Overview of basic constituent analysis and data visualization considerations for telling data-rich stories in the higher education context. Presentation delivered at NERCOMP's 2024 Data Day.

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

3. Defining Constituents

4. Students
Faculty
Staff
Managers
Leaders
Board
Alumni
Parents / guardians
Prospective students
Donors / funders
Employers
Community organizations
Governmental agencies
Accreditors
Competitors
Partners
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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?
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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

7. Data Visualization Tips & Tricks

8. Consider: perceptibility
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Alberto Cairo, adapted from
Cleveland and McGill
small multiples, e.g.

9. Consider: (im)perceptibility
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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.

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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

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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

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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
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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
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65
Service degraded with school start; we
now average 65 missed tickets/mo

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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

23. Data Literacy

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?

26. Using Design Language

27. More Dashboard Guides

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?

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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
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