The Brave New World of Text Analytics Applications

Attensity Corporation announced today that it is joining forces with two other text analytics/information management companies, Living e and Empolis, to form a new company called Attensity Group focused on creating business user applications on top of sophisticated data and semantic analytics technology.  According to Michelle de Haaff, CMO, the new combined company has software deployed in more than 500 installations worldwide and is in 250 of the Fortune 1000 companies.  


The combined company offerings include the following applications:


·        Voice of the Customer/Market Intelligence:  analysis of unstructured information from internal and external sources in order to understand customer sentiment, root cause and issues.  This helps to answer questions such as:  What are the top three complaints about my service?  How do I stack up against the competition?

·        E-service:  creates portals for online service content and includes guided search and escalation paths for both end customers and service agents.  This helps to answer questions such as: How do I change my password?  What workarounds have helped other customers? 

·        Automated Response Management: allows timely and accurate responses to incoming communications. This helps companies to answer inbound customer inquiries leveraging the same knowledge base used for E-Service.

·        Research and Discovery:  provides advanced search and classification of internal and external data, enabling early detection of issues in corporate and legal processes.  This helps to answer questions such as:  What pending legislation could affect our launch?  Is my patent unique?

·        Intelligence Analysis:  analyzes human intelligence and public intelligence for the purpose of identifying and preventing criminal activity.  This helps to answer question such as:  Who is the leader of this cell?  How is money being moved?


Attensity Group will also provide custom applications.  It will sell in the Americas under the banner: Attensity, An Attensity Group Company and in Europe under the banner: Empolis, An Attensity Group Company.



Does it make sense?


In a nutshell, the combination of these companies elevates Attensity Group out of niche application provider status to $40M to $50M mid-sized company.  The combined company is backed by Aeris Holdings.


Is this a viable move?   Attensity Group claims that companies want applications, not platforms that they don’t know what to do with.  I think that the move makes sense because companies do want specific, straight-forward solutions to their problems.  Look at how popular the SaaS model has been for Voice of the Customer solutions that utilize text analytics.  And, the traction that Attensity has gotten with its VoC solutions.  Additionally, small companies have a hard time making it as platform providers.  Text Analytics platforms are best left to substantially larger companies such as IBM or EMC that offer text analytics in conjunction with platforms (or as part of it).  These companies have built a sustainable ecosystem around their platforms.   


It also doesn’t hurt that Attensity Group has some solid legs to stand on. 

IBM Business Analytics and Optimization – The Dawn of New Era

I attended the IBM Business Analytics and Optimization (BAO) briefing yesterday at the IBM Research facility in Hawthorne, NY.   At the meeting, IBM executives from Software, Global Business Services, and Research (yes, Research) announced its new consulting organization, which will be led by Fred Balboni.   The initiative includes 4000 GBS consultants working together with the Software Group and Research to deliver solutions to customers dedicated to advanced business analytics and business optimization. The initiative builds off of IBM’s Smarter Planet . 


IBM believes that there is a great opportunity for companies that can take all of the information they are being inundated with and use it effectively.  According to IBM (based on a recent study), only 13% of companies are utilizing analytics to their advantage.  The business drivers behind the new practice include the fact that companies are being pressured to make decisions smarter and faster.  Optimization is key as well as the ability for organizations to become more predictive.  In fact, the word predictive was used a lot yesterday. 


According to IBM, with an instrumented data explosion, powerful software will be needed to manage this information, analyze it, and act on it.  This goes beyond business intelligence and business process management, to what IBM terms business analytics and optimization.  BAO operationalizes this information via advanced analytics and optimization.  This means that advanced analytics operating on lots of data will be part of solutions that are sold to customers.  BAO will go to market with industry specific applications


‘Been doing this for years


IBM was quick to point out that they have been delivering solutions like this to customers for a number of years Here are a few examples:


·        The Sentinel Group , an organization that provides healthcare anti-fraud and abuse services, uses IBM software and advanced analytics to predict insurance fraud.

·        The Fire Department of New York is using IBM software and advanced analytics to “ build a state of the art system for collecting and sharing data in real-time that can potentially prevent fires and protect firefighters and other first responders when a fire occurs”.

·        The Operational Risk data exchange (ORX) is using IBM to help its 35 member banks better analyze operational loss data from across the banking industry.  This work is being done in conjunction with IBM Research.


These solutions were built in conjunction with the members of IBM Research who have been pioneering new techniques for analyzing data.  This is a group of 200 mathematicians and other quantitative scientists.  In fact, according to IBM, IBM research has been part of a very large number of client engagements.  A few years back, the company formalized the bridge between GBS and Research via the Center for Business Optimization.  The new consulting organization is yet a further outgrowth of this. 


The Dawn of a New Era


The new organization will provide consulting services in the following areas:

·        Strategy

·        Biz Intelligence and Business Performance Management

·        Advanced Analytics and Optimization

·        Enterprise info management

·        Enterprise Content management


It was significant that the meeting was held at the Research Labs.  We lunched with researchers, met with Brenda Dietrich, VP of Research, and saw a number of solution demos that utilized intellectual property from Research.  IBM believes that its research strength will help to differentiate it from competitors.


The research organization is doing some interesting work in many areas of data analysis including mining blogs, sentiment analysis, and machine learning and predictive analysis.  While there are researchers on the team that are more traditional and measure success based on how many papers they publish, there are a large number that get excited about solving real problems for real customers.   Brenda Dietrich requires that each lab participate in real-world work. 


Look, I get excited about business analytics, it’s in my blood.  I agree that world of data is changing and companies that make the most effective use of information will come out ahead. I’ve been saying this for years.   I’m glad that IBM is taking the bull by the horns.  I like that Research is involved. 


It will be interesting to see how effectively IBM can take its IP and reuse it and make it scale across different customers in different industries in order to solve complex problems.  According to IBM, once a specific piece of IP is used several times, they can effectively make it work across other solutions.  On a side note, it will also be interesting to see how this IP might make its way into the Cognos Platform.  That is not the thrust of this announcement (which is more GBS centric), but is worth mentioning.  


Redefining Innovation?

Can people learn to innovate?  It may depend on how you define innovation.  When most people think of innovation, they think invention – like developing a post-it note. But, innovation can be something different.  Innovation can be as simple as using something old in a new way or as complex as inventing something… well really complex.  In the technology world, innovation can be about taking a product to market and extending its life.  Or, about finding new markets for technology that you already have.  



The folks at Invention Machine believe that you can learn to innovate, regardless of the type of innovation.  In fact, they believe that innovation can be injected everywhere in the product life cycle – from planning and research to design to preventing and fixing defects.  The goal is to make innovation “repeatable and sustainable”.  They have crafted an interesting solution that blends best practices in innovation with text analytics software so that the software can actually act as a subject matter expert. 


Best practices in innovation include text analytics


How does it work?  The solution- called Goldfire- provides an innovation framework with a semantic engine that allows you to mine internal documents, patents, and external literature to find answers to questions.  It supports the following innovation categories:


·        Analyze a market: enables the user to cull through the literature, extracting relevant information in order to understand a technology better

·        Develop a Product:  enables the user to design a new or hybrid system, providing best practices frameworks for this along with the ability to analyze patents and other documents relevant to the functionality of the product.

·        Improve a System: enables users to diagnose and fix a problem and resolve contradictions in an existing system. 

·        Risk Management: provides predictive failure analysis

·        Leverage Intellectual property: enables users to cull through patents


Right now, the company is focused on the manufacturing sector only.  For example, assume you are interested in developing a new product.   You would log into the Invention Machine solution and select the task (on the left hand tool bar) design a system. You would see some steps involved with designing a new system.  These are illustrated in the diagram below.


Here, you can see some best practices concerning what is involved in designing a system and then the steps you would take to do this.  One step is to explore the opportunity space.  The company uses Natural Language Processing techniques to rank concept retrieval requests relative to the question being asked. For example, say you’re interested in designing a new coffee maker and you need a component to heat water.   If you ask, “What heats water?” the result “The anode heats water” would be ranked higher than “Water is heated on a stove” because semantically, the first answer is more accurate.  Note that here the user is interested in the function of heating water and the anode is a component that can help to do this.  The text analytics engine will make use of selective substations for accurate extended concept retrieval.  Selective means only the words that are precise substitutes are used for automatic rephrasing.



The order of words makes a difference in what would come back in a search query.  Using the coffee maker example, assume the user is interested in getting power to the switch on the machine.  If the user asks, “How to power the switch?” and “How to switch the power?” the software recognizes the difference in concepts expressed by the different word orders.  This is because the semantic approach used by Invention Machine looks at the word roles and the relationships implied by the structure of language rather than a simple keyword or Bayesian approach.    


Can you learn to innovate?


I have to say that I have not seen anything quite like this before.  I’ve seen text analytics used in R&D to search the literature and patents, but not wrapped in best practices. 

Invention Machine’s semantic approach is interesting because it helps users focus on components and functions and, at one level, this is a part of what innovation is all about.  You could argue that components and functions are relevant to any industry.  If I am building something or even delivering a service, I am interested in what components I would need to meet a specific function.  Right now Invention Machines is focused on manufacturing, but you could certainly see how the application could be extended to other domains. 


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