Posts Tagged ‘unstructured text’

Big Data Review: What The Surveys Say

bd2Big Data has been getting its fair share of commentary over the last couple months. Surveys from multiple sources have commented on trends and expectations. The Semantic Web Blog provides some highlights here:

  • From Accenture Anayltics’s new Big Success With Big Data report: There remain some gaps in what constitutes Big Data for respondents to its survey: Just 43 percent, for instance, classify unstructured data as part of the package. That option included open text, video and voice. Those are gaps that could be filled leveraging technologies such as machine learning, speech recognition and natural language understanding, but they won’t be unless executives make these sources a focus of Big Data initiatives to start with.
  • From Teradata’s new survey on Big Data Analytics in the UK, France and Germany: Close to 50 percent of respondents in the latter two countries are using three or more data types (from sources ranging from social media, to video, to web blogs, to call center notes, to audio files and the Internet of Things) in their efforts, compared to just 20 percent in the UK.  A much higher percentage of UK businesses (51 percent) are currently using just a single type of new data, such as video data, compared with France and Germany, where only 21 percent are limiting themselves to one type of new data, it notes. Forty-four percent of execs in Germany and 35 percent in France point social media as the source of the new data. About one-third of respondents in each of those countries are investigating video, as well.

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Spiderbook’s SpiderGraph: Linking Datasets To Help You Sell Better

spiderpix1Startup Spiderbook, which is building a linked dataset of companies and their partners, customers, suppliers, and people involved in those deals, has recently closed its seed round for $1 million. The next-generation sales intelligence company was co-founded by CEO Alan Fletcher, who was a vp of product engineering, IT and operations at Oracle, and Aman Naimat, who has been working in the realm of CRM software since he was 19 years old and also has a background in natural language processing. Along with other core members of the team, the company puts natural language processing and machine learning technology to work to help sales people better connect the dots that explain business relationships, extracting information from unstructured text to sell more effectively.

State-of-the-art CRM, says Naimat, by itself doesn’t help salespeople sell. Since the days of Salesforce, which he worked on at IBM and Oracle, it has remained the same thing, he says, “just evolving with better technology. But basically it is an internal-facing administration tool to give management visibility, not to help a salesperson sell or create business relationships.”

Built from billions of data elements extracted from everything from SEC filings to press releases to blogs to Facebook posts, Spiderbook’s SpiderGraph is taking on that challenge, starting with the goal of helping salespeople understand who is the right contact to talk to, how he or she can meet that person (through shared contacts, for instance), and who competitors are, including those providing technology or other products already in use at the company. “We have created a graph of customers, competition, and suppliers for every company that is all interconnected,” he says.

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New Report May Help You Pick Your Text Analytics Vendor

A new report from Hurwitz & Associates seeks to put text analytics vendors in context. In an environment where unstructured text accounts for 80 percent of the data available to companies, the market analyst and research firm has prepared a Victory Index to help companies suss out who can best help them get value from this information.

By providing the ability to analyze unstructured text, extract relevant information, and transform it into structured information, “text analytics has become a key component of a highly competitive company’s analytics arsenal,” write report authors Fern Halper, partner and principal analyst; Marcia Kaufman, COO and principal analyst; and Daniel Kirsh, senior analyst. Often, the research firm notes, companies begin to experiment with text analytics to gain insight into the unstructured text that abounds in social media, and from that move on to other use cases. For instance, they’ll discover value in mining unstructured data and using it with structured data to improve predictive models.

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