Communicating Data

1. Communicating Data B.Ramamurthy Partially Based on Ben Jones Book [1]

2. Overview In this session we will learn how to communicate data with tools such as Tableau software.

3. Tableau Based on the book by B. JONEs [1]

4. Outline • Huge opportunity to find and share insights contained in data: • “data-driven” applications • Communication involves: numbers, words, images and videos • There are challenges: meaningful? fidelity? appeal? engaging? useful? breathtaking? • Tableau software has developed and created a visualization querying engine and user interface to make it easier to discover and communicate with data. • It frees the data from tables and spreadsheets that are indeed originally meant to be input medium • Tableau is for everyone, no need to know a programming language • Tableau desktop can connect to wide variety of data sources: relational databases, cloud sources, Hadoop technologies, etc. • Available for only Windows operating system.

5. Data Data refers to any kind of factual information that can be stored and digitally transmitted: Can be news articles, financial information in tables, data bases and so on. Communicating data is an important step in the data discovery process as shown in the next slide

6. The discovery process

7. Discovery process (contd.) • This is a highly iterative process that begins with a question; Domain-specific. • Specific question such as “which combination of products occurs most often?” • General question such as “what can we learn about historical sales of our products?” • Gathering data: • Internal , external • Buy or methods for gathering data yourself through feedsand APIs, free data available online (R data, amazon data) • The Data Science book we used for earlier sessions has given quite a few sources for gathering data • Verify the sources for reliability and fidelity

8. Discovery Process (contd.) • Data Structuring: This is an arduous process often refereed to as “data wrangling” and “data munging” • Cleaning up tags and fillers and • Filtering off unwanted data • Data is formatted, shaped, merged, converted and made ready for data exploration step • We looked at this with an R exercise in Session 3 • Our Data science book has many examples: see the example using data extracted via NYTimes API in Chapter 5

9. Discovery Process (contd.) Exploring data: data is viewed, analyzed from various points of views until one of more insights are gleaned. This exploration provides the insights/discoveries/knowledge/quantitative results Communicating data involves representing the discoveries in a form that the discoveries/insights can be easily understood by decision makers.

10. Six principles of communicating data [1] • Know your goal • Who? Target audience • What? Intended meaning • Why? Desired effect • Use the right data • Does not have to be big data but right data: Example: the story of a single data point 14. • Right amount of data: big or small • Ethically and legally collected • Select suitable visualizations • Quantitative, ordinal and nominal datatypes, each demand different types of visualization • Choices: position, length, angle, area, grey ramp, color ramp, color hue, shape, maps

11. Six Principles (contd.) [1] • Design for aesthetics (of course) • Choose an effective medium and channel • Medium: the form the message takes • Channel: how it gets delivered • Check the results • Check the reach, understanding and impact

12. Tableau Tableau is a drag and drop analysis and visualization software It is a level of abstraction above d3.js, three.js and R in that it requires no programming Learning curve for Tableau is flat; one can quickly ramp up and create useful and impressive visuals and analytics

13. Main Components of Tableau • Workbook • Worksheet • Data sources, Plots, charts. • Dashboard(s): single interactive visual with one or more sheets • worksheets • Story: a sequence of interactive visuals with one or more dashboards and worksheets with navigation facilitating presentation • dashboards • worksheets

14. Dimensions and Measures • When a user connects to a data source, Tableau automatically classifies each field as either a Dimension or Measure. • Dimensions are fields that are used to group or categorize the data • Example: Country, State • Measure Names • Measures are fields that can be used compute: like summing and averaging. • Area • Population • Latitude, longitude • Measure values

15. Usage of Tableau • Excellent tool of team interaction: for encouraging discussions during team meetings to explore “what if” questions. • No need for a prepared dashboard or story: just data exploration • Dashboards enable you to communicate facts to your management team, to your customer via your web page. Example: create a dash board and display it on your web page, let your audience interact and watch and monitor their interest • Story: lets you communicate results to any audience, specifically clients, decision makers, sales force and upper management.

16. Tableau Exercises (See Ubbox for Instructions) We introduce the main features and basic plots and “worksheet” of Tableau using world data about GDP and population. (Exercise 1) Exercise 2 is a comprehensive example covering most features of a Tableau and an interesting real data set of NHL 100 top point scorers. Exercise 3 continues with the same NHL data with the focus preparing a Tableau “Dashboard” Final exercise is on designing a Tableau “Story” using the World data on GDP and population.

17. Summary We studied principles and methods for communicating data More specifically we looked at Tableau for drag-drop data analytics and visualization We also worked on complete examples illustrating its features.

18. References B. Jones. Communicating data with Tableau, Designing, developing and delivering data visualizations, O’Reilly, 2014. http://dataremixed.com/books/cdwt/