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Data Day:
Telling Your Data
Story
Defining Constituents,
Data Vizzes and Telling
a Data Story
Jeremy Anderson & Krisztina Filep
Agenda
• Defining Constituents
• Data Visualization Tips & Tricks
• Telling a Story with Data
• Activity
Defining Constituents
Students
Faculty
Staff
Managers
Leaders
Board
Alumni
Parents / guardians
Prospective students
Donors / funders
Employers
Community organizations
Governmental agencies
Accreditors
Competitors
Partners
Page 4
We serve many constituents
Who is your primary audience? Is there a secondary? Tertiary?
What decision or action will they need to make?
What data and background are needed for this decision or action?
What is their level of familiarity with the necessary data?
What is their general data literacy?
What visualizations do they often see?
What biases do they have?
Page 5
Consider & verify needs with key questions
These can be used irrespective of the targeted constituent group(s)
Data uses tend to differ across organization levels
Frontline & Managers
operations and tactics
internal focus
assign resources
efficiency & effectiveness
OKRs, measures
explore
Executives
strategies
external focus
allocate resources
organization health
KPIs
explain, recommend
Generalizations can help, but always verify needs
Data Visualization Tips & Tricks
Consider: perceptibility
Page 8
Alberto Cairo, adapted from
Cleveland and McGill
small multiples, e.g.
Consider: (im)perceptibility
Page 9
Alberto Cairo, adapted from
Cleveland and McGill
Use with Caution!
Humans struggle with
accurately perceiving
these methods of
encoding data. They
also are used less
frequently, so they are
less familiar.
Page 10
Consider: purpose
When you want to compare categories
Bars / columns are
super familiar. Go
horizontal with long
category names
Lollipops are easily
decoded and reduce
clutter
Stacked bars group on
a second variable. Avoid
too many categories
Dumbells group on a
second variable. Useful
for binary categories
Page 11
Consider: purpose
When you want to present trends / continuous variables
Lines are super familiar.
Don’t have too many,
otherwise use color
sparingly to highlight
Slopes are good for
showing change across
two points of time
Bumps show change in
rank over time. Can get
noisy very fast, so use
color intentionally
Page 12
Consider: purpose
When you want to demonstrate distribution
Histograms to show
proportion of data
across distribution. The
distro curve helps
differentiate from bars
Box and whiskers to
demonstrate summary
stats quickly: range,
median, IQR, outliers
Violins to demonstrate
summary stats quickly
and actual distribution
Establish FOCUS
Declutter by removing
tick marks, grids, data
labels, title, legend.
Basically, every default
element Microsoft adds
except the axes and
data itself.
Use gray so that
everything is pushed to
the background to start.
Received
Processed
Adapted from Nussbaumer Knaflic (2015)
Gain ATTENTION
Use preattentive
attributes to bring your
featured element out of
the background.
Add data labels for data
richness that will help
make your point.
Adapted from Nussbaumer Knaflic (2015)
Received
Processed
75
10
55
65
Page 15
Gain ATTENTION
Preattentive attributes that our brain automatically registers
Few (2018)
Provide EXPLANATION
Use headlines just like
a newspaper would.
These are your 5
second takeaways.
Add explainers to give
context to the data
labels you added, to
impactful trends, etc.
Adapted from Nussbaumer Knaflic (2015)
Received
Processed
Two employees quit in May and we did not
backfill due to budget constraints
Backfill 2 techs to improve responsiveness
Ticket volume over time
75
10
55
65
Service degraded with school start; we
now average 65 missed tickets/mo
Page 17
Received
Processed
Two employees quit in May and we did not
backfill due to budget constraints
Backfill 2 techs to improve responsiveness
Ticket volume over time
75
10
55
65
Service degraded with school start; we
now average 65 missed tickets/mo
Adapted from Nussbaumer Knaflic (2015)
Telling a Story with Data
Data paints a scene,
Narrating with precision,
Storytelling’s key.
-ChatGPT
Iterative Story Crafting Process
PLAIN TABLES / RAW OUTPUT / ALL NUMBERS
UGLY GRAPHS / INAPPROPRIATE VISUALS
SIMPLE GRAPHS / IMMATURE VISUALS
GOOD GRAPHS / APPROPRIATE VISUALS
DATA STORIES / COMPELLING VISUALS
DATA INFORMED CHANGE
CHANGE BASED ON GUT
Adapted from Nussbaumer-knaflic via
https://www.storytellingwithdata.com/
visualize
declutter
focus & words
tell a story
Compelling
Visually
Rooted
in Data
Guidance
Based
Narrative
TELL A
GREAT
STORY
Cote, 2021. Harvard Business Review
Guide Others:
Define a Clear
Storyline For
Attendees
Be Honest:
Set the Context
For The
Challenge
Be Bold:
Recommend
Actions
Connect The
Dots:
Present
Visuals With
Intention
Discriminate:
Choose Charts
That Are Easily
Consumable
Think Visually:
Incorporate
Context
Relevant
Photos/Videos
Be Thorough:
Use Context
Appropriate
Techniques
Go Deep: Use
Complete Data
Sets To Set
Foundation For
The Story
Be
Transparent:
Highlight
Unknown or
Missing Data
Equity-Minded Sense-Making and Analysis
The term "Equity-Mindedness" refers to the perspective or mode of thinking exhibited
by practitioners who call attention to patterns of inequity in student outcomes. These
practitioners are willing to take personal and institutional responsibility for the success
of their students, and critically reassess their own practices. It also requires that
practitioners be race-conscious and aware of the social and historical context of
exclusionary practices in American Higher Education.
Source: USC Center for Urban Education
Equity-Minded
Sense-Making
and Analysis
Equity-Minded Focus and Data Analysis
Focus
• Eliminate disparities experienced by
excluded, marginalized or
minoritized groups
• Prioritize institutional accountability,
not deficits in students, faculty and
staff
• Monitor the impact of institutional
practices, policies and processes
Data Analysis
Disaggregate by all groups
Explore intersectionality
Frame findings
• What is it about our culture, climate,
procedures, policies that better supports
certain groups?
Use qualitative data to complement
quantitative data
DATA WHY?
REFLECTION ACTION
Data Literacy
the ability to read, write and
communicate data in context, including
an understanding of data sources and
constructs, analytical methods and
techniques applied, and the ability to
describe the use case, application and
resulting value.
Gartner
definition of
Data Literacy
Source:
https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy
UAIR’s DEFINITION OF Data Literacy
Position-specific support of data competencies and data fluency.
≠
? Why?
Using Design Language
More Dashboard Guides
Dashboard to Guide Users to Content
Thorny Issue – Provisioning and Access
• Who can see what?
• Permissions based on roles? Based on data steward rules?
• How do you control it?
• Validated content vs anyone can publish
• Steps to gain access?
• Open? Training? Data Use Agreement?
Page 30
“Data without insights is
meaningless, and insights
without action are pointless”
Tomas Chamorro-Premuzic
https://hbr.org/2020/02/are-you-still-prioritizing-intuition-over-data
Questions?
Jeremy Anderson
Vice President of Learning Innovation, Analytics, & Technology
Bay Path University
jeanderson@baypath.edu
Krisztina Filep
Director of Operational Analytics, UAIR
University of Massachusetts Amherst
kfilep@umass.edu
Page 33

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Defining Constituents, Data Vizzes and Telling a Data Story

  • 1. Data Day: Telling Your Data Story Defining Constituents, Data Vizzes and Telling a Data Story Jeremy Anderson & Krisztina Filep
  • 2. Agenda • Defining Constituents • Data Visualization Tips & Tricks • Telling a Story with Data • Activity
  • 4. Students Faculty Staff Managers Leaders Board Alumni Parents / guardians Prospective students Donors / funders Employers Community organizations Governmental agencies Accreditors Competitors Partners Page 4 We serve many constituents Who is your primary audience? Is there a secondary? Tertiary?
  • 5. What decision or action will they need to make? What data and background are needed for this decision or action? What is their level of familiarity with the necessary data? What is their general data literacy? What visualizations do they often see? What biases do they have? Page 5 Consider & verify needs with key questions These can be used irrespective of the targeted constituent group(s)
  • 6. Data uses tend to differ across organization levels Frontline & Managers operations and tactics internal focus assign resources efficiency & effectiveness OKRs, measures explore Executives strategies external focus allocate resources organization health KPIs explain, recommend Generalizations can help, but always verify needs
  • 8. Consider: perceptibility Page 8 Alberto Cairo, adapted from Cleveland and McGill small multiples, e.g.
  • 9. Consider: (im)perceptibility Page 9 Alberto Cairo, adapted from Cleveland and McGill Use with Caution! Humans struggle with accurately perceiving these methods of encoding data. They also are used less frequently, so they are less familiar.
  • 10. Page 10 Consider: purpose When you want to compare categories Bars / columns are super familiar. Go horizontal with long category names Lollipops are easily decoded and reduce clutter Stacked bars group on a second variable. Avoid too many categories Dumbells group on a second variable. Useful for binary categories
  • 11. Page 11 Consider: purpose When you want to present trends / continuous variables Lines are super familiar. Don’t have too many, otherwise use color sparingly to highlight Slopes are good for showing change across two points of time Bumps show change in rank over time. Can get noisy very fast, so use color intentionally
  • 12. Page 12 Consider: purpose When you want to demonstrate distribution Histograms to show proportion of data across distribution. The distro curve helps differentiate from bars Box and whiskers to demonstrate summary stats quickly: range, median, IQR, outliers Violins to demonstrate summary stats quickly and actual distribution
  • 13. Establish FOCUS Declutter by removing tick marks, grids, data labels, title, legend. Basically, every default element Microsoft adds except the axes and data itself. Use gray so that everything is pushed to the background to start. Received Processed Adapted from Nussbaumer Knaflic (2015)
  • 14. Gain ATTENTION Use preattentive attributes to bring your featured element out of the background. Add data labels for data richness that will help make your point. Adapted from Nussbaumer Knaflic (2015) Received Processed 75 10 55 65
  • 15. Page 15 Gain ATTENTION Preattentive attributes that our brain automatically registers Few (2018)
  • 16. Provide EXPLANATION Use headlines just like a newspaper would. These are your 5 second takeaways. Add explainers to give context to the data labels you added, to impactful trends, etc. Adapted from Nussbaumer Knaflic (2015) Received Processed Two employees quit in May and we did not backfill due to budget constraints Backfill 2 techs to improve responsiveness Ticket volume over time 75 10 55 65 Service degraded with school start; we now average 65 missed tickets/mo
  • 17. Page 17 Received Processed Two employees quit in May and we did not backfill due to budget constraints Backfill 2 techs to improve responsiveness Ticket volume over time 75 10 55 65 Service degraded with school start; we now average 65 missed tickets/mo Adapted from Nussbaumer Knaflic (2015)
  • 18. Telling a Story with Data Data paints a scene, Narrating with precision, Storytelling’s key. -ChatGPT
  • 19. Iterative Story Crafting Process PLAIN TABLES / RAW OUTPUT / ALL NUMBERS UGLY GRAPHS / INAPPROPRIATE VISUALS SIMPLE GRAPHS / IMMATURE VISUALS GOOD GRAPHS / APPROPRIATE VISUALS DATA STORIES / COMPELLING VISUALS DATA INFORMED CHANGE CHANGE BASED ON GUT Adapted from Nussbaumer-knaflic via https://www.storytellingwithdata.com/ visualize declutter focus & words tell a story
  • 20. Compelling Visually Rooted in Data Guidance Based Narrative TELL A GREAT STORY Cote, 2021. Harvard Business Review Guide Others: Define a Clear Storyline For Attendees Be Honest: Set the Context For The Challenge Be Bold: Recommend Actions Connect The Dots: Present Visuals With Intention Discriminate: Choose Charts That Are Easily Consumable Think Visually: Incorporate Context Relevant Photos/Videos Be Thorough: Use Context Appropriate Techniques Go Deep: Use Complete Data Sets To Set Foundation For The Story Be Transparent: Highlight Unknown or Missing Data
  • 21. Equity-Minded Sense-Making and Analysis The term "Equity-Mindedness" refers to the perspective or mode of thinking exhibited by practitioners who call attention to patterns of inequity in student outcomes. These practitioners are willing to take personal and institutional responsibility for the success of their students, and critically reassess their own practices. It also requires that practitioners be race-conscious and aware of the social and historical context of exclusionary practices in American Higher Education. Source: USC Center for Urban Education Equity-Minded Sense-Making and Analysis
  • 22. Equity-Minded Focus and Data Analysis Focus • Eliminate disparities experienced by excluded, marginalized or minoritized groups • Prioritize institutional accountability, not deficits in students, faculty and staff • Monitor the impact of institutional practices, policies and processes Data Analysis Disaggregate by all groups Explore intersectionality Frame findings • What is it about our culture, climate, procedures, policies that better supports certain groups? Use qualitative data to complement quantitative data DATA WHY? REFLECTION ACTION
  • 24. the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value. Gartner definition of Data Literacy Source: https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy
  • 25. UAIR’s DEFINITION OF Data Literacy Position-specific support of data competencies and data fluency. ≠ ? Why?
  • 28. Dashboard to Guide Users to Content
  • 29. Thorny Issue – Provisioning and Access • Who can see what? • Permissions based on roles? Based on data steward rules? • How do you control it? • Validated content vs anyone can publish • Steps to gain access? • Open? Training? Data Use Agreement?
  • 31.
  • 32. “Data without insights is meaningless, and insights without action are pointless” Tomas Chamorro-Premuzic https://hbr.org/2020/02/are-you-still-prioritizing-intuition-over-data
  • 33. Questions? Jeremy Anderson Vice President of Learning Innovation, Analytics, & Technology Bay Path University jeanderson@baypath.edu Krisztina Filep Director of Operational Analytics, UAIR University of Massachusetts Amherst kfilep@umass.edu Page 33