Reporting Data<br />Eduardo Jezierski<br />(Engineering)not a doctor!!<br />edjez@instedd.org<br />
Objectives<br />Reporting and collecting data: <br />Collecting, balancing structured vs. un-structured (and semi-structur...
External data<br />Information is power<br />Relevance & Targeting<br />Aggregation, Sharing, Authorizing<br />Rapid Feedb...
Example Technologies<br />Desktop<br />Epiinfo<br />Groove<br />Mobile<br />Open Data Kit<br />Epicollect<br />Java ROSA<b...
Survey Monkey
Google Spreadsheets & Form
Fusion Tables
SMS
Geochat
RapidSMS
Frontline SMS
Analysis
SPSS
STATA
Etc.</li></li></ul><li>Emerging alternatives<br />USSD<br />Unstructured Supplementary Service Data<br />Simple to use<br ...
Why is SMS popular<br />Low cost<br />Needs little signal (1 bar enough)<br />Uses little power<br />Works on existing equ...
Ways of sharing information<br />UNSTRUCTURED<br />“Hello everyone we have meeting tomorrow”<br />“We are dealing with cho...
Feature Extraction<br />Unstructured Data<br />Feature Extraction<br />Structured Data<br />SMS Messages<br />Time<br />Da...
SMS Syntax<br />
Feature Extraction Example: Places<br />“At Stung Treng, seeing Cholera”<br />Lat; Long<br />1) FIND Feature<br />2) Assoc...
A Haitian with a need sends an SMS to the 4636 shortcode<br />The SMS goes through Nuntium and then onto Emergency Informa...
27 letters<br />8 buttons<br />
50+ letters<br />8 buttons<br />
New Project…<br />syntax<br />Simplify your users’ experience<br />Machines adapt to the users<br />
Tombodu (Sierra Leone) traditional registration<br />
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UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data

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UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data

  1. 1. Reporting Data<br />Eduardo Jezierski<br />(Engineering)not a doctor!!<br />edjez@instedd.org<br />
  2. 2. Objectives<br />Reporting and collecting data: <br />Collecting, balancing structured vs. un-structured (and semi-structured), formats, level of detail, tools for collecting, storing and visualizing <br />Learning Objectives:<br />Explain the difference between structured, semi-structured and un-structured data<br />Design a simple structured data reporting format<br />Understand the options for collecting, parsing, storing and analyzing and sharing structured data<br />
  3. 3. External data<br />Information is power<br />Relevance & Targeting<br />Aggregation, Sharing, Authorizing<br />Rapid Feedback<br />Alerting<br />Early analytics<br />Getting information<br />
  4. 4.
  5. 5. Example Technologies<br />Desktop<br />Epiinfo<br />Groove<br />Mobile<br />Open Data Kit<br />Epicollect<br />Java ROSA<br />Pendragon<br />USSD<br />Digital Pen<br /><ul><li>Web
  6. 6. Survey Monkey
  7. 7. Google Spreadsheets & Form
  8. 8. Fusion Tables
  9. 9. SMS
  10. 10. Geochat
  11. 11. RapidSMS
  12. 12. Frontline SMS
  13. 13. Analysis
  14. 14. SPSS
  15. 15. STATA
  16. 16. Etc.</li></li></ul><li>Emerging alternatives<br />USSD<br />Unstructured Supplementary Service Data<br />Simple to use<br />Simple to report structured information<br />Requires collaboration with all phone companies<br />Digital Pen<br />Simple to use<br />Simple to report much information<br />Ongoing cost of ‘paper’/forms<br />Requires separate mobile<br />
  17. 17. Why is SMS popular<br />Low cost<br />Needs little signal (1 bar enough)<br />Uses little power<br />Works on existing equipment<br />Scales from few users to nationwide<br />
  18. 18. Ways of sharing information<br />UNSTRUCTURED<br />“Hello everyone we have meeting tomorrow”<br />“We are dealing with cholera outbreak will call you later”<br />SEMI-STRUCTURED<br />“at Ratchaburi, we are seeing Cholera URGENT”<br />“H5N1 Birds:200 should we call PHD?”<br />STRUCTURED<br />“H5N1, Birds:200, Lab: No, FollowUp: no” <br />E.g TURTLE standard<br />Simple, easy, flexible<br />Simple, requires FEATURE EXTRACTION, some training<br />Complex for human entry, hard to learn and to get right<br />
  19. 19. Feature Extraction<br />Unstructured Data<br />Feature Extraction<br />Structured Data<br />SMS Messages<br />Time<br />Data Records<br />Algorithms & Databases<br />Voice/Radio Calls<br />Place<br />Relationships<br />Pictures and videos<br />Person<br />Metadata<br />Event<br />Experts<br />Organization<br />Sensor Readings<br />Trustworthiness<br />Image Recognition<br />Crowds<br />Closed Captioning<br />Sensors<br />Face Recognition<br />Calibration<br />
  20. 20. SMS Syntax<br />
  21. 21.
  22. 22. Feature Extraction Example: Places<br />“At Stung Treng, seeing Cholera”<br />Lat; Long<br />1) FIND Feature<br />2) Associate Metadata<br />Stung Treng= Lat; Long<br />OPEN DATABASES tend to be the richest sources of local data & provide a strong platform<br />Google, Yahoo Geocoders<br />Open Street Maps<br />Your own database e.g. PCODES<br />Humans are excellent at extracting features! Unless you need real-time; large volume geocoding, crowd sourcing is an excellent option<br />
  23. 23. A Haitian with a need sends an SMS to the 4636 shortcode<br />The SMS goes through Nuntium and then onto Emergency Information System<br />A Haitian volunteer or staff and translates, tags, geocodes<br />The organized information is then dispatched to response or added to reports<br />
  24. 24. 27 letters<br />8 buttons<br />
  25. 25. 50+ letters<br />8 buttons<br />
  26. 26. New Project…<br />syntax<br />Simplify your users’ experience<br />Machines adapt to the users<br />
  27. 27. Tombodu (Sierra Leone) traditional registration<br />
  28. 28.
  29. 29.
  30. 30. Other Considerations<br />Scalability<br />Logical vs Physical data paths<br />Cloud Computing<br />User Experience & Design<br />
  31. 31. Scaling & Reducing costs of SMS<br />?<br />?<br />Operator Collaboration<br /><ul><li>Per country
  32. 32. Per company</li></ul>High Scale<br />Lowest Costs<br />Long negotiations<br />Complex connection<br />Local Gateway<br /><ul><li>USB Modem
  33. 33. Phone via USB</li></ul>Easy to get started<br />No internet required<br />Slow (1msg/6 second)<br />Unreliable- Support<br />Virtual Operators<br /><ul><li>Skype
  34. 34. International #</li></ul>Easy to get started<br />High Scale<br />Spotty coverage<br />Blocked in some countries<br />Cost/msg<br />
  35. 35. Logical vs Physical data paths<br />Pattern: Digitize existing reporting protocols<br />Antipattern: implement in digital form the physical path of paper<br />Complex-Expensive-Unreliable-Less Secure<br />
  36. 36. Cloud Computing<br />“Cloud Computing”<br /><ul><li> Cost effective
  37. 37. Secure
  38. 38. Scalable
  39. 39. Reliable</li></ul>Not “IF”,<br />but “HOW to do cloud computing”<br /><ul><li>Standard data download
  40. 40. Can delete on demand
  41. 41. Ownership?
  42. 42. Privacy?
  43. 43. External dependency?</li></li></ul><li>User Experience Tips<br />“Interact with the world through a 160 character browser”<br />Context & Goals<br />Wizard of Oz<br />Start Small, Start simple<br />Log all messages for retrospective, rinse, repeat<br />SMS Challenges<br />Forgiving Empowering<br />
  44. 44. Appropriate design is best done by locals<br />Share your skills and get out of the way!<br />
  45. 45. Summary<br />Data collection is only one link in the chain<br />Simplicity and usability are key<br />Unstructured and semi-structured data are a good balance of data quality and usability<br />
  46. 46. Thank You!<br />Eduardo Jezierski<br />(Engineering)not a doctor!!<br />edjez@instedd.org<br />@edjez<br />
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