Over the past few years, Hennepin County has been exploring the use of graph databases for a variety of business problems. Chief among them has been integrating data to better understand how county programs and services function in concert. In this presentation, we'll share early insights and lessons learned from the development of a customer journey graph.

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Connecting the dots: Early insights from customer journey mapping with graphs
Chris Roberts MS, Tracy Bibelnieks PhD, Erik Erickson PhD
Hennepin County

3. Hennepin County
An outline of our story
• Who We Are
• Our Graph Journey
• Use Case: Entity Resolution
• Use Case: Client Journey Mapping
• What’s Next

4. Who We Are
Hennepin County
Hennepin County
• MN’s most populous county - 34th most
populous county in the United States
• ~1.26 million residents
• Seven lines of business (LOBs):
Disparity Reduction, Health, Human Services, Law
Safety and Justice, Operations, Public
Works, Resident Services

5. Hennepin County
Integrated
Data &
Analytics
Enterprise
Data
Services
GIS
Integrated Data and Analytics
Focus on cross-LOB and strategic
business initiatives.
Integrate data via a carefully
governed and highly secured
integrated data system to produce
summary insights.
Human
Services
Law,
Safety, &
Justice
Disparities
Reduction
Public
Works
Resident
Services
Operations
Health
Who We Are

6. Our Graph Journey
DataTech
MongoDB
CAL Pilot
COVID-19
Inflection Pt
Dan McCreary
gave a talk on
graphs at
Datatech 2019;
sparked our
interest
Internal pilot
leveraging
existing
resources: gremlin
was no fun
Worked with the
Carlson Analytics
Lab and did a
pilot project on
food insecurity
Leveraged graph
to incorporate a
wide array of
indicators for
situational
awareness
Currently working
to move beyond
successful POCs
and establish
graphs as part of
our data suite
Hennepin County

7. Current Use Cases
Hennepin County
• Entity Resolution
o Better entity resolution enables better summary insights
o Leveraging multidimensional and inconsistent data
• County Client Journey
o Identify client cohorts – Community/Similarity detection
o Identify critical programs and services – Centrality
o Identify client pathway patterns – Pathfinding
o Analyzing churn

8. Hennepin County
Entity
Resolution
Key Question
How does a graph-based approach to entity
resolution compare to our combined
deterministic and probabilistic approach in a
relational database?

9. PII
Hennepin County
Entity
Resolution
Leverage index-free
adjacency
Instance model
Person
Person
Reference
ER
Groups
Person nodes
are entirely the
result of the
entity resolution
process
Point index allows
fast location
comparisons

10. Hennepin County
Entity
Resolution
Key Learnings and Next Steps
• Missing/inconsistent
data fields
• Accounts for degree of
PII easily
• At least equals result
quality of our well-
honed tabular
approach.
• Graph approach scales
more effectively.

11. Hennepin County
County
Client
Journey
Key Question
How can we provide insight into common
patterns of involvement that may help
streamline services and make them more
effective?

12. Person-
Month
Hennepin County
County
Client
Journey
Person
Instance Model
Events
Event
Categories
Capture schemas in highly flexible
way.
Current data model allows for
different grains in the journey
mapping.
Natively capture relationships that
are challenging in a relational
setting

13. Hennepin County
County
Client
Journey
Initial Approach
Person-
Month
Person
Events
Event
Categories
Create a bipartite projection from
Person and Program type nodes.

14. Hennepin County
County
Client
Journey
Initial Approach
Person
Event
Categories
Create a bipartite projection from
Person and Program type nodes.
Apply Louvain to detect
communities.
Analyze communities to determine
common characteristics.

15. Hennepin County
County
Client
Journey
Initial Results
Visualize the volume of interactions
people have with the county
(administrative burden)
There isn’t one typical pattern of
involvement with county services
Month
nodes
Event
nodes
Program node
Cluster node

16. Hennepin County
County
Client
Journey
Key Learnings and Next Steps
• Store analytical results in the
graph!
• Iterate on your data model.
• Center your business questions in
your data modeling.

17. Summary
Hennepin County
Benefits – County Client Graph Data Science
• Graphs enables the use of inconsistent data in entity resolution
• Leverage both the data and relationships in data
• Better entity resolution enables better summary insights
Action – Leveraging the Power of Graphs for Public Good
• Operationalize insights to improve delivery of services and client
experience

18. Thank you to the IDA team!
Hennepin County
Bryan Felix, Data Scientist Mansoor Kahn, Data Engineer
Kimberly Mandery, Data Scientist Mark Knutson, Data Engineer
Jonathan Watkins, Data Scientist Alex Long, Data Engineer
Michael Soto, Data Scientist Susan Lee-Rife, Data Strategist
Tamra Boyce, Data Strategist