Bit120 m02 l04 - accessing the value of information
- 1. © 2011 Neumont University accessing the value of data DATA VISUALIZATION 1
- 2. © 2011 Neumont University transformation of data 2
- 3. © 2011 Neumont University definition • Data visualization (or more appropriately, information visualization) has been defined as the use of visual representations to explore, make sense of, and communicate data. • Since, information is the aggregation, summarization, and contextualization of data (raw facts), what is portrayed in visualizations is both the information and the data.
- 4. © 2011 Neumont University DATA INSIGHT Blog post: http://www.datarevelations.com/mostly-monthly-makeover-utah-state-university-survey-of-student-engagement.html
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- 9. © 2011 Neumont University why visualization? 9 constructed in 1973 by the statistician Francis Anscombe
- 10. © 2011 Neumont University our brains detect patterns • we’re extremely good at detecting patterns and pattern violations … – trends – gaps – outliers 10
- 11. © 2011 Neumont University our brains detect patterns • we’re extremely good at detecting patterns and pattern violations … – trends – gaps – outliers 11
- 12. © 2011 Neumont University visual analysis process Source: http://www.tableausoftware.com/videos/zen
- 13. © 2011 Neumont University successful visualization – pillars • has clear purpose • includes (only) relevant content • uses appropriate structure • has useful formatting • purpose – why this visualization • content – what to visualize • structure – how to visualize it • formatting – everything else 13
- 14. © 2011 Neumont University visualization creation - purpose do you know … • Why am I creating this visualization? • Who is it for? • What do they need to understand? • What actions do you need to enable? 14
- 15. © 2011 Neumont University visualization creation - content 15 • What data matters? • What relationships matter? • Informed by purpose! • What’s excluded is as important as what’s included. http://www.businessinsider.com/iphone-bigger-than-microsoft-2012-2
- 16. © 2011 Neumont University visualization creation - structure 16 http://hipmunk.com • How do we best reveal the most important data and relationships? (Position!) • Choose meaningful layout and axes! • Use both axes! (Both, not three…) • Informed by purpose and content.
- 17. © 2011 Neumont University visualization creation - formatting 17 • How should it look and feel? • How will it be consumed? • Makes data and relationships accessible. • Makes importance visible.
- 18. © 2011 Neumont University Data Visualization Software • TABLEAU • IBM COGNOS • DOMO 18
- 19. © 2011 Neumont University TABLEAU 19
- 20. © 2011 Neumont University IBM COGNOS 20 Cognos software enables organizations to become top-performing and analytics-driven entities. From business intelligence to financial performance and strategy management, Cognos software is designed to help everyone in your organization make the decisions that achieve better business outcomes—today and in the future.
- 21. © 2011 Neumont University DOMO 21 Domo’s Business Cloud is the world’s first open, self-service platform to run your entire organization. The Business Cloud brings together the data, the people, and the insights business users need to deliver a big picture view of what’s happening in your business so you can answer your most pressing business questions and optimize performance in real time.
- 22. © 2011 Neumont University visualization software comparisons 22
- 23. © 2011 Neumont University data mining 23
- 24. © 2011 Neumont University dashboards 24 keeping your corporate ship on course
- 25. © 2011 Neumont University in class … • question & answer from today • creating data visualization with Excel exercise • visualization software experience • case studies to demonstrate application 25
Editor's Notes
- #8: * They began with a simple enough concept – plotting numbers of championships won against number of years spent competing. To this, the developers added an extra dimension of information; the level of investment received by each sporting institution. * It was through the convergence of these three metrics that a narrative was formed. At a glance, content users were able to understand the return on investment offered by each franchise, and also the impact that differing levels of investment have upon different sports.
- #10: 1- Anscombe's quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance of graphing data before analyzing it and the effect of outliers on statistical properties. 2- All four sets are identical when examined using simple summary statistics, but vary considerably when graphed 3- The quartet is still often used to illustrate the importance of looking at a set of data graphically before starting to analyze