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Fuzzy is Fine for Stuffed Animals, Not for Your Big Data!

Luca ScagliariniIn this special guest feature, Luca Scagliarini, CEO of Expert System USA discusses the importance of using semantic technology for the effective management of unstructured information. Luca provides the leadership for Expert System’s worldwide sales organization. Prior to Expert System, as CEO Luca cofounded and launched ADmantX, contributed to an impressive first-year growth at SiteSmith, which culminated in a sale to Metromedia Fiber Network, and has held leading positions at Hewlett Packard in the US and Italy. Luca holds an MBA from Santa Clara University and an Engineering degree from the Politecnico di Milano, Italy.

Whether in the form of premium news feeds, comments on social media, research field notes or text files on the company intranet, the amount and level of unstructured data available to any organization today is an important source of intelligence and also an ever-growing part of what constitutes big data.  Semantic technology, which allows for a deeper automatic understanding of text by working to quickly understand the meaning of words in context, has evolved to become a strategic opportunity for enterprises to capitalize on these data sources for a competitive advantage, with the added benefit of not disrupting existing technologies and systems.

From an information management point of view, semantics enables more efficiency and productivity. The time it would take for a human to read, digest, categorize and/or organize such massive amounts data with accuracy would be costly, not to mention the loss of time to act on that intelligence when it comes to making quick decisions tied to new products, market trends or customer issues.

From a strategic point of view, semantic technology helps unlock the hidden value of data, filtering out the useless from the relevant, helping companies channel it into actionable information that identifies patterns, predicts trends and fuels decision-making.

From a technology integration standpoint, there are a number of advantages made possible by tapping into the large amount of data housed in legacy decision making tools, allowing for not only better, more informed decision making, but also greater insight on customers and performance.  For organizations that want to maintain their competitive edge, or small, innovative players hoping to level the playing field, the opportunity to leverage all the information available is one that can’t be ignored.

Talking about the potential of this technology is one thing, but showing it is another.  Here are three use cases that demonstrate how semantic intelligence technology can turn massive amounts of “fuzzy” big data into a strategic asset:

Supplier and Partner Risk Analysts:  What if your supplier is in trouble? How would it impact your business and more importantly your customers? Uncovering and evaluating supplier risk using open source intelligence is one important use for content analysis technology.  Corporations, such as those in the Oil & Gas and Pharmaceutical industries, need to continuously and carefully evaluate their business partners and suppliers to minimize enterprise risk.  Proper evaluation is a critical process because business partners must have an untarnished legal and ethical profile and the ability to ensure continuity of service to its clients.

This means that a one-time, credit rating based evaluation is insufficient and what is needed is continuous monitoring so as to avoid:

  • Potential brand reputation damage (ie. suppliers with criminal ties or with corrupt management)
  • Repercussions on production (ie. suppliers who don’t pay their employees, environmental problems which may compromise the delivery of services, etc.).

In this case semantic intelligence technology, became effective in supporting the analysts involved in evaluating and monitoring business partners and suppliers.  The program was able to retrieve and monitor thousands of pieces of data contained in open sources, social networks and within specific information flows. Then it selected, ranked and highlighted any potentially critical information and turned it over for the analysts to use.   The target for analysis was quite vast and included owners, board members, and management as well as the supplying company itself (and all of its related partners and/or subsidiaries).

Customer Experience Intelligence – Driven by large amounts of information and a highly competitive regulatory environment, financial institutions must be able to utilize their information for quick decision making and calculating risks, all while ensuring the positive customer experience that is essential for long-term success.

A leading European banking and financial services organization chose to use semantic intelligence technology to help maximize use of structured and unstructured information for actionable intelligence, and to create a more intuitive customer navigation experience to support the increased use of online self-service applications.

Through the use of natural language processing, the institution was able to transform user feedback into actionable sentiment and intelligence, and it enabled more effective handling of online customer requests through a complete understanding of customer queries written in natural language, even where slang or abbreviations were used. With this solution, the tools were also in place to use their structured and unstructured information to combat fraud in transactions and more effectively target its products and services to customers.

By using semantic intelligence technology, the Bank was able to:

  • Improve the online customer experience while reducing call center requests by 46%.
  • Enhance identification of new customer needs, problems and expectations.
  • Achieve greater quantitative and qualitative detail on customer satisfaction levels.

Business Networking Intelligence: The last use case is a bit more eccentric, but uses open source data to cull personal data for better networking.  The CEO of a large corporation, who has a great number of business meetings to attend each month, wanted to find a way to gather information about the people he was meeting with. His aim was to build better interpersonal and professional relations because he believes that these will ultimately reap positive effects on his business.  Information regarding the professional life of top managers and politicians are usually easily found by conventional methods (ie. company websites, resumès, etc.). However, when open sources are analyzed, we can also find different kinds of information regarding someone’s personal interests.

In using semantic intelligence technology the CEO was armed with discussion topics and ideas for activities during “social time” so that he could ultimately create interpersonal bonds with his meeting participants.

While all the big data in your organization may at first seem fuzzy and overwhelming, it is important to realize the significant opportunities it holds to propel your organization, and even you, to new frontiers and possibilities, making it no longer the 800 pound fuzzy gorilla in the room.

 

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