The Fit of Data Visualization Design for Knowledge Activation
Presentation Type
Event
Student
Yes
Track
Other
Abstract
Big data provides insight into products, services, business processes and management control activities. The ability to leverage data for actionable knowledge is the key to gaining and maintaining a competitive advantage. More is not always better; as big data is not easily processed manually, there is an increased reliance on computational tools and technologies to extend, partner, supplement and support human cognitive abilities. The combination of human intelligence with technological capabilities is the answer to make smarter decisions in the face of uncertainty and complexity. As the volume of data increases so does the need for new and improved analytical tools facilitating interactions between humans and technology (Bumblauskas, Nold, Bumblauskas, & Igou, 2017; Davenport et al., 2012). The power of visualization comes from coupling soft system attributes (perceptive skills, cognitive reasoning, and domain knowledge) with hard system attributes (computing and data storage). The design of visualizations is often separated from the user, resulting in misinterpretation and leading to error-prone decision-making (Few, 2006). Obtaining a better understanding of what it means for a human to see and think will help in developing more effective knowledge management tools for handling big data.
This research proposes a model for determining the fit of data visualizations grounded in the theories of situational awareness and task-technology fit. The model identifies design attributes that facilitate the task of sensemaking as a mediating factor to knowledge activation. Knowledge activation will only occur if a fit exists between task, technology, and individual.
Start Date
2-5-2019 3:30 PM
End Date
2-5-2019 4:30 PM
The Fit of Data Visualization Design for Knowledge Activation
Pheasant Room 253 A/B
Big data provides insight into products, services, business processes and management control activities. The ability to leverage data for actionable knowledge is the key to gaining and maintaining a competitive advantage. More is not always better; as big data is not easily processed manually, there is an increased reliance on computational tools and technologies to extend, partner, supplement and support human cognitive abilities. The combination of human intelligence with technological capabilities is the answer to make smarter decisions in the face of uncertainty and complexity. As the volume of data increases so does the need for new and improved analytical tools facilitating interactions between humans and technology (Bumblauskas, Nold, Bumblauskas, & Igou, 2017; Davenport et al., 2012). The power of visualization comes from coupling soft system attributes (perceptive skills, cognitive reasoning, and domain knowledge) with hard system attributes (computing and data storage). The design of visualizations is often separated from the user, resulting in misinterpretation and leading to error-prone decision-making (Few, 2006). Obtaining a better understanding of what it means for a human to see and think will help in developing more effective knowledge management tools for handling big data.
This research proposes a model for determining the fit of data visualizations grounded in the theories of situational awareness and task-technology fit. The model identifies design attributes that facilitate the task of sensemaking as a mediating factor to knowledge activation. Knowledge activation will only occur if a fit exists between task, technology, and individual.