Addressing the importance and imperative for everyone to become data literate for the future of work for both individuals and for organizations, Dr. Borne will cover five major themes: data awareness (what is it?), data relevance (why me?), data literacy (show me how), data science (where's the science?), and the data imperative (create and do something with data). Data permeates our daily lives through all conceivable digital technologies, handheld devices, business activities, and personal activities. Through data, the world is computable. The focus is not on the mathematics, the algorithms, or the engineering. Instead, the focus is on demonstrating that data science is universally appealing, data literacy is accessible, and data fluency is achievable for all. The democratization of data assets and data literacy is essential for all. Data Literacy is not a math skill -- it is a life skill.

1. Dr. Kirk Borne
Principal Data Scientist and Data Science Fellow
Booz Allen Hamilton
@KirkDBorne
Download slides here: http://www.kirkborne.net/QlikNYC2020/
Data Literacy and its Implications for Society
“It is not a Math Skill. It’s a Life Skill.”
#LeadWithData

2. What is Big Data’s Biggest Challenge?
Hint: it’s not Volume. Answer: it’s Complexity!
3
https://cdn.andertoons.com/img/toons/cartoon6517t.png

3. What is Big Data’s Biggest Challenge?
Hint: it’s not Volume. Answer: it’s Complexity!
1. Every organization collects many different (complex) sources of data,
especially financial organizations.
2. These multiple diverse data sets are often stored in separate silos (**).
4
(**) Consider the Blind Men
and the Elephant …
"The data needed to form a complete picture is never in one place."
– Paul Van Siclen, Qlik
https://blog.qlik.com/the-value-of-options-in-the-data-integration-and-analytics-supply-chain

4. What is Big Data’s Biggest Challenge?
Hint: it’s not Volume. Answer: it’s Complexity!
1. Every organization collects many different (complex) sources of data,
especially financial organizations.
2. These multiple diverse data sets are often stored in separate silos (**).
3. Silos inhibit data teams from integrating multiple data sets that (when
combined) could yield deep, actionable insights to create business value.
5
(**) Consider the Blind Men
and the Elephant …
“What we have is a lot of information, but very few insights."

5. What is Big Data’s Biggest Challenge?
Hint: it’s not Volume. Answer: it’s Complexity!
1. Every organization collects many different (complex) sources of data,
especially financial organizations.
2. These multiple diverse data sets are often stored in separate silos.
3. Silos inhibit data teams from integrating multiple data sets that (when
combined) could yield deep, actionable insights to create business value.
4. Teams of Data Literate business professionals have the power to
understand & integrate those data sources and to change that entire story!
6

6. OUTLINE
• Data Awareness
• Data Relevance
• Data Literacy
• Data Science
• Data Imperative
7
Source: https://www.expertsystem.com/government-data-mining/
http://www.boozallen.com/ai
http://www.boozallen.com/datascience @KirkDBorne

7. OUTLINE
• Data Awareness
• Data Relevance
• Data Literacy
• Data Science
• Data Imperative
8
Source: https://www.expertsystem.com/government-data-mining/
http://www.boozallen.com/ai
http://www.boozallen.com/datascience @KirkDBorne

8. People want (need) to know…
“What is data?”
https://www.kiasuparents.com/kiasu/article/big-pond-vs-small-pond-how-does-competition-affect-our-kids/attachment/teacher-asking-question-with-children-in-classroom/
9

9. How do we learn about our World
(and the Universe) around us?
WE GATHER DATA AND INFORMATION,
FROM WHICH WE DERIVE KNOWLEDGE AND WISDOM,
FROM WHICH WE DECIDE WHAT ACTIONS TO TAKE.
10

10. How do we learn about our World
(and the Universe) around us?
WE GATHER DATA AND INFORMATION,
FROM WHICH WE DERIVE KNOWLEDGE AND WISDOM,
FROM WHICH WE DECIDE WHAT ACTIONS TO TAKE.
Data => Information => Knowledge (Wisdom, Understanding) => Action!
11

11. How do we learn about our World
(and the Universe) around us?
WE GATHER DATA AND INFORMATION,
FROM WHICH WE DERIVE KNOWLEDGE AND WISDOM,
FROM WHICH WE DECIDE WHAT ACTIONS TO TAKE.
Data => Information => Knowledge (Wisdom, Understanding) => Action!
12
Data => Knowledge Discovery => Decision Support => Actionable Intelligence!

12. 13
What is “Data”?
… Is this data? …

13. This is Data…
Databases
Data Tables (Excel)
Images (photos)
Graphs (plots)
Documents (text)
Social Networks
Phone App Usage
Web Clicks
Purchase Logs
Sensor Readings
Time Series
Speech (voice)
Audio (sounds)
Smells (odors)
Biometrics (my face)
… and more …
14

14. It’s all Data…
15
http://blog.agroknow.com/?p=4690

15. OUTLINE
• Data Awareness
• Data Relevance
• Data Literacy
• Data Science
• Data Imperative
16
Source: https://www.expertsystem.com/government-data-mining/
http://www.boozallen.com/ai
http://www.boozallen.com/datascience @KirkDBorne

16. People want (need) to know…
“Why is data relevant to me?”
https://www.kiasuparents.com/kiasu/article/big-pond-vs-small-pond-how-does-competition-affect-our-kids/attachment/teacher-asking-question-with-children-in-classroom/
17

17. The world is now data!
18
https://www.visualcapitalist.com/what-happens-in-an-internet-minute-in-2019/

18. We are ALL data generators!
Shouldn’t we all also be
value generators?
The most important
characteristic of Big Data
is Value Creation!
https://knowyourmeme.com/photos/1119756-the-internet
19

19. My Data Science Career “Aha!” moment:
What is this Data Mining and Machine
Learning stuff really good for?
6
Data
Mining
what?
20

20. The digital technology world is created by,
driven by, and defined by data!
https://hadoopilluminated.com/hadoop_illuminated/Big_Data.html
21

21. The Rapidly Expanding Digital Innovation Frontier:
Innovations are inspired by data, informed by data, enabled by data.
– These digital innovations generate value and create jobs.
• AI
• 5G
• Drones
• Robotics
• Virtual Reality
• Virtual Assistants
• Machine Learning
• Augmented Reality
• Natural Language Processing
• IoT (Internet of Things) … sensors everywhere
• Autonomous Dynamic Data-driven Application Systems
• 3D-Printing … moving on to 4D-printing
• XPUs (specialized CPUs at the Edge)
• XAI (eXplainable AI, Trust in AI)
• Linked Knowledge Graphs
• Autonomous Vehicles
• Quantum Computing
• Computer Vision
• Digital Twins
• Blockchain
• …
22

22. “Data is for all.
Data is not a 4-letter word!”
23

23. OUTLINE
• Data Awareness
• Data Relevance
• Data Literacy
• Data Science
• Data Imperative
24
Source: https://www.expertsystem.com/government-data-mining/
http://www.boozallen.com/ai
http://www.boozallen.com/datascience @KirkDBorne

24. People want (need) to know…
“What is Data Literacy?”
https://www.kiasuparents.com/kiasu/article/big-pond-vs-small-pond-how-does-competition-affect-our-kids/attachment/teacher-asking-question-with-children-in-classroom/
25

25. Data Literacy is a way of thinking about
numbers and measurements of things …
https://www.reddit.com/r/funny/comments/4axvj1/the_new_cuyama_sign/ 26

26. Data Literacy is a way of thinking about
numbers and measurements of things …
27

27. Initiative for Analytics and Data Science
Standards (IADSS) – https://www.iadss.org/
Tasks and Activities associated with Data:
• Business Understanding
• Data Exploration and Preparation
• Data Representation and Transformation
• Computing with Data
• Model Building
• Model Evaluation and Maintenance
• Visualization and Presentation
• Deployment and Application Development
• Communication = “Talking the Walk” = Data Storytelling
28

28. Key Component of Data Literacy – Data Storytelling:
(Successful Data Science includes telling compelling Data Stories)
29

29. Key Component of Data Literacy – Data Storytelling:
(Successful Data Science includes telling compelling Data Stories)
30
https://www.exaltus.ca/blog/emotional-storytelling-the-b2b-marketing-superpower-you-may-be-neglecting/

30. Key Component of Data Literacy – Data Storytelling:
(Successful Data Science includes telling compelling Data Stories)
31
https://www.exaltus.ca/blog/emotional-storytelling-the-b2b-marketing-superpower-you-may-be-neglecting/

31. Key Component of Data Literacy – Data Storytelling:
(Successful Data Science includes telling compelling Data Stories)
32
https://www.exaltus.ca/blog/emotional-storytelling-the-b2b-marketing-superpower-you-may-be-neglecting/

32. Key Component of Data Literacy – Data Storytelling:
(Successful Data Science includes telling compelling Data Stories)
https://datajournalism.com/
33
https://www.exaltus.ca/blog/emotional-storytelling-the-b2b-marketing-superpower-you-may-be-neglecting/

33. Data Literacy
34
(Jordan Morrow, Qlik)
http://www.dataliteracynetwork.org/definitions.html
“Data Literacy includes the ability to read,
work with, analyze, and argue with data.”
Source: http://bit.ly/2mEzJsr

34. OUTLINE
• Data Awareness
• Data Relevance
• Data Literacy
• Data Science
• Data Imperative
35
Source: https://www.expertsystem.com/government-data-mining/
http://www.boozallen.com/ai
http://www.boozallen.com/datascience @KirkDBorne

35. People want (need) to know…
“Where’s the Science?”
https://www.kiasuparents.com/kiasu/article/big-pond-vs-small-pond-how-does-competition-affect-our-kids/attachment/teacher-asking-question-with-children-in-classroom/
36

36. What do you see here? … a receipt?
37

37. I see data!
38

38. This person sees opportunity!
https://twitter.com/DataToViz/status/1124752405973782528
https://www.susielu.com/data-viz/reviziting-the-receipt
39

39. Data Science = Discovering the patterns and behaviors of
things (including their relationships and related outcomes)
through data (i.e., their observed properties).
40
“The two most important
things in Data Science are
the data and the science!”
– Kirk Borne

40. 4 Types of Discovery from Data:
1) Class Discovery: Find the categories of objects
(population segments), events, and behaviors in
your data. + Learn the rules that constrain the class
boundaries (that uniquely distinguish them).
2) Correlation (Predictive Power) and Causality
(Prescriptive Power) Discovery: (INSIGHT
DISCOVERY) – Find trends, patterns, dependencies
in data that reveal the governing principles or
behavioral patterns (the object’s “DNA”).
3) Outlier / Anomaly / Novelty / Surprise Discovery:
Find the new, surprising, unexpected one-in-a-[million /
billion / trillion] object, event, or behavior.
4) Association (or Link) Discovery: (Graph and
Network Analytics) – Find both the usual and the
unusual (interesting) data associations / links /
connections across the entities in your domain.
(Graphic by S. G. Djorgovski, Caltech)
41
Data Science

41. ◼ Classic Textbook Example of Data Mining (Legend?): Data
mining of grocery store logs indicated that men who buy
diapers also tend to buy beer at the same time.
Association Discovery Example #1
42

42. ◼ Amazon.com mines its customers’ purchase logs to
recommend books to you: “People who bought this book also
bought this other one.”
Association Discovery Example #2
43

43. ◼ Netflix mines its video rental history database to recommend
rentals to you based upon other customers who rented similar
movies as you.
Association Discovery Example #3
44

44. It is not about the math or the engineering…
…it is fundamentally about understanding
how to create value from data!
45

45. 46
Source for graphic: https://www.altexsoft.com/blog/datascience/machine-learning-strategy-7-steps/
Predictive Analytics is a major application of Data Science
and Data Literacy, especially in Financial Services

46. OUTLINE
• Data Awareness
• Data Relevance
• Data Literacy
• Data Science
• Data Imperative
47
Source: https://www.expertsystem.com/government-data-mining/
http://www.boozallen.com/ai
http://www.boozallen.com/datascience @KirkDBorne

47. People want (need) to know…
“How important is it that
my organization leads with data?”
https://www.kiasuparents.com/kiasu/article/big-pond-vs-small-pond-how-does-competition-affect-our-kids/attachment/teacher-asking-question-with-children-in-classroom/
48

48. “Poor data literacy is crippling businesses”
https://www.experianplc.com/media/news/2020/the-cost-of-data-debt-rises-as-businesses-face-the-challenge-of-low-data-literacy/
https://www.edq.com/resources/data-management-whitepapers/2020-Global-data-management-research/
(report published 2/18/2020)
49
“According to a new report from Experian, even
though 84% of organizations consider data literacy
a “core skill”, the inability to use and analyze data
effectively is crippling businesses; and 85% agree
that improving data literacy rates among the
workforce will be critical to future success.”
https://www.itproportal.com/news/poor-data-literacy-crippling-businesses/

49. From Qlik.com report: “The Data Literacy Index”
https://thedataliteracyproject.org/
50

50. “Making data literacy part of your strategy can be easier said than done,”
says Jamie Blommarert, from Qlik:
– https://twitter.com/jamieblommaert/status/1229103154827952129
Headlines and Quotes…
“Lots of people are scared of data,” says Bernard Marr.
– https://www.telegraph.co.uk/business/data-literacy/how-businesses-can-improve/
“Three-quarters of employees aren’t comfortable when using data.”
– https://www.telegraph.co.uk/business/data-literacy/how-businesses-can-improve
“Lacking digital skills is currently costing UK businesses a staggering £10
billion in lost productivity every year.”
– https://www.consultancy.uk/news/23685/lacking-digital-skills-costs-10-billion-in-lost-productivity
Qlik Debuts Industry First Data Literacy Consulting and
Service Offerings
– https://blog.qlik.com/qlik-debuts-industry-first-data-literacy-consulting-and-service-offerings
51

51. Let’s see that again…
52
Qlik Debuts Industry First Data Literacy as-a-Service Offering
– https://blog.qlik.com/qlik-debuts-industry-first-data-literacy-consulting-and-service-offerings

52. Data Data Science Engineering Design
53
(It’s everywhere and part of everything!)

53. Data Data Science Engineering Design(Discover new knowledge!)
54

54. Data Data Science Create & Do Something
55

55. http://rocketdatascience.org/
56

56. http://rocketdatascience.org/
Data Literacy is a way of thinking and a way of life!
57

57. Come for the data! Stay for the science!
#LeadWithData
Thank you!
Dr. Kirk Borne
Principal Data Scientist, Booz Allen Hamilton
Twitter: @KirkDBorne , Email: kirk.borne@gmail.com
These slides: http://www.kirkborne.net/QlikNYC2020/
58http://www.boozallen.com/ai
http://www.boozallen.com/datascience @KirkDBorne