Attensity, NLP, and ‘Data Contextualization’

(Part 2 of an Attensity/text analytics update. Click to read part 1, Attensity Doubles Down: Finances and Management.)

Attensity ex-CEO Kirsten Bay’s LinkedIn profile states her Attensity objective as “develop go-to-market strategy to reorient corporate focus from social media, text analytics to corporate intelligence.” A shift of this sort — a step up the value ladder, from technology to solutions — seems sensible, yet Attensity has gone in a different direction. Since Bay’s December 2013 departure, the company has instead doubled-down on a technology-centered pitch, placing its positioning bet on algorithms rather than on business benefit.

We read on Attensity’s Web site — the first sentence under About Attensity (as of August 14, 2014) — “Our text analytics technologies use patented, state-of-the-art semantic approaches to extract and recall information into valuable insights.” Other main-page tag-lines: “Using semantic analysis to extract textual insights” and “Enabling customer insights for social and non-social data.” There are variations on this sort of language throughout Attensity’s marketing collateral.

A tech-centered pitch is great if you’re marketing to developers, to a market segment that knows it needs “semantic approaches.” A tech pitch may also appeal to insights professionals, to market researchers and consultants. But for a business exec who’s looking to boost customer satisfaction, engagement, and loyalty, for competitive advantage? Perhaps not so compelling.

In an article that preceded this one, I characterized Attensity’s May 2014, $90 million financing announcement as a doubling down, in both investment and technical positioning. The earlier article covered Attensity’s financial and management picture. This second one focuses on positioning and prospects, with a few words on Attensity Q, a new “easy-to-use real-time intelligence” solution for marketers.

Go Your Own Way

In my earlier article, I offered the impression that Attensity’s business performance, measured in financial and competitive terms, has been stagnant in recent years. Attensity’s Aeris Capital owners evidently agreed: Ex-CEO Kirsten Bay, in describing her Attensity assignment in her LinkedIn profile (as of the moment I’m writing this article) uses “restructure” and “recapitalize” twice each. Recapitalize? Done, although it’s unclear where the $90 million is going, or went. Restructure? I don’t know what steps have been taken beyond the hiring of a new marketing head, Angela Ausman, and the reversion to tech-centric market messaging. The company has declined to discuss its product roadmap.

Attensity’s message is certainly different from its nearest competitors’, who have had greater market and corporate success. Among business solution providers –

Medallia has invested heavily in text analytics, maintaining, however, a pitch built around business benefit: “understand and improve the customer experience.” Clarabridge repositioned several years ago as a customer experience management (CEM) solution provider; you won’t find “text analytics” on the main page of Clarabridge’s Web site. Kana (owned by interaction-analytics leader Verint) sells “customer service software solutions.” Confirmit bought text analytics provider Integrasco earlier this year, but eschews the “text analytics” label in favor of a functional description of the capability provided: “Discover insights in free-form content.” Pegasystems focuses on improving customer-critical business processes, with text analysis capabilities enhanced via the May 2014 MeshLabs acquisition but still playing a supporting role.

Attensity could do likewise: Contextualize its text analytics technology by repositioning as a business solutions provider. Attensity certainly does understand the importance of context, because “context” (along with “insights”) is the part of the company’s pitch that best bridges tech and business benefit.

Context and Sense

In some of its more-recent material, Attensity has termed itself “the leading provider of corporate insight solutions based on proprietary data contextualization.” See, for example, the Attensity Q announcement.

I asked Attensity its definition of “data contextualization,” but again, the company declined to take up my questions. So I’ll give you my definition: The notion that accurate data analysis accounts for provenance (identity, demographic and behavioral profile, and reliability of the source), channel (e.g., social media, surveys, online reviews, contact center), and circumstances (location, time, and activity prior, during, and after a data point) among other factors. There’s a word that describes data context — metadata — so what’s different is a dedication to better use it in analyses.

Nathan Shedroff: From data to wisdom via context.

Nathan Shedroff: From data to wisdom via context.

Authorities such as IBM’s Jeff Jonas have written (virtual) reams about context. See, for instance, Jonas’s G2 | Sensemaking -– Two Years Old Today. Other vendors have made the case for context. One pitch: “Digital Reasoning uses machine learning techniques to accumulate context and fill in the understanding gaps.” I’ll present to you an illustration that dates back two decades. It’s to the right, pulled from Nathan Shedroff’s 1994 Information Interaction Design: A Unified Field Theory of Design.

Gary Klein, Brian Moon, and Robert R. Hoffman wrote in 2006 about sensemaking embodied in intelligent systems that “process meaning in contextually relative ways,” that is, relative to the data consumer’s situation. “Data contextualization,” as I understand it, makes explicit an extension of Shedroff-type models into the data producers’ realm, to better power those sought-after intelligent systems. The concept isn’t new (per IBM, Digital Reasoning, SAS’ s Contextual Analysis categorization application, and other examples), even if the Attensity messaging/focus is.

How has Attensity implemented “data contextualization”? I don’t know details, but I do know that the foundation is natural language processing (NLP).

Attensity NLP Based Q

“The strongest and most accurate NLP technology” is Attensity advantage #1, according to marketing head Angela Ausman, quoted in a June Agency Post article, Attensity Q Uses NLP and Visualization to Surface Social Intelligence From the Noise. Attensity advantage points #2 and #3 are development experience and “deep breadth of knowledge in social and engagement analytical solutions,” setting the stage for introduction of Attensity Q. Attensity visualization dashboardAusman cites unique capabilities that include real-time results; alerting; “quotable metrics for volatility, sentiment, mentions, followers, and trend score”; and term-completion suggestions, via a visualization dashboard.

Attensity Q comes across as designed for ease-of-use and sophistication (which are not necessarily mutually exclusive categories) beyond the established Attensity Analyze tool’s. Attensity Pipeline‘s real-time social media data feed remains a differentiator, as do the NLP engine’s exhaustive extraction voice-tagging capabilities and the underlying parallelized Data Grid technology.

But none of this, except for Q, is new, so is any of it, Q included, enough to support a successful Attensity relaunch? The question requires context, which I’ve aimed to supply. Its answer depends on execution, and that’s CEO Howard Lau’s and colleagues’ responsibility. I wish them success.


Disclosure: Attensity sponsored 3 instances of a conference I organize, the Sentiment Analysis Symposium, most recently in the fall of 2012, and the company was a 2011 sponsor of my text analytics market study. A new version of that study is out, Text Analytics 2014, available for free download, sponsored by Digital Reasoning among others. And IBM’s jStart innovation team sponsored my 2014 sentiment symposium.

Attensity Doubles Down: Finances and Management

Attensity, founded in 2000, was early to market with text analytics solutions. The company’s “exhaustive extraction” capabilities, referring to the use of computational linguistics to identify entities and relationships in “unstructured” text, set a standard for commercial natural language processing (NLP). Rival Clarabridge, as a start-up, even licensed Attensity’s technology. Yet Attensity has struggled in recent years, reaching a possible low point with 2013’s less-than-a-year tenure of Kirsten Bay as CEO. And now, under Howard Lau, chairman (since early 2013) and CEO (with Bay’s December 2013 departure), and with $90 million in equity financing?

I would characterize the May 14, 2014 financing announcement as a doubling down, in both investment and technical positioning.

Attensity is worth a re-look, hence this article with a financial focus and another on positioning points that I’ll post soon. [Now posted, August 14, 2014, Attensity, NLP, and 'Data Contextualization'.] I hope to follow them in the fall with a look at innovations and the product roadmap.

A Doubling Down?

All Attensity will say about the $90 million transaction is that “Financing was provided by an international private equity fund and financial advisor company. The new capital secured will be used to accelerate product innovation; fuel market growth; and expand the sales, marketing, and engineering teams to meet the growing need for engagement and analytic applications using patented natural language processing (NLP) technology.”

I tried and failed to learn more. Marketing head Angela Ausman, who joined the company in April, declined to comment on questions I posed to her and CEO Howard Lau, regarding market positioning and growth markets, the competitive landscape, alliances, and the product roadmap. Lau has been unavailable for discussions.

“The company says it previously raised about $58 million,” according to May reporting in the San Francisco Business Times, and Attensity-acquired company Biz360 had itself raised about $50 million. I’m guessing the $58 million figure includes only investment in Attensity in its current form, that it discounts funding prior to 2009, the year Aeris Capital bought Attensity. Attensity no longer lists investors or much history on the company Web site. Early investors, surely since bought out, include In-Q-Tel, which led a 2002 $6 million round. I’d speculate that early owners did not fully recoup their investments.

Turn-arounds are tough.

I don’t know whether former CEO Kirsten Bay, who held the post from January to December 2013, chose to leave or was pushed out. Regardless, she may not necessarily have failed so much as not sufficiently succeeded, by her own or Attensity’s standards. When a company is losing customers, talent, and money (in order of importance) only radical restructuring or an asset sale will save the day.

My take is that Attensity’s troubles started under the longtime executive managers who preceded Bay. An industry-analyst friend offers the comment, “By the time they took Aeris money Attensity was already a dead enterprise. The money and support gave them a Death 2.0 runway.”

Attensity used the Aeris money to attempt to go big but was unable to make a go of the 2009 roll-up of Attensity and two German companies, Empolis and Living-e. Buying Biz360 in 2010 in order to get into social intelligence was a mistake. Social intelligence was at the time, and largely still is, a low-value, small(ish)-deal proposition that doesn’t pay unless you’re set up to do mass marketing, which Attensity wasn’t and isn’t. Attensity Respond goes beyond social intelligence to provide engagement capabilities.

I wonder whether the 2010 deal for real-time access to Twitter’s “firehose” has paid off, or will ever.

Also, Attensity had a failed foray in advanced analytics (data science), which probably wasted attention and opportunity more than it wasted money, but still a loss ill-afforded by the company. (Clarabridge pursued a similar predictive analytics initiative around the same time, in 2010 or so, working with open-source RapidMiner software, but didn’t invest much in the effort.)

Attensity Analyze

Attensity Analyze

So Attensity unrolled the 2009 roll-up, in particular shedding European professional services, but has maintained social listening (Pipeline) and engagement (Respond) capabilities, complemented by Attensity Analyze for multi-channel customer analytics. (I plan to write in another article about a new product, Attensity Q, and perhaps also about product technical underpinnings.)

Get On Up

Attensity business performance has seemingly been stagnant in recent years. The company has lost customers to rivals with no recent new wins that I know of. Attensity shed most senior staff. Everyone I knew personally at the company left within the last couple of years. Beth Beld, the chief marketing officer whom Kirsten Bay brought on in May 2013, stayed less than five months.

I do know that Bay met with potential Attensity acquirers, including two of my consulting clients, but none of them, evidently, saw promise sufficient to justify terms. If Dow Jones reporter Deborah Gage is correct, that the new funding comes from Aeris Capital AG, already Attensity majority owner, then the $90 million transaction truly does represent a doubling down, in a game with no other players.

The May funding announcement was a plus — call it a take-over from within — and I did receive a positive comment from a consultant friend: “We’re working with one of the Top 10 banks in the U.S. and they are migrating new lines of business from other tools over to Attensity.” Good new, but for Attensity to revive, we’re going to have to hear a whole lot more.


Click here for part 2, posted August 14, 2014, Attensity, NLP, and ‘Data Contextualization’.


Disclosure: Attensity sponsored 3 instances of a conference I organize, the Sentiment Analysis Symposium, most recently in the fall of 2012, and the company was a 2011 sponsor of my text analytics market study. A new version of that study is out, by the way, Text Analytics 2014, available for free download.

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.