Application Envisioning idea
Examples from three knowledge work domains:
(Illustrated above) An architect views a table in her building modeling application that lists all of the named interior features within a building design. She then filters the list to view only those items that have changes pending approval, and the 3D building representation “lights up” with colors that correspond to different approval states.
Adopting computing tools to organize and store information in knowledge work can remove useful cues and context. With so much information carrying a similar, default visual weight (C8), onscreen aggregations of content may seem somehow “flat” and overwhelming.
A financial trader filters a shared table of today’s trades so that it only shows his transactions. He then sorts the table by dollar value to get a general sense for his cash volume.
A scientist transforms a visualization of clinical data in her analysis application to show only data points from one group of subjects. Data belonging to subjects in the study’s other four experimental conditions disappear from view, revealing an interesting visual trend.
Product teams can envision functionality concepts for reordering large volumes of content, highlighting specific items within a content pool, or excluding information that does not match criteria relevant to workers’ current goals (A6, A7, A8). Options and categories for sorting and filtering concepts can arise either from the specifics of targeted work practices (A) or from novel uses of available data (B). Thinking holistically (C4), teams can envision each of these view manipulation methods (F8, F9) as individual operations and tasks within larger progressions of information seeking behaviors (F4, G1), which may also involve search functionality (I2).
When product teams do not actively consider the potential role of sorting and filtering in their application concepts, opportunities to support workers’ needs for isolating and understanding subsets of information can be lost. Individuals may find manually scanning though volumes of data to be excessively effortful (D2, D3), especially when they are familiar with the potential value of sorting and filtering options. Key categories of content may be more difficult to identify, locate, assess, and select (G2), potentially leading workers to incorporate less relevant information into their work outcomes
(G3, K5, L1).
Conversely, without appropriate feedback cues (D6, F10), filtering and sorting functionality can lead to errors when users do not recognize or remember that these options are currently being applied to their views of a data set (C9, E5).
See also: E3, F, H, I
Application Envisioning questions:
More specific questions for product teams to consider:
Outside of using search options, what approaches do targeted individuals currently employ to narrow in on information within the work practices that your team is striving to mediate?
How do knowledge workers currently reorder large amounts of content in order to meet certain goals?
How might the adoption of new computing options into targeted work practices create volumes of information where some type of filtering and sorting functionality could be useful?
What expectations for reordering, highlighting, or excluding information have targeted individuals developed from using other computing tools?
What larger technology trends and advanced analogies to other domains could valuably inform your team’s ideation around relevant filtering and sorting functionalities?
What inherent data attributes, such as the characteristics of interaction objects, could become useful facets for filtering and sorting of information displays?
Which of your sketched functionality ideas could benefit from specialized, contextually tailored options for sifting through and rearranging data?
What filtering and sorting options could become standards across multiple functional areas within your application concepts?
What novel interactions might your team envision to allow users to filter and sort the content of your sketched information representations? How could these methods relate to other visualizations and view transformations that you have envisioned?
How might powerful filtering and sorting options break common ground between workers or otherwise influence collaborative practices?
Do you have enough information to usefully answer these and other envisioning questions? What additional research, problem space models, and design concepting could valuably inform your team’s application envisioning efforts?
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