Sophia, the provider of contextually aware enterprise search solutions, announced Sophia Search, a new search solution which uses a Semiotic-based linguistic model to identify intrinsic terms, phrases and relationships within unstructured content so that it can be recovered, consolidated and leveraged. Use of Sophia Search is designed to minimize compliance risk and reduce the cost of storing and managing enterprise information. Sophia Search is able to deliver a “three-dimensional” solution to discover, consolidate and optimize enterprise data, regardless of its data type or domain. Sophia Search helps organizations manage and analyze critical information by discovering the themes and intrinsic relationships behind their information, without taxonomies or ontologies, so that more relevant information may be discovered. By identifying both duplicates and near duplicates, Sophia Search allows organizations to effectively consolidate information and minimizing storage and management costs. Sophia Search features a patented Contextual Discovery Engine (CDE) which is based on the linguistic model of Semiotics, the science behind how humans understand the meaning of information in context. Sophia Search is available now to both customers and partners. Pricing starts at $30,000. http://www.sophiasearch.com/
Day: September 22, 2010
One of the conclusions of our report Smart Content in the Enterprise (forthcoming next week) is how a little bit of enrichment goes a long way. It’s important to build on your XML infrastructure, enrich your content a little bit (to the extent that your business environment is able to support), and expect to iterate over time.
Consider what happened at Citrix, reported in our case study Optimizing the Customer Experience at Citrix: Restructuring Documentation and Training for Web Delivery. The company had adopted DITA for structured publishing several years ago. Yet just repurposing the content in product manuals for print and electronic distribution, and publishing the same information as HTML and PDF documents, did not change the customer experience.
A few years ago, Citrix information specialists had a key insight: customers expected to find support information by googling the web. To be sure, there was a lot of content about various Citrix products out in cyberspace, but very little of it came directly from Citrix. Consequently the most popular solutions available via web-wide searching were not always reliable, and the detailed information from Citrix (buried in their own manuals) was rarely found.
What did Citrix do? Despite limited resources, the documentation group began to add search metadata to the product manuals. With DITA, there was already a predefined structure for topics, used to define sections, chapters, and manuals. Authors and editors could simply include additional tagged metadata that identified and classified the contents – and thus expose the information to Google and other web-wide search engines.
Nor was there a lot of time or many resources for up-front design and detailed analysis. To paraphrase a perceptive information architect we interviewed, “Getting started was a lot like throwing the stuff against a wall to see what sticks.” At first tags simply summarized existing chapter and section headings. Significantly, this was a good enough place to start.
Specifically, once Citrix was able to join the online conversation with its customers, it was also able to begin tracking popular search terms. Then over time and with successive product releases, the documentation group was able to add additional tagged metadata and provide ever more focused (and granular) content components.
What does this mean for developing smart content and leveraging the benefits of XML tagging? Certainly the more precise your content enrichment, the more findable your information is going to be. When considering the business benefits of search engine optimization, the quality of your tagging can always improve over time. But as a simple value proposition, getting started is the critical first step.
