Ensuring national security is often a matter of connecting the dots – of discovering and digging into the relationships between individuals (recent evidence: Osama bin Laden being tracked down through one of his couriers), and among people and organizations. Businesses might want to take a page from that playbook, finding within their own data and that of external sources such as social media unexpected relationships that can lead to new markets, clients, or even employee leads.
Data Intelligence Technologies is hoping to exploit the data relationship expertise it developed in the national security consulting arena — “building bad guy networks,” as founder and CEO James Kraemer puts it — to the commercial enterprise space (as well as continuing to serve the national security market). “On a high level we allow business intelligence where you get insight into data. All BI offers that,” Kraemer says. What sets this solution apart, he says, is supporting knowledge networks with targeting features that an organization can use to search, look for profiles, and discover additional relationships inside the data.
“But on a lower-level we are very interested in targeting,” – that is, in building profiles that match the organization’s business target, enriched by giving employees the ability to search, look for profiles, and discover additional relationships inside the data.
The Entity Analytical Platform emphasizes a Dynamic Data Model for enabling collaboration “around business data and relationships to create custom knowledge graphs [a combination of social/professional/business networks based around the customer’s business model]– Linked Data really,” he says. In contrast to running analytics in isolation for data ported over t o a data warehouse, tree, force, radial, circle, icicle and sunburst visualizations of data are designed to engage the user to explore relationships by hovering over entities, and showing a full model of their attributes.
The platform doesn’t itself tout semantic underpinning, but Kraemer says most of the concepts infusing it owe their existence to semantic technology: RDF triples denote relationships among entities; linkages between entity classes are defined through an ontology; transitive relationships and MoreLikeThis similarities enhance smart discovery.
“Here you can collaborate around the data and focus more on relationships and discovery of more relationships. That’s not something you usually see in business intelligence suites,” he says. “You don’t see a lot of them mapped on top of semantic relationships.”
Among some of the commercial options he describes for using the platform could be in marketing departments, where personnel pull in relationships based on their Google contact lists, Facebook Likes, and Twitter followers, and discover relationships about who likes what. Or recruiters could pull in LinkedIn Data to create a relationship graph to share among their team for collaborating around potential employment leads – including who else some of those leads might know that could be a fit for the job. Searches on the field level link to the Dynamic Data Models.
Organizations can source the Entity Analytical Platform for installation on site or in a hosted model. The company also expects to soon launch a free public site for collaborating around social networking data –visualizing, for instance, relationships among thousands of likes and the followers who select them.