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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

10/18
2010

How Analytics Can Improve Collaboration Behavior

Larry Hawes How Analytics Can Improve Collaboration BehaviorThis is a guest blog post by Larry Hawes (@lehawes), Principal of Dow Brook Advisory Services and Lead Analyst, Collaboration & Enterprise Social Software at Gilbane Group. Larry’s research and consulting work is focused on collaboration and knowledge management practices enabled by enterprise social, unified communication and collaboration, enterprise portal, document and content management, business process management, and learning technologies.

Enterprises deploy collaboration and social software to allow employees to more effectively address business problems and identify business opportunities.  This software helps users find content from which they can learn and locate other people with whom they can collaborate. Success will depend on how well the application enables users to find what they are seeking, so application designers and administrators must understand which user experiences create successful outcomes–and which lead to failure.

Designers and developers can use analytics to find out more about user experience in these applications. Steps could include:

1.      Determining the usage frequency of specific tools (for example, wikis, blogs, discussion forums, etc.) within multi-functional collaboration applications and suites

2.      Tracking the individual actions (such as clicks and searches) taken to find relevant documents and people

3.      Documenting the sequences of specific actions taken during a collaborative efforts

4.      Determining whether there are patterns in resource use based on the user’s position or role in the organization

5.      Gathering user-generated ratings on the relevance and helpfulness of discovered content and individuals

Analysis of frequency will show which tools users find most effective for surfacing knowledge and enabling collaboration. Information about the individual actions taken while using those tools (such as clicks made and search terms used), can lead to a better understanding of how effective particular tools are in helping an individual find the knowledge they need.

Examination of collaboration activity sequences reveals specific patterns of actions that lead to successful collaboration outcomes; application designers can build those patterns into the application to maximize successful knowledge transfer and collaboration.  Along the same lines, analyzing user-generated data (such as tags, ratings, and rankings) in relation to an individual’s job or role in the organization can lead to a better understanding of what content and expertise a particular type of user is seeking. Application administrators can then present the most relevant content and people to a specific user, so the individual does not have to navigate or search to find it.

This model of analytics-driven change can take on added power through the use of real-time analytics to dynamically alter the user experience. By observing and analyzing an individual’s current actions and work role, and comparing that to historical data, systems can automatically suggest not only valuable content and people that have knowledge, but also tools that will help the user actively collaborate with others to accomplish a task.

Social analytics is still relatively young, compared to business intelligence activities that focus on structured data residing in enterprise systems. However, the basics of data analysis apply just as well to collaboration as to other types of business activity. In collaboration, as in financial transactions, post-usage analysis provides insight, but dynamic analysis–when executed and applied correctly—can add even more value.

Image Credit: Courtesy of Larry Hawes

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