Workshop presentation for Texas Council on Family Violence Tech2Empower conference, August 28-30. Austin, TX

1. BIG MOUNTAIN DATA
Data-driven solutions to Intimate Partner Violence
Texas Council on Family Violence
Tech2Empower Workshop
August 29, 2017

2. Who am I?
● Susan Scrupski,
Entrepreneur
● 30-yr Career in
Technology
● Have “lived
experience” with
domestic violence

3. What is Big Mountain Data?
● An early stage social impact startup, founded Fall 2014
in Austin, TX !
● We focus on OFFENDERS and their CRIMES
associated with domestic violence
● We take a STEM approach to addressing Violence
Against Women
● We partner with leading edge technology companies
that align with our vision and goals

4. Domestic Violence is
An Invisible Crime... Until it Isn’t
“Abusers have two things
going for them:
1.) Anonymity and
2.) Lack of Accountability.”
“Transparency is the
antidote to a social epidemic
that thrives on secrecy.”

5. The CDC reports that 1 out of 4 women have experienced
severe physical violence from an intimate partner.
That’s 40 MILLION women in the U.S.
On the flipside of that equation, there are an equal number
of OFFENDERS. Even if we consider one offender stands to
abuse 1 - n number of women, the scale of the “problem”
still runs into the millions.
We have this data on offenders today. Further, we can
experiment with new models to merge “unstructured” data
with the structured data already in law enforcement
databases.
Domestic Violence
captures a lot of DATA
Today’s DV paradigm focuses nearly exclusively
on victim safety vs. holding offenders accountable
for their criminal acts of violence.

6. Domestic Violence is a
Local Problem
$$$$
solutions
*Domestic violence happens EVERYWHERE in the U.S. Outside of major urban areas, the
majority of police forces in the U.S. have fewer than 50 sworn officers.
Our approach: A community-led niche
“Long Tail” strategy. Town by town,
neighborhood by neighborhood.*

7. What can the data tell us?
● Who the most dangerous repeat offenders are and their criminal history
● When repeat offenders are more likely to commit an act of violence.
● How at-risk a victim is to being re-victimized by her abuser.
● The probability of whether a first-time offender is a good candidate for
behavioral change.
● Where domestic violence occurs (everywhere).

8. What data?
Data that already exists in law
enforcement systems.
● Calls for Service (Computer-
Aided Dispatch, CAD) 911
calls
● Arrest Data (Record
Management Systems)
● Incident Reports

9. But… the raw data is
sometimes not helpful

10. One simple correction

11. What can we do?
We can examine
existing datasets
and identify
meaningful patterns
in the data.
We can recommend
data-driven solutions
that can be field-
tested by law
enforcement and
county agencies.

12. Projects SF Hackathon

13. Testing our Thesis
Bayes Impact inaugural hackathon: November
15-16, 2014. Five teams tackled the High Point
PD challenge.
• Justice League - Location-aware app that
can discover if offenders that need a
preventative visitation is nearby
• Hack DV Offenders - Predictive tool that can
determine the likelihood that a person would
engage in severe domestic violence
• Will It Blend - Predictive analytics on who is
likely to engage in domestic violence
• To Arrest or Not to Arrest - Machine learning
tool to predict when an arrest should be
made
• All About the Bayes - Identifies the
addresses where domestic violence is most
likely to happen

14. Bayes Impact Hackathon
Example:

15. Projects First U.S. Data Dive

16. A response to the Obama
Administration's
President’s Task Force
on 21st Century Policing
14 of 74 of the key issues
identified had to do with data and
transparency
Orlando experimented with the
first community “data dive”
exploring domestic violence and
sexual assault data.
PDI Today:
129 Jurisdictions
Orlando Data Dive

17. Projects Hashtag Analysis

18. #WhyIStayed
#WhyILeft
An analysis of the social media
phenomenon that erupted over
the Ray Rice NFL scandal.
We analyzed 225K rows of data
to ask a simple question:
WHY DID THEY?
We published the results.
Then, we open sourced the data.

19. Projects MSDA Board, UCF

20. UCF Capstone
As a Board member to the
Master of Science in Data
Analytics program at
University of Central Florida
(UCF), we are designing a
graduate-level capstone
project.
Machine Learning, Predictive
Analytics, AI, Predictive
Modeling, Game Theory
Graduate Class: Spring 2018

21. Projects High Point Film

22. High Point 10-79 at #IACP2016

23. Offerings Data Analysis

24. Get your List!
With our partner, Superion, we
launched IPVO Tracker.
We identify and rank offenders
using data you already have in
your law enforcement systems.
It’s the FIRST STEP to holding
offenders accountable.

25. Offerings Killer App

26. SKORE Card
A cloud-based ubiquitous app
that enables law enforcement to
work seamlessly with Domestic
Violence advocates to properly
assess the real-time threat level
of each offender.
“This app could save an
officer’s life.”
– Mark Wynn,
International expert
on DV and policing

27. Finally… Thanks Mort!

28. “The Answers are in the Data”
susan@bigmountaindata.com
www.bigmountaindata.com
Facebook.com/bigmountaindata
@bigMdata
youtube.com/c/BigMountainData2015
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