In an informal workshop hosted by Bob Corporaal, the group looked at the connection between IoT, Digital Transformation and Data Visualization. They explored a step-by-step process that can help create more value with the data from your IoT application.

1. Leveraging Data
Visualization for IoT
— The Chicago Connectory
by CLEVER°FRANKE | 11/06/2018
Follow us: @cleverfranke

2. 5 quintillion bytes of data produced every day*
* According to Cisco in 2018
= 5 000 000 000 000 000 000 BYTES

3. How to make an impact with all this data?

4. Large data volume and diversity of metrics
Information overload
Difficult to extract relevant insights
Tool complexity
Taking data beyond the experts
Communication across the organization
Challenges in leveraging IoT data
DATA CHALLENGES

5. Crop health Traffic congestion
Air quality Wildebeests

6. 4 key questions that lead to
better data-driven IoT applications

7. ``
CHICAGO
DRIVING CHANGE
THROUGH DATA-DRIVEN
EXPERIENCES & TOOLS
35+ PEOPLE
UTRECHT
CLEVER°FRANKE FACTS

8. INGREDIENTS
BUSINESS GOALS USER GOALS
DATA AND CONTENT
TECHNOLOGY

9. INGREDIENTS
BUSINESS GOALS USER GOALS
DATA AND CONTENT
TECHNOLOGY
SWEET SPOT

10. PROCESS
BUSINESS GOALS
USER GOALS
DATA
TECHNOLOGY
1
Phase
DISCOVER
& DEFINE
2
Phase
IDEATE
& PROTOTYPE

11. PROCESS
BUSINESS GOALS
USER GOALS
DATA
TECHNOLOGY
1
Phase
DISCOVER
& DEFINE
2
Phase
IDEATE
& PROTOTYPE

12. QUESTION #1
Who is your audience?

13. PAGE TITLE
Consider weather data

14. DIFFERENT AUDIENCES, DIFFERENT NEEDS
On a route over time Hyperlocal forecast
Extreme weather Basic information

15. GETTING STARTED
Identifying and understanding the target audience

16. GETTING STARTED
Identifying and understanding the target audience
SAY & DO?
attitude in public
appearance
behavior towards others
what does the professional
PAIN?
fears
frustrations
obstacles
GAIN?
wants/needs
measures of success
obstacles
what does the professional
HEAR?
what friends say
what boss says
what influencers say
what does the professional
THINK & FEEL?
what really counts
major preoccupations
worries & aspirations
what does the professional
SEE?
environment
friends
what the market offers
Empathy map

17. Roles and responsibilities
Goals and challenges
Relevant KPI’s
Context when using your product
Frequency of usage and duration
Current tools, frustrations and likings
Their expertise in relation to (AI) models
Things to know about your audience
YOUR AUDIENCE

18. Keep in mind there is probably
more than one audience.
How do they relate?
How do they differ?
How do they share their insights?
YOUR AUDIENCE

19. QUESTION #2
What is the intent?

20. Personal commuteQuick update

21. WHAT IS THE INTENT?
The intent of a data visualization
Lookup Persuade Creative techniques
Answer questions
Learn / increase knowledge
Monitor signals
Change behavior
Conduct analysis
Trigger questions Tell a story Play with data
Enlighten
Contextualize dataFind patterns
Serendipitous discoveries
Familiarize with data
Shape opinion
Emphasize issues
Inspire
Grab attention
Present arguments
Experimentation Shock / make an impact
Art / aesthetic pleasure

22. Data volume and complexity
Place and time the data is viewed
Relevancy of your data
Intended next action after seeing the data
Focus on real time or trends or both
Your relation with the audience
Priority of different intents
Things to consider about the intent
THE INTENT

23. QUESTION #3
Are you explaining
or are they exploring?

24. ExploringExplaining

25. Is exploring sufficient to find insights
Is explaining sufficient to be confident
Will the audience draw the right conclusions
Tools needed for exploration
Build an engaging narrative
Combine both modes, but start with one
Things to consider about explaining vs exploring
EXPLAINING VS EXPLORING

26. QUESTION #4
Are they reading or
feeling the data?

27. FeelingReading

28. What accuracy is needed to read
Is the focus on trends or data points
How dynamic is the data
Focus on the most relevant data dimensions
Things to consider about reading vs feeling
READING VS FEELING

29. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

30. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

31. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

32. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

33. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

34. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

35. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

36. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

37. GETTING STARTED
Establishing intent
By Andy Kirk
Reading the data
Feeling the data
Explanatory
goals:
…analysis
…finding patterns
goals:
…communication
…presenting findings
intended to:
• Create an aesthetic that portrays a general
story or sense of pattern
• Give a feel for the physicality of data
• Immersive experience
intended to:
• Deliver fast, effective and precise portrayals of data;
• Usually a captive audience that wants to learn from the data
• Show performance / operational activity;
Exploratory

38. CREATING
Different chart types for different uses
Comparing quantitative & categorical values Charting hierarchical & part-to-whole relationships
Mapping spatial data
Graphing connections & multivariate relationshipsPlotting trends & changes over time

39. 1. Who is your audience?
↓
2. What is the intent?
↓
3. Are you explaining or are they exploring?
↓
4. Are they reading or feeling the data?
4 KEY QUESTIONS

40. 1. Who is your audience?
↓
2. What is the intent?
↓
3. Are you explaining or are they exploring?
↓
4. Are they reading or feeling the data?
4 KEY QUESTIONS

41. 32