Better Analytics

Improve your existing structured data analytics infrastructure by expanding it to incorporate unstructured data - resulting in deeper insight into your Big Data dilemma.

Using fact and sentiment extraction the analysis can be fed into downstream content management systems, search engines and data stores, to create content flow and product reports, drive information governance processes, and provide support to applications that rely on extracting data from text.

In most organizations today business intelligence has focused on the 20% of information that is structured data – and this has provided many strategic and operational insights about what has happened or what is going on. While the structured data reveals what happened, the unstructured data goes on to reveal why it happened.

Smartlogic Content Intelligence provides the ability to augment the existing analytics infrastructure – expanding it into the unstructured content domain that provides deeper insight into why things are happening.

Big Data

Turning attention to harnessing the value of content-based information in new ways adds a further dimension of value to Content Intelligence. Gartner stated that 80% of an organization’s information is contained in content; today that information is beyond the data horizon from an analytics perspective. But Content Intelligence can bring that information into view - unifying unstructured with structured data under a consistent set of semantics, allowing the organization to discover new insights, develop competitive strategies and solve problems more effectively. 

Big data has infrastructure requirements for distributed storage, massive parallel processing and collection and collation of a vast array of formats. Ultimately, the goal of big data is to enable analysis and decision making from a distillation of this massive data volume, velocity and variety in near real time.

Semaphore, Smartlogic’s Content Intelligence platform, is a key enabler for this complex decision support task. Our focus is bringing structure to the unstructured (variety) and being able to scale to enterprise requirements and beyond (velocity).

Learn more about Big Data analytics

Saving Time and Money

Ontologies are used to create an atlas of the content and data that exists within the enterprise and that is accessible beyond it. The ontology describes the data, the vocabulary it uses and how to map it. This is a far more dynamic and cost effective approach compared to traditional Extract-Transform-Load (ETL) methods.

Semaphore analyzes the data and content itself to normalize data and to extract facts from text.

Examples of facts extracted from text could be:

  • Products that failed, the reason for failure, product age and circumstances drawn from warranty reports and trouble tickets.
  • Products that customers discuss and the opinions stated drawn from call center reports or online social media.
  • Equipment failures including symptoms, error codes, environment and diagnosis drawn from service reports.
  • Medications taken including their dosage, frequency, side effects and outcomes from clinical trials or medical notes.
  • Acquisition deals including deal size, date, target, acquirer, regulator and advisor from news articles.
  • Well locations, drilling rights, term and fees paid drawn from legal contracts.

The mapped data and extracted facts are made available as structured data to analytics platforms such as Tableau so that users can explore and graph the data. The ontology gives users a powerful way to explore the relationships in the ontology to find new connections in the content.

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