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Data @ Work
Fueling good decisions in San Francisco
✓
Phases of Open Data in SF
Phase 1:
Phase 2:
Phase 3:
Phase 4:
Phase 5:
CENTRAL HYPOTHESIS:
Open data can help solve
our internal data challenges
Start with
problems…move to
opportunities
Ability
Access
We structure our goals to confront our challenges
Establish efficient
and effective data
governance
Make timely data
easily available
Increase use of data
in decision-making
…
…
…
Task: Update Data
Task: Obtain Data
See previous
Open data can address access barriers
We operate services to make open data timely, accessible
and usable
Our SF Building Explorer integrates 12+ open datasets
to provide a holistic view of a building
The data silo challenge
– fragmented data governance and stewardship
– complex, time-consuming data integration or sharing
– complex regulatory control and data compliance
– data management and standards missing or inconsistent
New Policy: Interdepartmental data must be exposed
as a service
…
…
…
Task: Renew DUA
Task: Create DUA
See previous
…
… …
X
Dept C
Dept A
Dept B
How to break from the DUA DEATH SPIRAL!!!
$
✓
Why align
…
…
What data?
Data Science and algorithmic
decision aides
Active performance
management
Endless, painful
reporting
Automated reporting
Benchmarks in data driven decision making
What data?
Data Science and algorithmic
decision aides
Active performance
management
Endless, painful
reporting
Automated reporting
Benchmarks in data driven decision making
Zachary
Kanin. The New
Yorker Collection/
The Cartoon Bank
Liberate employee capacity from the never-ending
reporting cycle
Dashboard Strategy: Reduce IT staff burden and migrate
to self-service
What data?
Data Science and algorithmic
decision aides
Active performance
management
Endless, painful
reporting
Automated reporting
Benchmarks in data driven decision making
TranStat
Performance
management only
works if you have
good metrics
.....................
Results based
accountability (RBA)
We apply this framework to all of our programs (and
publish it)
What data?
Data Science and algorithmic
decision aides
Active performance
management
Endless, painful
reporting
Automated reporting
Benchmarks in data driven decision making
What is data science?
Introducing data science can evoke a range of reactions
Whatever, you’re
just being trendy.
Get out of our
business. We’re
doing this already.
Resource hog.
This will solve
everything!!!!
The Truffle
Pig
Problem:
You know you have a truffle pig problem
if…
Can I have a
dashboard?
Can you build
a warehouse?
Can you
automate this
process?
s3
s2
s1
Solution:
The
project
typology
Find the needle in the haystack
Prioritize your backlog
Flag “stuff” early
AB test something
Optimize your resources
Some combination
Something else…
Service Issue:
Backlog is tackled via
first in, first out (FIFO)
What to
prioritize?
Data
Science
Service
Change
Data Science Process:
Create a model to
categorize and group
past and current cases
Service Change:
Prioritize cases based
on categories in order
of risk, need or
opportunity
Result: Department addresses high priority cases first
How it
works:
“Prioritize
your
backlog”
Increased client list 200% for commercial and 1000% for multi-family
properties and created a new leading indicator to help prioritize those leads
Able to predict with 83%
accuracy who will drop out of by
13 months of child age
Objective: Demonstrate power of data science
and eventually embed in culture and fabric
Develop Deliver Celebrate
To summarize….our work…
Data: the fuel for good decisions
(and new products)
Nothing is possible without a fantastic team.
Learn more at datasf.org & @datasf
THANK U@joybonaguro | medium.com/@joybonaguro
Head of People, Operations and Data | Corelight.com
Data, for the love of the City

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