Presented by Karen Graham, Maddie Grant, and Jenn Taylor at Good Tech Fest on May 21, 2019

1. Data Maturity
Nine Lessons and Three
Assignments
Good Tech Fest
May 21, 2019

2. INTRODUCTION
Karen Graham
IDEALWARE EXPERT TRAINER
Director of Education &
Outreach, Tech Impact
karen@techimpact.org

3. Jenn Taylor
SYSTEMS ARCHITECT
Partner, Deep Why Design
jtaylor@deepwhydesign.com
INTRODUCTION

4. INTRODUCTION
Maddie Grant
DIGITAL STRATEGIST
Culture Designer, Human Workplaces
maddie@humanworkplaces.net

5. Can be found on the
course page!
What We’ll Cover Today
Introducing the Data Maturity
Spectrum (Karen)
Matching Tools to Your Level of Data
Maturity (Jenn)
Culture’s Intersection with Data
Maturity (Maddie)
Where Organizations Get Stuck
Breakout Discussions
Wrap-Up
INTRODUCTION

6. The Data Maturity
Model1

7. What Does Data
Maturity Mean?
Is it about big data?
Small data?
Tech tools?
Photo by greeblie used under CC license
DATA MATURITY MODEL

8. Lesson One: Data Maturity Is About Org Culture
“A data-informed organization is
one that collects and tracks
information about constituents—
from simple things, like contact
info or event attendance, to more
complex ideas, like the different
ways they interact with your
programs—and acts on what you
learn from that information.”
DATA MATURITY MODEL
Becoming a
Data-Informed
Organization,
2017

9. Idealware: Your Technology Resource
www.idealware.org
Proud to be a program of Tech Impact
DATAMATURITYMODEL

10. DATA MATURITY MODEL
Get the research report at
www.idealware.org

11. Getting
Started
Piloting
Establishing
Practices
Data-
Informed
Data-Centric
From Basic to Advanced
DATA MATURITY MODEL

12. Getting
Started
Piloting
Establishing
Practices
Data-
Informed
Data-Centric
Lesson Two: It’s a Developmental Model – Not a
Typology
Meaning…
DATA MATURITY MODEL

13. Stage 1: Getting
Started
You might be collecting
some basic data.
But things feel
disorganized and
inefficient.
DATA MATURITY MODEL

14. Stage 2: Piloting
Your data is in a
spreadsheet or database,
where you can sort it and
create reports.
You have begun to define
metrics and processes.
DATA MATURITY MODEL

15. Stage 3: Establish
Organization-Wide
Practices
The ED and most of the
staff supports expanded
use of data.
You have defined a logic
model and put someone
in charge of data.
DATA MATURITY MODEL

16. Stage 4: Data-
Informed
Data-driven decision
making is part of the
organization’s culture.
You take external
research and
transparency into
account.
DATA MATURITY MODEL

17. Stage 5: Data-Centric
Data is part of the
organization’s DNA.
Hypotheses are made
and tested to optimize
programs.
(This is the ultimate
stage—but it doesn’t
mean perfection.)
DATA MATURITY MODEL

18. Lesson Three: In
Order to Find Your
Way…
It helps to know where
you are starting from, and
where you are headed.
DATA MATURITY MODEL
This Photo by Unknown Author is
licensed under CC BY-ND

19. DATA MATURITY MODEL
Assignment One: Discover Your Maturity Level

20. Matching Technology
to Maturity2

21. Lesson Four: The Only Bad Choice is Misalignment
MATCHING TECHNOLOGY TO MATURITY

22. Breathe...
MATCHING TECHNOLOGY TO MATURITY

23. Lesson Four: The Only Bad Choice is Misalignment
MATCHING TECHNOLOGY TO MATURITY
Getting
Started
Piloting
Adventure! (and maybe some chaos)
• So much knowledge is in individual brains
• Almost everything is subject to change
• You're doing a lot of inventing and
deciding
Your software should be flexible – you're
not ready to build a house yet
• Individual productivity tools (MS Office,
Google Suite, Email, Online Calendars)
• Some purpose-built tools (bookkeeping)

24. Lesson Four: The Only Bad Choice is Misalignment
MATCHING TECHNOLOGY TO MATURITY
Establishing
Practices
Data
Informed
Building Expertise and Focus
• You have built up some expertise –
sometimes the hard way
• You can train and onboard new staff
with a lot less pain than a few years ago
• You rely on core reports to monitor
performance
Your software is sturdy and has some
structure – but isn't going to change
quickly or easily
• Purpose-built tools (donor
management, program management)
• Enterprise systems (flexible platforms
like Salesforce, MS Dynamics, ZoHo)

25. Lesson Four: The Only Bad Choice is Misalignment
MATCHING TECHNOLOGY TO MATURITY
Data
Centric
Data-driven Learning, Prediction, and
Adaptation
• You can easily answer the basic questions
with data
• You look to your data as much for "what if"
questions as for "what happened"
questions
Your software is now infrastructure - rules
and governance keep the peace and
stability enables growth and insight
• Enterprise systems and middleware
• BI and predictive analytics

26. Lesson Five: Thinking Like a Computer
MATCHING TECHNOLOGY TO MATURITY
“The first rule of any technology used in a business is that automation applied
to an efficient operation will magnify the efficiency. The second is that
automation applied to an inefficient operation will magnify the inefficiency.”
– Bill Gates

27. Lesson Five: Thinking Like a Computer
MATCHING TECHNOLOGY TO MATURITY
Getting
Started
Piloting
Phases 1& 2: Brains and Situations Define Your
Data
And that's OK – because you haven't fully figured it all
out yet!
You're not sure:
• If the sequence of steps to capture certain data is
always the same
• Who is making decisions about data – definitions,
when to enter it, how to report on it
• What data you really need to capture, or what
indicators you need to pay attention to
Stay flexible in phases 1 and 2.
This isn't the time for automation or
efficiency.
This is the time for being very hands-
on and asking great questions.

28. Lesson Five: Thinking Like a Computer
MATCHING TECHNOLOGY TO MATURITY
Establishing
Practices
Data
Informed
For Every Data-Generating Process, You Know:
1. The decisionmakers, data producers, and data
consumers
2. The set of steps that should occur, who should
do them, what data should be referenced (if
any) and what data should be captured
3. How the data relates to reports, the logic model,
management goals, or other key metrics
4. How reports are generated so that a consistent
understanding of information can be achieved
organization-wide
Phases 3 and 4 are where you begin
to define and document your rules and
establish common understanding and
norms around data.
While you don't want to stagnate, you
do want to focus on clarity and
consistency.
Check out the blog post "When Should You Automate in Salesforce" on the
501Partners web site for detailed steps to consider in thinking like a computer.

29. Lesson Six: Change is the Only Certainty
MATCHING TECHNOLOGY TO MATURITY
Document
the Path
Review it
Regularly
Take
Action
Seek Role
Clarity
Who can make decisions about this
part of the process?
Who influences and is impacted by
changes?
Where is responsibility or ownership
ambiguous?
What MUST we track and report on?
How do we capture the raw data that
is transformed into what we track and
report on?
Are the key activities written down?
What has changed in our: reporting
requirements, day-to-day activities,
technology?
Are those changes being fully
supported or utilized?
What are the gaps and barriers that we
should address to ensure full support
and utilization ?
Communicate changes to all
impacted stakeholders.
Seek stakeholder input and
expertise.
Ensure resources are committed to
executing on necessary changes.
Keep documentation up to date.

30. Your technology systems are the
embodiment of your
management decisions and
processes.
They support and enforce the
behaviors and actions you and
your clients see every day.
Care for them accordingly.
Assignment Two:
Start Practicing Good Change Management
MATCHING TECHNOLOGY TO MATURITY

31. The Role of Culture3

32. The Background: Developing a language to describe
and assess workplace culture

33. 2 Common Mistakes
Mistake #1 - Asking how people "feel" about the organization
Getting data on what people like and don’t like is not the same thing as identifying the
cause of a misalignment between individual and organizational success.
Mistake #2 – benchmarking against abstract models of an "ideal"
culture
Your organization is unique, so whether or not you need to meet the “standard”
is debatable.

34. Lesson Seven: Mature Organizations Measure
How Their Culture is Experienced – Not How
People Feel About it

35. Lesson Eight: Culture Data is About Patterns and
Identifying Gaps in Alignment

36. Lesson Eight: Culture Data is About Patterns and
Identifying Gaps in Alignment

37. Lesson Eight: Culture Data is About Patterns and
Gaps in Alignment

38. Lesson Eight: Culture Data is About Patterns and
Identifying Gaps in Alignment

39. Lesson Nine: Use Culture Data for Action

40. Assignment Three: Define What Your Culture IS
To get from A to B,
we need to know our
starting point.

41. Barriers to Data
Maturity4

42. What Gets in the Way
of Data Maturity?
Example:
Fear that data will reveal
the organization isn’t as
effective as it claims to
be.
What’s a possible solution
to this?
This Photo by Unknown Author is licensed under CC BY
BARRIERS

43. What Gets in the Way
of Data Maturity?
Example:
E.D. is very experienced
and trusts her
instincts/discounts the
data.
What’s a possible solution
to this?
This Photo by Unknown Author is licensed underCC BY-NC-ND
BARRIERS

44. What Gets in
the Way of
Data Maturity?
What are your
examples?
This Photo by Unknown Author is licensed under CC BY-NC-ND
BARRIERS

45. Discussion5

46. Discussion Questions
What similarities or differences did you notice between
the three speakers’ perspectives?
How do the nine lessons compare to your lived
experience?
DISCUSSION

47. Discussion Questions
What does this mean for your own organization?
What does it mean for the sector?
What might we do in response?
DISCUSSION

48. Nine Lessons
1. Data maturity is about organization culture.
2. It’s a developmental model – not a typology (you can
improve!)
3. Understanding your current level of maturity helps you
choose appropriate tools and practices.
4. With tools, the only bad choice is misalignment.
5. Think like a computer.
6. Change is the only certainty.
7. Mature organizations measure how their culture is
experienced
8. Culture data is about patterns and identifying gaps in
alignment
9. Use culture data for action
WRAP-UP

49. Additional Learning Resources
Finding Truth In Data
Nonprofit’s Guide to Data Migration
Nonprofit’s Guide to Managing Security Risk
Data Sanity for Nonprofits
Playing the Data Playbook
Recorded Course: Using Data to Transform Your
Organization
From Chorus America and WolfBrown:
Intrinsic Impact Audience Project
Workplace Culture Resources
WRAP-UP

50. Questions?

51. Acknowledgments
Maddie Grant, Jenn Taylor, and Karen Graham jointly
developed these materials with the help of their
colleagues.
Please feel free to share them in their original format
with attribution.
All images are used under a Creative Commons royalty-
free non-attribution license, except for speaker
headshots which were provided by the speakers.