We'll start by considering how Nonprofits use data - toward what ends. Then we'll talk about how "data-people" do the things they do, i.e. what are the basic processes and tools common to any data project? Finally, we'll touch on some relevant technology trends, and consider one of today's hottest job titles: "Data Scientist"

1. © 2018 Epistemic
Nonprofits + Data:
Pathway to Innovation

2. © 2018 Epistemic
Motivation
TechSoup forum posts by category (as of 7/27/18)
Integration is important . . . It puts innovation within
reach!

3. © 2018 Epistemic
Agenda
● About Epistemic
● Some Technical Characteristics of a Data Project
● How Nonprofits Use Data
● How We Work with Data
● How We Implement Data Pipelines
● The Challenge of “Data Science”
● Questions?

4. © 2018 Epistemic
About Epistemic
Epistemic is a consultancy founded earlier this year to help nonprofits
apply the power of data science to some of the world’s pressing problems.
It is led by Jim Etkin, an IT architect/strategist and hands-on engineer with
long experience creating data infrastructures that drive decision making in
a variety of organizations.
epistemic [adjective] - relating to knowledge, the conditions for acquiring it,
and/or the degree of its validation

5. © 2018 Epistemic
Some Technical Characteristics of a Data Project
Quality Standardized & Complete vs.
Varied & Incomplete
Rigor Adhoc/Flexible vs.
Rigorous/Repeatable
Scale Desktop vs. Server
Structure Tabular vs. Complex
Analytic Output Descriptive > Diagnostic >
Predictive > Prescriptive
Your use case implies a great deal about the data you will work with, and
how you will store and process it.
There are many technical characteristics we could discuss here.
Let’s focus on these 5:

6. © 2018 Epistemic
How Nonprofits Use Data
As a tool for
driving
development
In direct
support of the
organization’s
mission
As a means of
demonstrating
impact

7. © 2018 Epistemic
How Nonprofits Use Data
Driving Development
Using data to help bring donor dollars into your organization by conceiving,
running and assessing outreach activities:
● Understand where your donations come from, segment your donor
base
● Personalize donor communications
● Identify new targets
Opportunities for innovation:
● External data sources
● Social media
● Geographic data

8. © 2018 Epistemic
How Nonprofits Use Data:
Supporting the Mission
Data Oriented
Mission
Action Oriented
Mission
Publication of data-based
reports and/or distribution
of original data products is
the mission of the
organization
Data and analytics help fine-
tune projects and guide the
application of the org’s
resources

9. © 2018 Epistemic
Jeff Beals was one of 7 people vying to be the Democratic candidate to
oppose John Faso in the race to represent New York’s 19th Congressional
District
Technologies: Python, QGIS, Google maps
Key points: Enriching CRM info with outside sources; using geodata
Example:
Beals for Congress
Other orgs’
Donor Lists

10. © 2018 Epistemic
Analysts working for the Shift Project assess corporate sustainability
practices against the UN’s Guiding Principles Reporting Framework
Technologies: Python, IBM Watson
Key points: ML can do amazing things, but you need the right roadmap
Example:
Shift Project

11. © 2018 Epistemic
How Nonprofits Use Data:
Measuring Impact
To manage your organization effectively, and to support your development
goals, you need to assess how efficiently you are executing your mission,
and show how much positive impact you are having
● Key performance indicators
● Assessment/demonstration of need
● Competitive positioning with regard to other orgs in the same “space”

12. © 2018 Epistemic
CAMBA provides holistic programs to under-served individuals, families and
communities addressing issues such as housing instability, living with
HIV/AIDS, language barriers, domestic violence, and insufficient job skills.
Key points: Infographics are the key to making a compelling case with data
Example:
CAMBA.org

13. © 2018 Epistemic
for each source
How We Work with Data:
Data Pipelines
Acquire
Explore
Refine
Analyze
Communicate
Orchestrate
Basic
● Ingest, save & publish
● CRUD (create, read, update, delete)
● Filter, aggregate & merge
● Do statistical calculations
● Produce reports & visualizations
● Schedule/run/monitor tasks
This “generic” data pipeline implies the
need for a set of technical capabilities:
Advanced
● Handle geo-data, produce maps
● Perform NLP
● ML (classification & regression)
● Scale massively

14. © 2018 Epistemic
How We Implement Data Pipelines:
What’s Your “Stack”? - Cloud + Open Source
Basic
● Ingest, save & publish
● CRUD
● Filter, aggregate &
merge
● Statistical calculations
● Reports & visualizations
● Tasks
Advanced
● Geo-data, maps
● Perform NLP
● Machine learning
● Scale massively
Python (open source)
● pandas
● matplotlib
● NLTK, SciKitLearn
● AirFlow
● Jupyter Notebook
● GeoPandas
Google Cloud Platform™
● App Engine
● Cloud Storage
● Maps
● Big Query
“Stack” - The set of hardware & software components and subcomponents used to
support an application(s)
Example

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The Challenge of Data Science: DS Venn Diagram
● Domain Expertise - detailed,
specialized knowledge of your field
● Computer Skills - designing and
building reliable solutions that mix
software, hardware and services
● Math/Statistics - the scientist’s
understanding of how to design
experiments and draw valid
conclusions from data
Computer
Skills
Math /
Statistics
Domain
Expertise
Data Science sits at the intersection of three skill sets:
DS

16. © 2018 Epistemic
MyGivingStory is part of the #GivingTuesday initiative. It published
individual’s stories and solicited monetary contributions. A team of data
scientists used NLP and ML to analyze 1,500 stories and model an optimal
“sentiment curve”.
Tools: AWS, Python, ScikitLearn, NLPToolkit
Key point: Output was a predictive model estimating likelihood of success
Example:
MyGivingStory
Pessimistic
Optimistic

17. © 2018 Epistemic
The Challenge of Data Science: The Talent Gap
Student Supply
23% of educators say all
graduates will have data
science and analytics skills
Employer Demand
69% of employers say
they will prefer job
candidates with these
skills over ones without
In 2016, the BHEF (Business-Higher Education Forum) commissioned Gallup
to conduct a survey focussing on data science and analytics education vs.
demand in a variety of industries . . .

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Questions…?