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AttentionStream™: The Attensa Advantage

Mining Behavioral Attention Data to Improve Collaboration

In the 1600's, the amount of written information absorbed in an average person's lifetime was the equivalent of today's daily edition of the New York Times. Today we are confronted on a daily basis with more information than we can possibly process, and in an age of information overload our Attention is the most precious of all resources. By identifying, organizing and sharing the information that we selectively choose to enter our awareness and draw our focus - the things that capture our attention - we can streamline the process used to build collective wisdom. New software tools are emerging designed to help cut through information overload and streamline collaborative efforts by intelligently watching and analyzing Attention Data, the explicit and implicit behaviors knowledge workers use to identify and process valuable information that captures their attention.

Much of the work involved in searching, retrieving, analyzing and sharing information with project teams involves spinning wheels and wasting time. The process that each of us uses to find, sift, analyze, shape and share relevant information is mysterious and unique to our individual thought processes and work habits. As knowledge workers, we are constantly reinventing the information wheel because we don't have an effective way of accessing and benefiting from the results found by others. In many cases projects are bogged down with duplication of effort or needless weeding through extraneous, distracting information that is outside the pertinent bounds of the project.

Having tools that facilitate information sharing is paramount to streamlining the communication and workflows companies are struggling with everyday. New software tools using behavioral analysis are being developed that notice all of the steps we use to gather and process information.

These tools can help us gain insight into how users effectively convert information into wisdom by mining the Attention Data that is created as users gather information in their quest to answer questions and solve problems. This information typically answers questions about "who, what, when and where." Information becomes knowledge when patterns and perceptions emerge from the collected information that explain the "how." Wisdom is the end product that comes through understanding of the "why" and leads to action.

Software tools are being developed to mine the Attention Data created as users search, discard, retrieve, categorize and analyze content. By aggregating, triangulating and filtering Attention Data information can be prioritized and recommended in order to drive higher value content to the forefront while cutting down on useless and duplicate information. Effectively using Attention Data has the potential to create enormous cost savings by increasing the value and efficiency of information flow in the corporate environment.

Identifying Explicit and Implicit Attention Data through Context, Organization and Action

There are many explicit and implicit actions knowledge workers use to highlight information which has captured their attention. These behaviors can be overt or subconscious and generally fall into three buckets: the context of the information sought, the organization of the information retrieved and the action required to share the information following analysis.

Some examples of explicit contextual attention data include:

• Keywords used in search Web searching
• Keywords used to tag Web pages, blog posts and RSS articles
• Web pages and blog posts bookmarked
• Rating articles to indicate the value of the information
• Adding a new RSS news feed subscription
• Deleting irrelevant news feed subscriptions

Looking at how relevant information is organized is critical. Since tagging and bookmarking are pure expressions of attention, identifying the categories and clusters used to organize content gives structure to relevant information.

Analyzing more subtle implicit behaviors can enrich the Attention Data and provide deeper insights into the value of information.

These implicit indicators can include:

• The dimension of time: Noting what isn't being read can be just as important as the content a user examines. How much time is being devoted to reading specific content? Which content is deleted instantly? In addition, article length needs to be considered in the analysis. Spending 5 seconds on a 15 word article means something completely different from spending 10 seconds on a 2,000 word article.
• Prioritization: Which RSS articles, documents, reports, emails and other content are being read first? What links are followed? What content is simply ignored?
• Access: As knowledge workers adopt multiple communications devices, analyzing where they chose to have information available gives insight into the value they place on the incoming information.

Other key indicators of the value of information are the actions taken to share relevant information with project team members and management. The information can be forwarded in an email, a co-worker can be pointed to a significant URL, or a blog or wiki post can be created to share the information with interested parties. The assumption can be made that only the most valuable information is shared.

Using Behavioral Attention Data

Once all of this behavioral information is collected and analyzed, there are a number of things that can be done with it to extract its inherent value. It is important to realize that all of the attention data -- both explicit and implicit -- must be taken into account. As the technology surrounding Attention Data evolves, knowledge workers will be connected with the right tools to effectively gather and it. Email systems, RSS readers and aggregators, collaborative publishing tools including blogs and wikis, will be connected to a secure back-end infrastructure that ties users together so entire communities can benefit from aggregating, triangulating and filtering Attention Data.