The Zen. Chris Stolte. Vice President, Engineering & co-founder

Transcription

1 The Zen of Visual Analysis Chris Stolte Vice President, Engineering & co-founder

2 How do people reason about data?

3 The Cycle of Visual Analysis

4 2008 Presidential Election Campaign Expenditures

5 Not just Aha!

6 advice: Iterate, explore, p and experiment regardless of task

7 How can products support being one with your data and how can I use them effectively?

8 How can products support being one with your data and how can I use them effectively? Support the Cycle of Visual Analysis

9 Supporting the Cycle of Visual Analysis Incremental Expressive Unified Direct

10 Supporting the Cycle of Visual Analysis : Incremental Allow people to easily and incrementally change the data they are looking at and how they are looking at it click click

11 advice: Start simple

12 Supporting the Cycle of Visual Analysis : Expressive There is no single view for all tasks and all data

13 Supporting the Cycle of Visual Analysis : Expressive

14 advice: Reverse your thinking start with the data

15 Supporting the Cycle of Visual Analysis : Unification with the database Query many times and then generate a summary graph. TraditionalReporting Tools versus Query once and then iterate on the presentation of the data. Traditional Visualization Tools Iteratively change the data and image to find the perfect view. Tableau

16 Supporting the Cycle of Visual Analysis : Unification with the database Leverage the advances in Leverage the advances in fast database technology

17 Supporting the Cycle of Visual Analysis : Direct Interaction Make the tool disappear. Allow the user to directly interact with the data.

18 How is this possible? VizQL Imagine if databases shipped with only 5 prebuilt queries

19 How can products support being one with your data and how can I use them effectively? Generate Effective e Views of Data

20 Generating Effective Views of Data People shouldn t have to be graphic designers or psychologists

21 Generating Effective Views of Data An effective presentation of data: Communicates all of the data Communicates only the data Leverages the human perceptual system Is understandable Is interpretable

22 Generating Effective Views of Data Communicate all of relevant tdt data Bad Bad Good

23 Generating Effective Views of Data Communicate only the dt data Bad Bad

24 Generating Effective Views of Data Leverages the human perceptual system Bad Good

25 Generating Effective Views of Data Is understandable

26 advice: Keep your visuals simple

27 Generating Effective Views of Data Is interpretable

28 advice: Annotate but remain focused

29 Generating Effective Views of Data How does Tableau support generating effective views of data? Limiting the visual properties to a small and proven set Great defaults Small multiples Show Me and Show Me Alternatives Titling, captioning, and annotation

30 Generating Effective Views of Data How does Tableau support generating effective presentations? Limiting the visual properties to a small and proven set Great defaults Small multiples Show Me and Show Me Alternatives Titling, captioning, and annotation

31 Generating Effective Views of Data How do you pick effective colors? A line in HSV corresponds closely to a set of perceptually p equivalent colors but not quite. There is no good programmatic way to pick the colors so we hired a color expert to manually pick colors.

32 Generating Effective Views of Data To encode unordered data, we vary colors primarily in hue: To encode numeric or ordered data, we vary primarily in saturation and value: We discourage the use of more than 20 colors. Pick colors that can be named so people can talk about them that thatpoint the thecyan pinkishorangein theuppercorner corner We consider color blindness. Compare to Excel XP:

33 Generating Effective Views of Data Color for communicating data Color for aesthetics

34 Generating Effective Views of Data Automatic marks picks the best mark based on properties of the data.

35 Generating Effective Views of Data It is only possible to encode a limited number of dimensions in properties such as color, size, and shape.

36 advice: Use small multiples to simply and effectively see more data

37 How can products support being one with your data and how can I use them effectively? Be Easy and Simple to Use

38 Be easy and simple to use What do people mean when they say easy to use? Learnable Intuitive Efficient Direct Measuring ease of use is difficult: l A chart wizard is easy to learn and yet inefficient and hard to use.

39 Be easy and simple to use How do we achieve ease of use? Allowing incremental interaction Being direct Providing freedom Knowing and respecting our user Being simple

40 Be easy and simple to use Provide freedom No templates, no six month IT projects, no modes, no wizards Ask questions of your data and get answers!

41 advice: Empower your audience share interactive views

42 Be easy and simple to use Know and respect the user Focus the user interface on the content Support the user s task Allow the user to get into an uninterrupted t flow Provide great defaults

43 Be easy and simple to use Examples: A single toolbar with a limited set of muted buttons. Minimizing the non data ink. Unsaturated colors for the interface, saturated colors for the data. the user s data

44 advice: Stay focused on the data

45 How can products support being one with your data and how can I use them effectively? Generate Beautiful Results

46 Generating Beautiful Results

47 Who can Visual Analysis help? Everybody with data that is not getting answers

48 advice: Don t limit yourself to large data or special projects

49 advice: Seek outside inspiration

50 Summary Iterate, explore, and experiment regardless of task Start simple Reverse your thinking and start with the data Keep your visuals simple Annotate but remain focused Use small multiples Empower your audience by sharing interactive views Stay focused on the data Don t limit yourself to large data Seek outside inspiration