New Knowledge Management and Collaboration in Intelligence

Social, semantic technologies will change the game.

The statement “knowledge is power” has never been more relevant. Knowledge is what powers the mission. It’s what helps analysts do their jobs. And it’s the lifeblood of the intelligence community.

However, today’s intel agencies face high turnover, and losing people means losing knowledge. It is often a lengthy process to fill these vacancies. But even for those agencies not plagued by staffing shortages, there are issues of collaboration. Knowledge often sits in disparate silos or is buried in unique documents. These disconnects make it nearly impossible to locate information, identify a topic’s expert or discover synergies.

With the help of social, semantic technologies, intelligence agencies can connect people to people—and people to content—to unleash the full power of knowledge.

Social, semantic technologies are fueling a new era of collaboration. For many years, agencies have implemented enterprise-wide systems to tap into, extract and package employee intelligence. Yet many of these efforts have failed to meet expectations.

Knowledge management cannot keep pace with the speed at which the unprecedented volume of today’s information flows. This makes it challenging for organizations to effectively and rapidly provide new and current employees with the institutional or historical knowledge to effectively contribute to the organization’s mission.

Traditional knowledge management tools isolate information on “islands” with no connections. Today, social collaboration tools make new connections possible. By investing in the next generation of knowledge management and going a step beyond connecting people to people, agencies can connect people to knowledge and knowledge to knowledge, allowing agencies to collaborate more effectively.

The journey begins by liberating knowledge. Initial steps to get there include:

• Creating a knowledge “hub”—By gathering, organizing and connecting tacit and disparate knowledge in a central and synchronized architecture, information-sharing is not only possible, but encouraged.
  
  
• Cataloguing more strategically—By organizing knowledge around topics, it is no longer captured inside of documents, making it easier to locate and share specific and targeted information.
  
  
• Encouraging teamwork—When knowledge workers are free to contribute to content, all can benefit from the more robust knowledge stores.
  
  

Even with the growing popularity of social networking technologies or enterprise wikis, organizations continue to struggle with motivating employees to fully embrace and adopt such efforts. Specifically:

• As knowledge must be manually gathered and entered, it is cumbersome and difficult to get knowledge into traditional knowledge stores, databases or other enterprises.
  
  
• After content articles or pages are populated, users must manually connect and create links between such content and categorize it appropriately to ensure they can easily navigate between knowledge topics.
  
  
• Using a traditional knowledge management system or modern enterprise is usually seen as above and beyond—or least as lower priority than—the normal responsibilities of an agency employee, requiring contribution of their tacit knowledge outside their regular duties.
  
  
• It is difficult to get information out of many knowledge management systems— including traditional wikis—as the content is almost entirely unstructured, limited to keyword searches and does not have robust collaboration tools to truly connect people to people across the organization.
  
  
• A “catch 22” keeps adoption typically low for knowledge management tools—users need to contribute content for it to be useful, but those who see an empty knowledge base will likely never return.
  
  

Applications built on social, semantic technologies can help agencies overcome these challenges and tap a deeper level of intelligence.

It requires a significant amount of labor to manually maintain proper linkage to and from associated topics. Although the Wikipedia phenomenon has illustrated the productivity power of seamlessly navigating from topic to topic, information extraction and natural language processing systems are not mature enough to automate the manual process of maintaining a wiki.

Agencies need to break through the clutter and get to relevant information. To help them achieve this, Accenture created a social semantic knowledge platform to connect people to content; connect people to people; and facilitate useful content creation. This innovation combines four key features:

• Wiki—Enhances topic-centric knowledge management and collaborative content creation.
  
  
• Social—Includes rich user profiles, discussion forums, personal spaces, activity feeds and status updates.
  
  
• Semantic—Includes faceted search tools, visual navigation, temporal analysis and a rules-based inference engine.
  
  
• Text mining—Includes document ingestion, automatic link identification, automatic page categorization, all crowd-sourced for maintenance efficiency and improved accuracy.