1 / 11

Data : symbols

Data : symbols Information : data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions Knowledge : application of data and information; answers "how" questions Understanding : appreciation of "why" Wisdom : evaluated understanding.

clare
Download Presentation

Data : symbols

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Data: symbols Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions Knowledge: application of data and information; answers "how" questions Understanding: appreciation of "why" Wisdom: evaluated understanding. From: Gene Bellinger, Durval Castro, Anthony Mills (http://www.systems-thinking.org/dikw/dikw.htm)

  2. Our Mission(should we choose to accept it) • Approached to help a local grocery story figure out whether they have segments in their customer base. • Whether the segments indicate different operations strategies • They think it’s about average age, sex and time of day. • Matters because they want to shape the shopping experience around average shoppers and they think time of day matters.

  3. Design • Random intercept survey of customers at three times of day (weekdays): • 9am • 5:30pm • Midnight • Short survey: • Sex (nominal) • # of items (ratio) • Age (ratio) • Rate shopping experience (ordinal – poor, good, excellent)

  4. Analysis Plan?

  5. The Basics • n = 120 • 9am: n = 40 • 5:30pm: n = 40 • Midnight: n = 40 • Women = 64 (53%) • Men = 56 (47%) • Rating of shopping experience: • Poor = 30% • Good = 37% • Excellent = 33% • Average # of items = 16 • Average Age = 35

  6. 5:30 pm • 60% female; 40% male • Average age: 35 • Average # of items: 12 • Rating: 45%/18%/40% 9 am • 70% female; 30% male • Average age: 37.6 • Average # of items: 30 • Rating: 30%/40%/30% Midnight • 30% female; 70% male • Average age: 31 • Average # of items: 5 • Rating: 22%/46%/30%

  7. What next?

  8. Data to Information to Knowledge?

More Related