Data Literacy

1. Data Literacy Dennis Putze, Joey Arthur & Richard Ordowich

2. Agenda • Literacy: the basics • Data: what is it? • Data Literacy: what is it? • Why Data Literacy is important in: • Case Management • Performance Management • Data Design • Improving Data Literacy

3. Literacy - The Basics

4. Being Literate What does it mean being competent in a foreign language, say, French?

5. Child Support Case (French)

6. Literacy Capabilities The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines literacy as the "ability to identify, understand, interpret, create, communicateand compute, using printed and written materials associated with varying contexts. Literacy involves a continuum of learning in enabling individuals to achieve their goals, to develop their knowledge and potential, and to participate fully in their community and wider society. Key to all literacy isreadingdevelopment, a progression of skills that begins with the ability to understand spoken words and decode written words, and culminates in the deep understanding of text. Reading development involves a range of complex language underpinnings including awareness of speech sounds (phonology), spelling patterns (orthography), word meaning (semantics), grammar (syntax) and patterns of word formation (morphology), all of which provide a necessary platform for reading fluency and comprehension. Once these skills are acquired the reader can attain full language literacy, which includes the abilities to approach printed material with critical analysis, inference and synthesis; to write with accuracy and coherence; and to use information and insights from text as the basis for informed decisions and creative thought

7. Information Literacy Capabilities The United States National Forum on Information Literacy defines information literacy as: " ... the ability to know when there is a need for information, to be able to identify, locate, evaluate, and effectively use that information for the issue or problem at hand." Other definitions incorporate aspects of: "skepticism, judgment, free thinking, questioning, and understanding..." or incorporate competencies that an informed citizen of an information society ought to possess to participate intelligently and actively in that society

8. Evolution of Literacy

9. An introduction to data Whether or not you realize it, you are not only surrounded by computers but you have a persona created by the data associated with you. Some of this data you create yourself, consciously. Some is created when you open a bank account, shop using a loyalty card, and so on. What is this data, where does it come from and what you should know about this data are all things you need to be aware of.

10. Digital Personas

11. What’s in your wallet? Take a moment to look in your wallet or purse. What did you find?

12. An introduction to data The point is that these objects are data about us. Probably they hold our name, age, date of birth, and address in common, but each will also hold data that is different from the others. My persona consists of all of this data, whether I am aware of it or not. That is what I mean by a persona: a ‘picture’ of you created by various collections of data about you, such as your finances, shopping habits, interests. Does this “data” represent you? You might like to ask yourself at this point how aware you were, before doing the above exercise, that so much information about you existed. Think about the data we capture in child support. What are the digital personas of the participants in a case? Our data tells a story about us. Case data tells a story as well!

13. Defining Data What is data?

14. What is data? According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories: 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.

15. Definition of Data Data is a sequence of quantified or quantifiable symbols. Thus, a text is a piece of data. In fact, letters and characters are quantified symbols because there is a finite number of them; any alphabet (including digits and special characters) may be considered as a numbering system. Pictures, figures, recorded sounds and animation are also examples of (quantifiable) data, because they may be quantified (using digital scanners, cameras, recording devices, etc.) to the point that it is eventually difficult to distinguish, from their originals, their reproduction made from the quantified representation.

16. Data

17. Data Literacy Characteristics • What is Data Literacy? • Semantics • Syntax • Context • Interpretation • Computation • Understanding • Structure • Representation

18. Data Semantics (Meaning) 1234.56

19. Data Semantics Concept:A number Data Element Name: Arrears Amount Data Element Description: the amount of accumulated child support obligations

20. Introduction to Data Semantics How to discover the semantics of data?

21. Data Representation Representation Classification 1234.56 Text Binary 00110001001100100011001100110100001011100011010100110110 Hex 313233342e3536

22. Data Structure Arrears Amount Structure: Length: 10 Type: Numeric Decimal places: 2

23. Metadata Data Element Name: Arrears Amount Description: the amount of accumulated child support obligation Length: 10 Type: Numeric Decimal Places:2

24. Human Computer Interface

25. Data Syntax Creating sentences from data elements.

26. Data Syntax Child Support Case: Child Name & Custodial Party Name & Non Custodial Party Name & …= child support case Court Order: Child Name & Custodial Party Name & Non Custodial Party Name & Obligation = Court Order

27. Context of Data Who, what, why, where and when is the data being used? Two Primary Contexts: • Case Management • Performance Management

28. November 21, 2007 Interpretation

29. Interpretation Salaire

30. Computational Literacy You need to borrow $10,000. Look at the ad for Home Equity loans provided. Explain how you would compute the total amount of interest charges you would pay under this loan plan.

31. Data Literacy Skills

32. Data Literacy Manifesto • Ask what you want from your data not what your data will do for you. (use cases, scenarios, models and mockups) • Beware the data you touch. • Always scrub your data before using. (data quality) • Know your data (data standards) • Use data wisely. • Proverb: your data knows nothing. You impart meaning to your data. Without you, data is meaningless.

33. Learning to Read

34. Using Data Literacy • What is the intended purpose for using the data? • Case Management • Performance management

35. Case Management • Data Literacy Applied • “Reading a case” • Semantics • Syntax • Context • Interpreting data – critical thinking • Taking action – what needs to be done • Creating data – missing data

36. Child Support Case

37. CSE Case CSE Case data tells a story • Is the story a work of fiction or non-fiction? • Where does the story take place? • Who are the characters in a case? • What is the plot?

38. Child Support Case Data

39. CSE Case Data

40. Performance Management Applications of Data Literacy Selecting data Computing Measuring Interpreting

41. Performance Management The business analyst (BA) is asked to create a new performance metric: A measure of average collections per case or percent of cases with payments.

42. Data Literacy for Performance Management Writing the story

43. Data Journalism

44. Performance Management Semantics • What data is required? • Does the data have a time semantic? • Is the data current, this fiscal year, last five years? • Does the data have a geographic semantic? • Is this national, regional or local (State) data?

45. Transparency Guidelines: • What data was used, what data was excluded? • What were the assumptions that went into selecting the data? • How were the results calculated? What are the critical variables that affected the outcomes? – analytical model or algorithm transparency. • Its not enough to present the results. You must present how you got the results. Transparency provides the opportunity for others to “replay” the analytics.

46. Presenting the Results EXAMPLE: using subordinate clauses There were some anomalies in the data collection. The results confirm our hypotheses. a. Combine and emphasize the positive results. While there were some anomalies in the data collection, the results confirm our hypotheses. Explanation: by placing the anomalies in the subordinate clause, they are de-emphasized while the advantages of the results are emphasized. b. Combine and emphasize the anomalies in data collection. Although the results confirm our hypotheses, there were some anomalies in the data collection. Explanation: The subordinate clause de-emphasizes the results. c. Combine and give equal emphasis. The results confirm our hypotheses, but there were some anomalies in the data collection. Explanation: The coordinator "but" gives equal emphasis to the two clauses.

47. Math

48. Improving Data Literacy We’ve introduced you to the basics of Data Literacy. There are additional capabilities that can further improve Data Literacy: • Using Data Literacy to design data • Improving data quality

49. Data Design Improvements Applying Data Literacy to data design

50. Data Design Considerations - Past • Name • Description • Structure