical models

Liz Spencer & Elizabeth Rector of Cisco talk results from text-infused analytical models

The business value of sentiment, opinion, and emotion has been a focus of my work for several years now.

Another affirmation, for my 12 years in text analytics, was hearing Liz Spencer and Elizabeth Rector of Cisco describe how bringing results from customer satisfaction text analyses into their propensity-to-buy models led to a $5 million yearly sales gain, from that one, focused effort.

C3 did feature several partner presentations, from solution-provider partners and also from professional-service partners. It’s notable that the roster of Clarabridge service partners has expanded. Accenture has been on board for a few years, more recently joined by Acumen Solutions — information management lead Dave Marko is a Clarabridge alumnus; check out his 2013 C3 report — and this year, the W2O Group.

W2O’s Paul Dyer gave an interesting C3 presentation on Social Voice of the Customer. Dyer talked about a “complex ecosystem” of listening, filtering, and analysis solutions, built on tiers of data sources and suppliers, the latter including Gnip, DataSift, and Topsy. He offered the view that “software-enabled people” provide the best enterprise response to marketing challenges, and he described the emergence of “social intelligence supply chains” that harness analytics for business operations, via applications deployed ad-hoc and in command centers, to support customer engagement.

TemkinShot

People-centric experience design, according to Bruce Temkin

Finally, I always enjoy hearing customer-experience expert Bruce Temkin, who keynoted at C3 for perhaps the third time. This year’s talk was on “people-centric experience design,” which he characterizes as “fostering an environment that creates positive, memorable human encounter.” This design approach builds on purpose, empathy, and memories. While empathy in action, in corporate settings, boils down to ability to based decisions on deep customer insights — that is, the product of analytics — Bruce makes a key point that, in effect, it’s personal, the ability of customer-experience designers to see products and services from the customers’ point of view, as customers would experience them.

The implication, of course, is that applications powered by the Clarabridge Intelligence Platform, coupled with Clarabridge Social Intelligence, will help corporations get there, to uniformly positive customer interactions, through intentional, designed customer experience. The addition of engagement capabilities to the Clarabridge product portfolio, as showcased at this year’s C3 conference, is a significant advance toward that goal.


Disclosure: Clarabridge waived my C3 registration fee, but I did not ask the company to pay my travel expenses. Clarabridge paid me for a contributed blog article, Sentiment Analysis and Business Sense.

Metadata, Connection, and the Big Data Story

The big-data analysis process reduces to three elements: Collection, Synthesis, and Insight. We gather relevant data, harmonize and link it, and use analysis findings situationally. In the online/social/sensor era, “relevant” may reflect enormous data volume. “Harmonize” responds to variety, and situational applications must often accommodate high-velocity data. Context and latency considerations complicate matters. Latency refers to acceptable data-collection, analysis, and reporting lag. Low latency is crucial in online, mobile, and enterprise interactions. And context means metadata, good-old data about data, which can boost analysis accuracy (and also aide in proper data governance).

This article is about the roles of metadata and connection in the big-data story.

The Tower of Babel, by Pieter Bruegel the Elder

Human communications are complex: “The Tower of Babel” by Pieter Bruegel the Elder

Human Data: Fact, Feelings, and Intent

My particular interest is “human data,” communicated in intentionally expressive sources such as text, video, and social likes and shares, and in implicit expressions of sentiment. Implicit: We infer sentiment signals from behavior tracks (transaction records, click-/tap-streams, and geolocation) and social-network links and interactions.

Human data, from devices, online and social platforms, and enterprise transactional and operational systems, captures what Fernando Lucini characterizes as “the electronic essence of people.” Lucini is CTO of HP Autonomy. He is one of four industry authorities I interviewed as story sources. Lucini observes, “we interact with many systems, we communicate, we create,” yet analytics providers “don’t connect the dots in a way that’s truly useful, for each of us to be better served by information.”

The others I interviewed — IBM analytics strategist Marie Wallace, AlchemyAPI founder and CEO Elliot Turner, and Prof. Stephen Pulman of the University of Oxford and start-up TheySay — have similar points of view. (IBM and TheySay sponsored my recent, New York Sentiment Analysis Symposium. AlchemyAPI is sponsoring my up-coming market study, “Text Analytics 2014: User Perspectives on Solutions and Providers,” as is Digital Reasoning, mentioned later in this article.)

According to Marie Wallace, “the biggest piece of missing information isn’t the content itself, but the metadata that connects various pieces of content into a cohesive story.” What sort of metadata?

Stephen Pulman refers to properties of the message (for example, whether it’s humorous, sincere, or likely fake) and of the author, such as sex, age, and maybe also influence and ideology, which “tell us how we should treat the content of the message, as well as being interesting in themselves.”

As if expanding on Pulman’s thought, Marie Wallace asks, “if I don’t know the individual and the background behind her current communication, how can I really decide what her mood or intent is, and most importantly take effective action?”

Elliot Turner is particularly interested in intent mining, applied, for example, in efforts to predict an individual’s purchasing behavior.  Turner says, “success will combine elements like a person’s interests, relationships, geography — and ultimately his identity, purchase history and privacy preferences — so that applications can plot where a person is in his ‘buyer’s journey’ and provide the best offers at the best times.”

Natural Language Processing

Natural language processing (NLP) (and parsing and interpretation for formal languages) is a route to mining the information content of text and speech, complemented by techniques that extract interesting information from sound, images, and video. (Of course, network, geospatial, and temporal data come into play: Matter for another article.) Recognizing that NLP includes both language understanding and language generation, two parts of a conversation — think about, but also beyond, “question answering” systems such as Apple Siri — I asked my interviewees, How well are we doing with NLP?, and also about our ability to mine affective states, that is, mood, emotion, attitudes, and intent.

Stephen Pulman sees “steady progress on parsing and semantic-role labeling, etc., for well-behaved text” — by “well-behaved,” he means (relatively) grammatical, correctly spelled, and slang-free — but “performance goes down pretty steeply for texts like tweets or other more casual forms of language use.”

Elliot Turner observes, “a never-ending challenge to understanding text is staying current with emerging slang and phrases,” and Marie Wallace believes, “if we look to lower quality content (like social media), with inherently ambiguous analysis (like sentiment, opinion, or intent), then it’s still a bit of a crapshoot.”

Turner says “the trend is easy to spot: The interactive question-answering capabilities made famous by IBM’s Watson will become commonplace, offered at a fraction of today’s costs and made available as easy-to-integrate Web services… We will see search and retrieval transform to become dialog-based and be highly aware of an ongoing context. Machines will stay ‘in conversation’ and not treat each search as a unique event.”

In conversational context, Fernando Lucini sees a problem of understanding how information elements link to other pieces of information: “It’s how the information connects that’s critical,” and understanding depends on our ability to tap into the right connections. He sees progress in analytical capabilities being driven swiftly by increasing demand, applying “all sorts of techniques, from unsupervised to supervised [machine learning], from statistical to linguistic and anything in between.”

One particular technique, unsupervised learning, which AlchemyAPI CEO Turner describes “enabl[ing] machines to discover new words without human-curated training sets,” is often seen as materially advancing language-understanding capabilities, but according to Autonomy CTO Lucini, the real aim is a business one, “making sure that any piece of information fulfills its maximum potential… Businesses need to have a clear view how [information availability] translates to value.”

While Marie Wallace says, “we’ve only just scratched the surface in terms of the insights that can be derived from these new advanced learning techniques,” Prof. Pulman notes, “there is usually a long way to get from a neat research finding to an improved or novel product, and the things that researchers value are often less important than speed, robustness and scalability in a practical setting.” (Pulman gave a quite interesting talk, Deep Learning for Natural Language Processing, at the March 6, 2014 Sentiment Analysis Symposium.)

Opportunity

I see mobile computing as opening up a world of opportunity, exploitable in conjunction with advances on a variety of technical and business fronts. Which? I asked my interviewees. The responses bring us back to this article’s starting point, metadata, context, and connection.

Marie Wallace says “Mobile is the mother load of contextual metadata that will allow us to provide the type of situational insights the contextual enterprise requires.” Add longer-established sources to the picture, and “there is a significant opportunity to be realized in providing integration and analysis (at scale) of social and business data… Once we combine interactional information with the business action, we can derive insights that will truly transform the social business.”

This combination, which I referred to as “synthesis,” is at the core of advanced big-data analytics, the key to solutions from providers that include, in addition to IBM and HP Autonomy, companies such as Digital Reasoning and Palantir.

IBMer Wallace adds, “privacy, ethics, and governance frameworks are going to be increasingly important.”

According to Fernando Lucini, mobile is great for HP Autonomy because it means “more use of information — in our case, human information.” He sees opportunity in three areas: 1) supporting “better and more real-time decisions [that] connect consumer and product,” 2) information governance, because “securing or protecting information, as well as evaluating the risk in information and then being able to act suitably and in accordance with regulation and law, is a considerable integration and synthesis challenge,” and 3) provision of self-service, cloud tools.

Stephen Pulman similarly starts with a technical observation and then draws a lesson about business practices: “One thing we have learned at TheySay is that a combination of text analysis like sentiment along with other, often numerical, data gives insights that you would not get from either in isolation, particularly in the financial services or brand management domains. Finding the right partners with relevant domain expertise is key to unlocking this potential.”

Finally, Elliot Turner discusses the opportunity created by his company’s variety of technology, providing elements such as text analysis and classification and computer vision, via cloud services: “Large public companies are exploring how to incorporate modern text analysis capabilities into their established product lines,” while “innovative startups are charging at full speed with business plans aimed squarely at disrupting… business intelligence, customer support, advertising and publishing, sales and marketing automation, enterprise search, and many other markets.”

So we learn that the opportunity found via big-data analysis takes the form of situational insights, relying on integration and analysis of social and business data. It involves connection, governance, and easier access to strong tools, guided by domain expertise. The aims are expanded capabilities for some, innovation and disruption for the upstarts. Achieve these aims, and you just might have the clear view cited by one of my interviewees — provided via savvy, integrated, all-data analyses — of the path to value.


Read the full Q&A texts by clicking or tapping on the names of interviewees Fernando Lucini (HP Autonomy), Marie Wallace (IBM), Elliot Turner (AlchemyAPI), and Stephen Pulman (University of Oxford and TheySay). Also check out my recent article, Text Analytics 2014.

Analytics, Semantics & Sense: Q&A with Marie Wallace, IBM

I recently posted an article, Metadata, Connection, and the Big Data Story, covering the big-data analysis process as applied to “human data” that is communicated in intentionally expressive sources such as text, video, and social likes and shares and in implicit expressions of sentiment.

The article is spun out from Q&A interview of four industry figures: Fernando Lucini (HP Autonomy), Marie Wallace (IBM), Elliot Turner (AlchemyAPI), and Stephen Pulman (University of Oxford and TheySay). (IBM sponsored my recent, New York Sentiment Analysis Symposium.) Read each interview by clicking or tapping on the name of the interviewee.

This interview is with Marie Wallace, and as a bonus, you’ll find a video embedded at the foot of this article, of Ms Wallace’s March 6, 2014 Sentiment Analysis Symposium presentation, Engagement: The Unspoken Connection. First –

MarieWallaceAnalytics, Semantics & Sense: Q&A with Marie Wallace, IBM

1) What’s the really interesting information content that we’re not really getting at yet, and what’s interesting about it?

Having spent the last several years focused on social analytics, its not going to be surprising when I suggest that the biggest piece of missing information isn’t the content itself but the meta-data that connects various pieces of content into a cohesive story. We are still, for the most part, taking content snippets in isolation and making decisions about individuals based on this incomplete picture. It’s like listening into a telephone call, without understanding the background of the call, and then only catching every second sentence.

2) How well are we doing with Natural Language Processing, noting that formally, “processing” includes both understanding and generation, two parts of a conversation?

This depends totally on the type of analysis and the source of the content. If I look at areas like healthcare, I believe we are doing a phenomenal job where technologies like Watson are deriving some amazing insights from analyzing text. However, if we look to lower quality content (like social media) with inherently ambiguous analysis (like sentiment, opinion, or intent) then it’s still a bit of a crapshoot.

3) And how well are we able to mine and automate understanding of affective states, of mood, emotion, attitudes, and intent, in the spectrum of sources available to us?

I don’t believe these can be reliably extracted unless we do a better job at filling in the content & meta-data gaps. If I don’t know the individual and the background behind their current communication, how can I really decide what their mood or intent is, and most importantly take effective action. Maybe I’m complaining about my phone after having sent two e-mails to the unresponsive helpdesk, or maybe its because I’m away from home at Christmas and frustrated that I can’t get a good signal at my current location. The customer would expect a completely different response to each scenario.

4) Deep learning, active learning, or maybe some form of machine learning that’s being cooked up in a research lab: What business benefits are delivered by these technologies, and what are the limits to their usefulness, technical or other?

We’ve only just scratched the surface in terms of the insights that can be derived from these new advanced learning techniques and until such time as they are broadly adopted in large scale operational systems, it will be difficult to predict the limits of their usefulness.

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 is your company keeping up?

Mobile is at the heart of everything we do both in terms of the solutions we build for customers and those we deploy within our own business. Mobile is the mother load of contextual meta-data that will allow us to provide the type of situational insights the contextual enterprise requires.

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

Coming from a company with technologies and solutions spanning such a broad range of businesses, this is impossible to answer in the absolute. However from my own personal perspective as someone working in the Social Business Analytics space, I believe that there is a significant opportunity to be realized in providing integration and analysis (at scale) of social and business data. Social analytics without business data is incomplete at best or completely misleading at worst, however once we combine interactional information (Marie spoke to John) with the business action (to close a sales deal) we can derive insights that will truly transform the social business.

7) Anything to add, regarding the 2014 outlook for analytical and semantic and sensemaking technologies?

2014 is going to be the year of the Graph, at a minimum in the area of social and collaboration, although more likely we will see it applied broadly across the business; allowing new insights to be derived by understanding not just what is being said, but how people are interacting with each other and with the business. Privacy, ethics, and governance frameworks are going to be increasingly important as we look to apply more analytics on people information and derive new insights that need to be tempered by privacy and ethical considerations.

Thank you to Marie!

Click on the links that follow to read other Analytics, Semantics & Sense Q&A responses: Fernando LuciniElliot Turner, and Stephen Pulman. And click here for the article I spun out from them, Metadata, Connection, and the Big Data Story.


Analytics, Semantics & Sense: Q&A with Elliot Turner, AlchemyAPI

I recently posted an article, Metadata, Connection, and the Big Data Story, covering the big-data analysis process as applied to “human data” that is communicated in intentionally expressive sources such as text, video, and social likes and shares and in implicit expressions of sentiment.

The article is spun out from Q&A interview of four industry figures: Fernando Lucini (HP Autonomy), Marie Wallace (IBM), Elliot Turner (AlchemyAPI), and Stephen Pulman (University of Oxford and TheySay). (AlchemyAPI is sponsoring my up-coming market study, “Text Analytics 2014: User Perspectives on Solutions and Providers.”) Read each interview by clicking or tapping on the name of the interviewee.

This interview is –

ElliotTurnerAnalytics, Semantics & Sense: Q&A with Elliot Turner, AlchemyAPI

1) What’s the really interesting information content that we’re not really getting at yet, and what’s interesting about it?

Companies always want to know what consumers will buy and when they will buy it. But Intent Mining is still in its infancy.  Today, it is easy to classify the topics and sentiment of an individual post, or roll up how Twitter feels about the top entities extracted from a real-time hashtag feed.  The performance of these classification and ranking processes will get better, but they are just first components of future solutions that seek to predict an individual’s purchasing behavior.  Success in this task will combine other elements like a person’s interests, relationships, geography – and ultimately their identity, purchase history and privacy preferences – so that applications can plot where a person is in their ‘buyer’s journey’ and provide the best offers at the best times.

Going beyond text, other areas for big progress are in the mining of audio, speech, images and video.  These are interesting because of their incredible growth. For example, we will soon see over 1 billion photos/day taken and shared from the world’s camera phones. Companies with roots in unsupervised deep-learning techniques should be able to leverage their approaches to dramatically improve our ability to correctly identify the content contained in image data.

2) How well are we doing with Natural Language Processing, noting that formally, “processing” includes both understanding and generation, two parts of a conversation?

Google has trained us to search using keywords, and this won’t change overnight. But the trend is easy to spot: the interactive question-answering capabilities made famous by IBM’s Watson will become commonplace, offered at a fraction of today’s costs and made available as easy-to-integrate web services.

The goal of interactive search is to let us get faster answers to the detailed questions we ask every day. Early, mass adopters of new Question Answering applications include large support organizations with complex products where the entire business model can be improved with faster time-to-answer.  As interactive Question Answering systems move beyond professional analysts we will see search and retrieval transform to become dialog-based and be highly aware of an ongoing context – machines will “stay in conversation” and not treat each search as a unique event, but encourage ad-hoc drill down or expansion of answers already obtained.

3) And how well are we able to mine and automate understanding of affective states, of mood, emotion, attitudes, and intent, in the spectrum of sources available to us?

Work is on-going to move beyond a binary classification of sentiment to identifying richer emotional states, and applying these results to the best way a company should engage an individual.  But gaining a deeper, more valuable understanding of an individual’s intent, and tying this intent to the specific call-to-action a company could make, are big tasks that are just getting off the ground.

A never-ending challenge to understanding text is staying current with emerging slang and phrases. Unsupervised learning can enable machines to discover new words without human-curated training sets.

4) Deep learning, active learning, or maybe some form of machine learning that’s being cooked up in a research lab: What business benefits are delivered by these technologies, and what are the limits to their usefulness, technical or other?

Few NLP companies are truly deploying deep learning methods, even though deep learning can give us a much richer representation of text than is possible with traditional NLP approaches. Deep learning can produce more robust text and vision systems that hold their accuracy when analyzing data far different from what they were trained on.  Plus, unsupervised techniques make it practical to keep up with the rapid evolution of everyday language.  Did you know that 15 percent of the daily queries submitted to Google – over 500 million – have never been seen before by Google’s search engine? And this rate of previously unknown phrases is unchanged in the past 15 years? The ability to understand today’s short, jargon-filled phrases, and keep up with tomorrow’s new words, are predicated on mastering unsupervised, deep learning approaches.

With respect to limitations, unsupervised deep learning isn’t magic. It isn’t cheap or easy, either. It first requires some new skills for driving continued innovation in machine learning. Second, we need massive training data sets in order to build hierarchical representations of data without human involvement. In fact, we believe AlchemyAPI’s algorithms are trained on data corpora orders of magnitude larger than most competitive solutions.  Finally, processing all of this information at such huge scale requires technical innovations all their own, coupled with significant amounts of affordable computing power. Thankfully, all  these limitations are being addressed today to help bring about the next generation of intelligent machines.

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 is your company keeping up?

Almost overnight, Mobile has enabled an explosion in image capture to where we will soon surpass 1 billion photos taken and shared each day. AlchemyAPI is investing heavily in computer vision technologies used to help machines quickly understand the content of all these photos in order to drive a host of new commerce, content and social applications.

Response time is perhaps THE critical driver for mobile applications.  Our obsession with speed of results and low latency through our cloud infrastructure leads to AlchemyAPI being consistently chosen over competitors whenever NLP-based applications require near real-time processing of unstructured data.  

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

Today, AlchemyAPI is the world’s most popular text analysis cloud service.  Our obsession with speed and accuracy of our results has resulted in being selected by designers of innovative, near real-time NLP applications to process billions documents each month.  We will continue to advance our algorithms while adding new deep learning-based features including a new classifier, a computer vision API and advanced question-answering solutions.

Our greatest opportunity is to provide development teams a programmable Internet platform that makes it easy for their companies to create and deploy NLP applications in the cloud.  The Alchemy platform can include built-in data sources, NLP functionality, storage of results, data analysis and reporting. Customer apps will run on a secure service that scales, tunes, and backs up data automatically. Over time, visual tools, sample UIs and our library of components can minimize custom coding and streamline the assembly of NLP apps using a building-block approach.  These apps then integrate easily into a customer’s product lines and business systems using open APIs.

7) Anything to add, regarding the 2014 outlook for analytical and semantic and sensemaking technologies?

 Being a provider of NLP-based Web services gives AlchemyAPI a unique market perspective.  We help our customers build new applications that serve nearly every industry vertical and run on every computing device.  Because we are a subscription service, AlchemyAPI is also able to get a sense of which applications are succeeding and how fast they are growing.

What we see leads us to this conclusion: despite our industry’s penchant for hype, there is an arms race brewing in NLP-driven applications and we see 2014 as a year where much more of this energy moves from experimentation to building and deploying new apps into production.

Two types of businesses we talk to every day include large public companies who are exploring how to incorporate modern text analysis capabilities into their established product lines. The second type are innovative startups who are charging at full speed with business plans aimed squarely at disrupting the established order.  It’s this second group that’s adding excitement to our industry.  They are intent on making inroads into business intelligence, customer support, advertising and publishing, sales and marketing automation, enterprise search and many other markets.   They won’t all make it, but they are generating a lot of new interest, a lot of new customers, and forcing everyone to innovate faster.

Thank you to Elliot!

Click on the links that follow to read other Analytics, Semantics & Sense Q&A responses: Fernando LuciniMarie Wallace, and Stephen Pulman. And click here for the article I spun out from them, Metadata, Connection, and the Big Data Story.

Analytics, Semantics & Sense: Q&A with Stephen Pulman, Univ of Oxford & TheySay

I recently posted an article, Metadata, Connection, and the Big Data Story, covering the big-data analysis process as applied to “human data” that is communicated in intentionally expressive sources such as text, video, and social likes and shares and in implicit expressions of sentiment.

The article is spun out from Q&A interview of four industry figures: Fernando Lucini (HP Autonomy), Marie Wallace (IBM), Elliot Turner (AlchemyAPI), and Stephen Pulman (University of Oxford and TheySay). (TheySay sponsored my recent, New York Sentiment Analysis Symposium.) Read each interview by clicking or tapping on the name of the interviewee.

This interview is with Stephen Pulman, and as a bonus, you’ll find a video embedded at the foot of this article, of Prof. Pulman’s March 6, 2014 Sentiment Analysis Symposium presentation, Deep Learning for Natural Language Processing. First –

Stephen Pulman, Univ of OxfordAnalytics, Semantics & Sense: Q&A with Stephen Pulman

1) What’s the really interesting information content that we’re not really getting at yet, and what’s interesting about it?

Well, “interesting” is relative to organizations and individuals. For organizations that are listening to customers, I’d say that properties of the message (e.g. likely to be fake/humorous/sincere) and the author (simple things like gender, age, maybe also more sophisticated things like influence, ideology etc) are the things that we are not always getting. They tell us how we should treat the content of the message, as well as being interesting in themselves.

2) How well are we doing with Natural Language Processing, noting that formally, “processing” includes both understanding and generation, two parts of a conversation?

Not up to speed on generation, I’m afraid: it does not seem a very active research area at present.

For analysis, we are making steady progress on parsing and semantic role labeling etc. for well-behaved text. Performance goes down pretty steeply for texts like tweets or other more casual forms of language use, unfortunately. Finding ways to customise existing parsers etc to new genres is an important research task.

3) And how well are we able to mine and automate understanding of affective states, of mood, emotion, attitudes, and intent, in the spectrum of sources available to us?

Again, reasonably well in well-behaved text. But a very difficult task is to pre-filter the texts so that only genuine expressions of these states are counted, otherwise any conclusions drawn (if you include texts like advertisements etc) will be misleading. And in some areas, even recognizing the entities you are interested in can be challenging: one of my students, for example, was interested in what people were saying about horse racing, but we found it almost impossible to harvest relevant data because of the wide range of names for horses: e.g. “Ask the wife,” “Degenerate,” and even “Say.” And in tweets there’s often no capitalization to help. I think this kind of data cleaning or data pre-processing will become more important as the proportion of robot-generated text out there increases.

4) Deep learning, active learning, or maybe some form of machine learning that’s being cooked up in a research lab: What business benefits are delivered by these technologies, and what are the limits to their usefulness, technical or other?

All forms of machine learning should deliver benefits in rapid adaptation to new languages and domains. But there is usually a long way to get from a neat research finding to an improved or novel product, and the things that researchers value (getting an extra 1% accuracy on a benchmark, for example) are often less important than speed, robustness and scalability in a practical setting. But for a practical demonstration of the utility of deep learning we have Google’s speech rec…

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 is your company keeping up?

Wearing my TheySay hat, we are about to release a service, MoodRaker, which will offer real time analysis of a large choice of different text streams, along a variety of dimensions, configurable from any (sensible) browser.

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

I wish I knew! One thing we have learned at TheySay is that a combination of text analysis like sentiment along with other, often numerical, data gives insights that you would not get from either in isolation, particularly in the financial services or brand management domains. Finding the right partners with relevant domain expertise is key to unlocking this potential.

7) Anything to add, regarding the 2014 outlook for analytical and semantic and sensemaking technologies?

No predictions, but I’m looking forward to seeing what happens…

Thank you to Stephen!

Click on the links that follow to read other Analytics, Semantics & Sense Q&A responses: Fernando LuciniMarie Wallace, and Elliot Turner. And click here for the article I spun out from them, Metadata, Connection, and the Big Data Story.


Analytics, Semantics & Sense: Q&A with Fernando Lucini HP Autonomy

I recently posted an article, Metadata, Connection, and the Big Data Story, covering the big-data analysis process as applied to “human data” that is communicated in intentionally expressive sources such as text, video, and social likes and shares and in implicit expressions of sentiment.

The article is spun out from Q&A interview of four industry figures: Fernando Lucini (HP Autonomy), Marie Wallace (IBM), Elliot Turner (AlchemyAPI), and Stephen Pulman (University of Oxford and TheySay). Read each interview by clicking or tapping on the name of the interviewee.

This interview is –

Analytics, Semantics & Sense: Q&A with Fernando Lucini, HP Autonomy

Fernando Lucini, HP Autonomy

Fernando Lucini, HP Autonomy

1) What’s the really interesting information content that we’re not really getting at yet, and what’s interesting about it?

For me it’s the electronic essence of people. We interact with many systems, we communicate, we create, etc.. we are represented as human beings by a wealth of information and context, yet we don’t connect these dots, in a way that’s truly useful, for each of us to be better served by information. We have some pockets of what is referred to in the industry as “profiling” which clumsily help us in being presented ecommerce and advertising, but don’t help us choose a doctor when needed nor help us connect with our loved ones, or help us be better professionals through information in time etc….For me this is critical as we are surrounded by such a mass of generally low value information that we are going to need for this “essence” or “characterization” to be our pre-filter for information.

It’s close to my heart as it’s a human information problem and it’s what we are all about here at HP Autonomy.

2) How well are we doing with Natural Language Processing, noting that formally, “processing” includes both understanding and generation, two parts of a conversation?

Seeing as NLP in this context (question-answer) has been around and well understood for decades now, it feels like it’s having a new lease of life with things like Siri and others. But the reality is that the volumes and specialization needed to have the “conversation” means that we will only be dealing with a portion (the most traversed) of “conversations”. I’m not sure this is the solution to our interaction with information, but it’s certainly a part of it. Certainly in the consumer world. In the enterprise world this might prove more tricky as one does not share the conversations with vendors to analyze.

When faced with NLP I try to go back to the essence of the problem and work out what it is we are trying to solve. Is it that we humans tend to communicate in question form? Or is it that we tend to ask questions of our machine systems? In either case it’s a problem of understanding strong elements of information as well as the weak elements, then how they connect to other pieces of information that have strong and weak elements etc….i’m not sure questions in many cases are anything but the limited beginning of seeking answers within information. It’s how the information connects that’s critical and our ability to tap into the right connections.

3) And how well are we able to mine and automate understanding of affective states, of mood, emotion, attitudes, and intent, in the spectrum of sources available to us?

This one for me is a supply/demand driven question. I think we are much better able to do this today than 12 month ago and will be better again in 12 months from now. This is because there is a demand for this type of analysis and thus enough people out there are working to solve this unitary problem. Using all sorts of techniques, from unsupervised to supervised, from statistical to linguistic and anything in between.

Some sources are better suited to this analysis, say tweets or Facebook entries. Principally because they are short and to the point. Then we will have things like email that will pose a substantial problem as we use prose to characterize our needs, wants and desires with the full beauty of language.

Then we have the entire world of video and audio. Where even human beings might have difficulty identifying what are very personal states of expression which are very dependent on the individual. Yet, this is potentially the most interesting of media?

4) Deep learning, active learning, or maybe some form of machine learning that’s being cooked up in a research lab: What business benefits are delivered by these technologies, and what are the limits to their usefulness, technical or other?

As in the NLP question we need to go back to basics and never lose sight of the reason why we do things. Certainly as it relates to business and data. The central (and obvious) premise is that the information created by a company in all its forms represents an incredible asset to the any company. Yet clearly this information is serving a purpose already as it relates to the business. So for me business benefits in “mobilizing” this information further are in making sure that any piece of information fulfils its maximum potential. This is simple; when this information is needed it should be presented. A human being can then decide on whether to use or not. It’s about machine augmented intelligence.

There are limits. Technically speaking there is a cost to making all information available, businesses need to have a clear view on how this translates to value or won’t make the purchase. Then there is the fact that whatever technology one uses as we try to help the user in their daily lives we must take into account the fact that’s it’s down to the consumer to ultimately decide if the dots have been connected and this might be a very subjective operation. This speaks to perceived usefulness of this type of technology.

This is what we do for a living here at HP Autonomy. We connect the dots.

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 is your company keeping up?

Thankfully we put the emphasis on information and making sure we manage, understand and act on it generically. So mobile is great for us because of its greater use of information (in our case human information), and it makes some of our offerings even more relevant.

Interestingly I think mobile is turning every one of us into data scientists. Certainly data specialists and certainly data discerners. This is great because it pushes the industry to create tools for a more demanding user which in turn moves technology to new levels. This is exactly the point I make clumsily around NLP. Mobile will make NLP or question-answer systems take a leap. It’s down to the discerning consumer of the product.

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

For our business we see great opportunity in three areas (in no particular order of importance as they are all equal);

Firstly in supporting the chief marketing officer’s organization in any company in making better and more real-time decisions as they connect consumer and product. Using groundbreaking algorithms and our understanding of human information to automate what is at the moment a very manual process. This will mean companies can now consume more data about their users and mobilize more products yet be even more agile and certainly more successful.

Secondly we see incredible opportunity in a field that we already lead, which is the management and compliance of information to both regulated industries as well as for companies that look at governance of information proactively. Securing or protecting information as well as evaluating the risk in information and then being able to act suitably and in accordance with regulation and law is a considerable integration and synthesis challenge, but one which must be done 100% correctly. We continue to evolve our products to serve our customers in this respect better and better.

Finally I think that there is a definitive trend towards self-service and using tools in the cloud. So we are making a substantial bet in launching HP IDOL OnDemand. Where we are taking our core information platform and launching it as a developer friendly platform that any developer can create applications with in the cloud. The full richness of our platform is available to inquire, investigate and improve information to deliver information rich applications. This platform will shorten substantially the time it takes for any company to create an information rich application for their business but at the same time provide all the value of cloud in terms of costs and supportability. I can see that as customers experience that they can be more agile in creating valuable applications with HP IDOL OnDemand they will trend towards placing more and bigger bets in this platform.

Thank you to Fernando!

Click on the links that follow to read other Analytics, Semantics & Sense Q&A responses: Fernando LuciniMarie WallaceElliot Turner, and Stephen Pulman. And click here for the article I spun out from them, Metadata, Connection, and the Big Data Story.

Text Analytics 2014: Tom Anderson, Anderson Analytics and OdinText

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 –

Text Analytics 2014: Q&A with Tom Anderson, Anderson Analytics and OdinText

Tom H.C. Anderson

Tom H.C. Anderson, founder of Anderson Analytics and OdinText

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

Customers are starting to become a little more savvy than before which is something we really welcome. One of two things used to happen before, we either had to explain what text analytics was and what the value was or two, sometimes had to deal with a representative from purchasing who represented various departments all with different unrealistic and unnecessary expectations on their wish list. The latter especially is a recipe for disaster when selecting a text analytics vendor.

Now more often we are talking directly to a manager who oftentimes has used one of our competitors, and knows what they like and don’t like, has very real needs and wants to see a demo of how our software works. This more transparent approach is a win-win for both us and our clients.

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

I have several great ones, but perhaps my favorite this year was how Shell Oil/Jifffy Lube used OdinText to leverage data from three different databases and discover exactly how to drive profits higher : http://adage.com/article/dataworks/jiffy-lube-net-promoter-score-goose-sales/243046/

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

OdinText handles sentiment quite a bit differently than other companies. Without getting into that in detail, I will say that I’m pleased to see that one good thing has happened in regard to the discourse around sentiment. Specifically, vendors have stopped making sentiment accuracy claims, as they seem to have figured out what we have known for quite some time, that accuracy is unique to data source.

Therefore the claims you used to hear like “our software is 98% accurate” have stopped. This is refreshing. Now you are likely to only hear accuracy claims from academia, since usually they have very limited access to data and are less likely to work with custom data.

Equally important in the industry realizing that sentiment accuracy claims don’t make sense is the fact that even clients have started to realize that comparing human coding to text analytics is apples to oranges. Humans are not accurate, they are very limited, Text analytics is better, but also very different!

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?

We’ve been adding several new powerful features. What we’re struggling with is adding more functionality without making user interface more difficult. We’ll be rolling some of these out in early 2014.

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?

I know “real time analytics” is almost as popular buzzword as “big data”, but OdinText is meant for strategic and actionable insights. I joke with my clients when discussing various “real-time reporting” issues that (other than ad-hoc analysis of course) “if you are providing standard reports any more often than quarterly or at most monthly, then no one is going to take what you do very seriously”. I may say it as a joke, but of course it’s very true. Real-time analytics is an oxymoron.

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?

I do think there’s a lot more opportunity in monetizing data, one of the things we are looking at.

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

End of 2013 was better than expected, so very excited about 2014.

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

Text Analytics 2014: Sid Banerjee, Clarabridge

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 –

SidBanerjeeText Analytics 2014: Q&A with Sid Banerjee, Clarabridge

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

The market has seen a lot more competition by way of historically non-text analytics vendors adding various forms of text analytics solutions to their product mix.  In 2013 several survey companies added text and sentiment analytics capability.  Workforce Management vendors highlighted their customer experience analytics capabilities, many powered by text (and speech) analytics capabilities – which were, depending on the vendor, home grown, or licensed from pure play text companies.  And even social CRM, and social marketing vendors – whose primary focus until this year was social communication and marketing automation processes, started adding sentiment mining and text analytics capabilities into their product mix.  As a result the market got a bit more confusing from a vendor selection perspective.  Firms like Clarabridge have continued to tout “multichannel” customer experience intelligence and text/sentiment capabilities – partly because it’s always been our focus, but also to seek to differentiate from the new crop of mostly point solution providers of text analytics.  It’s likely that this trend of more point solutions focused on single source based analytics and deployment to departmental users, while enterprise providers focus more on multichannel analytics, and enterprise deployments, will continue in 2014.

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

A few.  An airline customer merged with a major airline and over the course of 2013 used text analytics (from surveys, social, and customer call centers) to ensure critical customer feedback was incorporated into the inevitable changes that occur when companies come together.  Feedback was used to figure out how to manage switching a customer base to a new brand of coffee with minimum disruption.  Feedback was used to identify which boarding processes (from the two airlines) was most acceptable to the passengers of the other airline.  And branding, support, frequent flyer programs, and many other processes were monitored and modified, as needed to ensure customer needs and wants were met.

My favorite story comes from a leading wireless vendor, who used Clarabridge during Hurricane Sandy. (while Sandy occurred in 2012, I learned about it in early 2013).  The carrier suffered extensive network outages along the Jersey Shore, and of course the outages, and general suffering from their customers who suffered displacement and devastation affected entire communities.  As the carrier was tracking outages, customer feedback, and general recovery efforts after the hurricane, they caught wind of a trending topic via social and other channels of customers wondering if they were going to be charged for the days and weeks their service was out.  Left unaddressed, the company realized they were likely to see a growing chorus of requests for credits flooding their call centers from unhappy and inconvenienced customers.  After consulting with the business owners across the region, the carrier decided to proactively notify all affected customers that if they were residents of areas incurring outages, their charges would be proactively suspended while reconstruction work was going on.  The positive impact on customer satisfaction was immediate, and the company averted a frustrating and distracting impact on its customer relationships.

Both stories highlight a consistent theme about the value of text analytics in the context of customer experience.  If you listen to your customers, you can find problems, you can fix things more proactively, you can avert cost and inconvenience for both you and your customers, and you can create a more loyal and and lasting relationship between you and your customers.   That’s really what it’s all about.

3) 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 a high level – expect to see the following:

More Big Data: we will support ever high data volumes

More Big Data Analytics: we will support more use of analytics to separate actionable data from non actionable data, to identify trends and insights, to make recommendations and predictions, and to suggest interactions and conversations between companies and customers.

More Uses:  In the past our customers have generally looked to Clarabridge insights and analytics, powered by text and sentiment analytics.  They will continue to see business value and application value in these areas, but our products have evolved in 2013 and will continue to evolve in 2014 to include more mobile deployability, more collaboration and alerting capability, and more capability to recommend and enable customer engagement.  Our solutions will increasingly be designed to support the specific usability and functionality requirements of key practitioners of customer experience analysis, customer engagement, and customer support.

4) 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?

We launched Clarabridge GO in fall 2013. With this application Android, iPhone, and iPad users can run reports, get alerts, view dashboards, collaborate with fellow employees, and directly engage with end customers, all from their mobile applications.  The application brings together social, survey and feedback content into a single mobile portal, alerting, and engagement framework.  We are seeing more and more of our customers looking for mobile access to insights and looking for the platform to engage and respond. Clarabridge GO is designed to package the Clarabridge capability for the mobile user.

5) 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?

More markets/languages – we will continue to internationalize our product for more markets, languages, and use cases.

Algorithms – we are continuing to invest in making our algorithms more scalable (to handle more volumes), more “intelligent” to provide more recommendation/action based findings, not just insights), and more accurate/useful (separating useful data from noise, ensuring the most accurate mappings and tagging are occurring as we process text and unstructured content), and more linked (connecting more data points from more disparate sources into integrated, linked insights across more and more customer interaction touch points.

Extending the core platform to more “uses” for more “users.” – lots of plans here – we will be announcing more in 2014.

More content types.  We intend to follow customer expression in whatever form factor it takes.  Customers increasingly are mixing media, structured, semistructured, unstructured.  We will continue to look for ways to follow customer conversations across media, and apply intelligence to structure, deduce, provide insights, and help make recommendations.

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

More partnerships – 2014 is the year I expect to see major productive partnerships developing between technology service, and support partners.  Companies are making major investments in institutionalizing customer experience across the enterprise, powered by customer insights extracted from unstructured customer interaction and feedback data.  To support the investments, expect to see the big Systems Integrators, Business Process Outsources (BPOs), marketing services companies, and technology vendors working more and more closely together to common cause – helping customers realize value to customer experience insights.  Making better products.  Creating positive customer experiences.  Marketing more relevant and successful campaigns. And more smartly managing customer relationships.  All aided by intelligent customer experience technologies like Clarabridge.

Thank you to Sid! Click on the links that follow to read other Text Analytics 2014 Q&A responses: Lexalytics CEO Jeff Catlin, Fiona McNeill of SAS, 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.

Text Analytics 2014: Fiona McNeill, SAS

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 –

Text 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.

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.