Program eval webinar final v2

Using data to improve and predict outcomes for
nonprofits
Alex Oftelie – Subject Matter Expert, Non Profit Business Analytics, IBM
Mario Gallegos – Director of Quality and Systems Improvement, McKinley Children’s
Center
© 2014 IBM Corporation

Agenda:
 Our point of view on Advanced
2
Analytics
 IBM Advanced Analytics Portfolio
in Action
 Advanced Analytics at McKinley
Children's Place
 Q&A

3
Our Point of View on
Advanced Analytics
© 2014 IBM Corporation 3

Why Program Evaluation?
4
Inform stakeholders on impact for
participants, strengths, and limitations
Discover problems or needs early to
prevent more serious problems later
Demonstrate ROI for grants and private gift
support

A focus on Predictive Analytics
5
Predictive Analytics helps connect data
to effective action by drawing reliable
conclusions about current conditions and
future events
Gareth Herschel, Research Director, Gartner Group

Advanced Analytics delivers ROI by upgrading operational
decisions
6
FROM TO
Evidence based
What you know
Proactive
Anticipate what´s
likely to happen
Dynamic
Automatically detect and
respond to changes
Based on “gut feel”
What you think you know
Reactive
Respond to
what has happened
Static
Long time to adjust to
changing circumstances

Key challenges uncovered
Difficult to deliver end-to-end constituent analytics
solution able to consume, integrate, analyze, score and
determine most appropriate action with individual
constituents
Inability to gather and synthesize insights from analysis
of social, text and interaction data to generate real-time
information to predict sentiment and needs
Incomplete view of constituent information at the time of
interaction, resulting in inappropriate or incomplete
offers, communications or both
Inconsistent service delivery and weak constituent
relationships, resulting in poor service or high churn
Lack of channel integration and siloed lines across
organizations, causing inconsistent or fractured
constituent interactions
Focus on uncoordinated messaging offers — scattered
one-hit selling, instead of broader strategic message
Challenged in using analytics to add short-term value
or enhance long-term strategy
7

Predictive Analytics in 3 steps
8
Align
Align your organization
around information
Manage, integrate and govern
both traditional and big data
information sources to create a
foundation for analytics
Anticipate
See, predict, and shape
Organizational outcomes
• Understand, at all times, what is
happening & why
• Look forward to model & predict
what could be happening
Act
Act with confidence
at point of impact
• Embed analytics into key
organizational processes
• Empower a culture of data-driven
decision making
Transform
Learn
Capabilities include:
Data collection, statistical analysis, data mining, predictive modeling, text analysis, decision management

9
Analytics in K12

IBM SPSS Predictive Analytics for Student Performance
10
Student
Information
Systems
Learning
Management
Systems
Evaluations...
At-Risk Students
Indicators of Performance
Recommended
Intervention
 Principals
 School Boards
 Departments of
Education
Predictive Scores & Prescriptive
Recommendations
Scorecards Reports &
Analysis
Dashboards At-Risk
Notification
Predictive Models
 Early Literacy
 Graduation Rate
 Student Performance
 Curriculum
Evaluation…
Business
Analytics
Full view of
students
Results
STUDENT
SUCCESS
1. Student back
on track
2. Dropout
avoided
3. Visibility,
transparency
Align Anticipate Act
Data mining & statistical analysis
uncovers hidden patterns &
associations within structured &
unstructured data
Feedback Loop: outcomes fed back as additional data source for increased accuracy

Business scenario: Lower graduation rates
District Superintendent
Sees improvement in the
graduation and drop-out rates
of the school and begins to
implement a district-wide
program.
11
Principal
“Our graduation rate of 69% is lower
than the average for a all other high
schools in our district of our size.
We must do something to address
this now.”
Student Guidance Counselor
“We are now able to better
identify those students at-risk
and get to them before the
situation escalates. This helping
to reduce drop-outs which was
impacting our graduation rates.”
Chief Financial Officer
“Predictive analytics has helped to
identify those students at risk before
it becomes an issue. Once
identified, we can put in place a
personal intervention strategy.”
Detect a
problem
Analyze the
situation
and build
models
Deploy
analytics to
solve the
problem
Monitor
improved
outcomes

IBM SPSS Predictive Analytics for Student Performance:
Empowering decision-makers
effectively gain deep
insight into student
12
Teachers
Can proactively implement
personalized education plans
for at-risk students
Principals
Can determine, improve
and implement solutions
for low-performing
students before funding is
impacted
Budgeting and Planning
Can actively contend for grants
and incentive programs for
tangible progress in improved
graduation rates
Analysts
Can efficiently &
performance
School superintendents
Can predict district performance
and track progress to ensure
schools are meeting
requirements for graduation rates

13
Capabilities Overview

The Modeling Approach is Different, but very useful
Modeling approach
14
Statistics approach
The statistics approach involves:
• Forming a theory about a
possible relationship
• Converting it to a hypothesis
• Testing that hypothesis using
statistical methods
It is a manual, user-driven, top-down
approach to data analysis.
The modeling approach involves:
• Interrogating the data
• Determining data by the
method and goal, rather than
by the user
It is a data-driven, self-organizing,
bottom-up
approach to data analysis that
works on very large data sets.

15
Classification
(or prediction)
Algorithms Usage
Autoclassifiers,
decision trees,
logistics, support
vector machines
and time series
Predict group membership
(e.g., will this student
graduate on time?)
or a number
(e.g., how many students
will struggle?)
Segmentation
Autoclusters
and K-Means
Anomaly detection
Classify data points into groups
that are internally homogenous
and externally heterogeneous
Identify cases that are unusual
Association
Apriori, CARMA
and Sequence
Find events that occur
together or in a sequence
(e.g., a personalized educational
plan for at-risk students)
Full breadth of predictive techniques

Text Analytics
16
Identify the context
and sentiment of
the text
Add Structure
to
Unstructured
Text

17
Frontline staff and systems
benefit from recommendations,
offers and dashboards—
wherever they are needed.
Optimize decisions, predictions, and rules
• Rules
• Predictive
analytics
• Simulation and
optimization
• Scoring
Streaming data
Textual data
Applications data
Time series
Geotemporal
and geospatial
Relational
Social networks

McKinley Children’s Center
 Founded in 1900, McKinley serves over 600 children and
families in the community offering the following services.
19
 Special Education Services to both community and
children living in the center
 Foster Care/Adoptions- Over 300 children, and 150
families
 Mental Health- Over 200 children and families in the
community
 Residential Care- We serve over 40 children.
 McKinley’s mission is to provide a safe environment for
children that need help, and to provide services that will
improve our communities by offering:
 Loving and stable homes for children
 Rehabilitative and Psychological services
 Case Management services

Analytics at McKinley
 Measure outcomes and understand the make up of
the population we serve.
 How can we improve those outcomes using data?
 Access to data was key
20
 Centralizing the data
 The ability to manipulate the data
 The ability to interact with the Data
 The Ability to visualize data
 The ability to set up systems that make the process
of data collection and data analysis easy for the
business user.

 McKinley decided to partner up with IBM because of their reputation for excellence.
 The solution components offered by IBM fit our current needs for data analysis and
reporting.
 The solution components are user friendly for the business user.
 IBM has been supportive through this process.
 IBM introduced us to a premier partner DATA 41 which helped us set up the
infrastructure for our data.
McKinley and IBM
21
®

 McKinley Children’s Center models and identifies variables that affect
permanency, helping improve child outcomes and program success
 Solution Components
22
Business Challenge: McKinley Children’s Center sought to better understand the many
variables, such as age, ethnicity and types of serious incidents, that can affect a child’s
permanency. However, caseworkers used paper and pen to record data, plus manually
collected data from external databases, resulting in a highly fragmented view of each child’s
needs. The problem was not lack of information but rather an inability to access and analyze it in
an efficient and constructive manner.
The Smarter Solution: The center deployed a big data and analytics system that collects and
aggregates near-real-time data from disparate databases, giving caseworkers a comprehensive
view of each child’s profile plus an unprecedented understanding of how different social and
home variables affect that child’s success. Using modeling and predictive analytics,
caseworkers can uncover hidden patterns and relationships and use the insight to determine the
right combination of services for each child, identify risk factors, match children with adoptive
families, and speed progress toward an optimal outcome.
Most children receive services from multiple agencies. The solution enables data sharing with
other organizations, giving kids the best opportunity for success.
—Executive, McKinley Children’s Center
99% reduction
in data collection time, from two
months to minutes, helping staff
spot trends in serious incidents
Pinpoints variables
that affect positive outcomes,
helping ensure a better foster
home experience
Identifies at-risk kids
helping caseworkers reduce
frequency of serious incidents
• IBM® SPSS® Modeler
• IBM SPSS Statistics
• IBM Cognos® Business Intelligence
• IBM Business Partner Data41

23
Resources
SPSS Non Profit Resource Center
ibm.biz/smarterphilanthropy
Next Steps
Conversation
Alexander Oftelie
aoftelie@us.ibm.com
www.linkedin.com/in/alexoftelie/
Office: 303-353-7306

Questions
24

Program eval webinar final v2

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