To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don't know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents. In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.

1. Confidential. ©2018 DataRobot, Inc. – All rights reserved
Enabling the AI-Driven Enterprise
Designing a Successful
Governed Citizen Data
Science Strategy

2. Confidential. ©2018 DataRobot, Inc. – All rights reserved
Ron Powell
Editor and BeyeNETWORK Channel
Community Expert for DATAVERSITY
linkedin.com/in/ronaldpowell
BeyeNETWORK.com
Speaker
Jen Underwood
Sr. Director, DataRobot
@idigdata
linkedin.com/in/idigdata
jenunderwood.com

3. Agenda
Confidential. ©2018 DataRobot, Inc. – All rights reserved
1. Introduction
2. Role of the AI COE
3. Path to becoming AI-driven
4. Q&A

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“Decision making techniques and technologies to
automate it will be the next competitive battleground
for organizations. Those who are using business rules,
data mining, analytics and optimization today are the
shock troops of this next wave of business innovation.”
Tom Davenport, Competing on Analytics

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Competitive
advantage in the
algorithm economy
The AI gap is already
being exploited
as a game-changing
competency.
Source: Notes from the AI frontier: Modeling the impact of AI on the world economy

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AI Center of
Excellence
(COE)
Due to the role of AI, higher
level skills, and experience
needed, avoid scattered
“bottoms up” approaches
Enterprise decision making
tools need to be trustworthy
and sustainable

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❏ Lead AI vision and create a roadmap of prioritized use cases
❏ Provide AI technical architecture in partnership with IT
❏ Define AI tools standards, governance processes and ethics reviews
❏ Develop and monitor AI models in collaboration with IT and
business area leaders
❏ Orchestrate external innovation relationships
❏ Build and grow organizational AI maturity, talent and data literacy
❏ Foster an internal network of AI champions
Core AI COE Responsibilities

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3 steps to becoming AI-driven
2. Apply Automated
Data Science
1. Upskill Your
Existing Team
3. Collaborate with
Expert Mentors

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The world’s most advanced Enterprise Machine Learning Automation platform
2012
Founded, HQ in Boston, MA
$224M
In funding
800,000,000+
Models built on DataRobot Cloud
200+
Data Scientists & Engineers (of 500+)
4
#1 ranked Data Scientists
50+
Top 3 finishes
INSURANCE FINTECH HEALTHCARE MARKETING BANKING RETAIL MANUFACTURING MANY MORE

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DataRobot is a Visionary
DataRobot debuted furthest to the right
for ‘Completeness of Vision’ on the latest
Gartner Magic Quadrant for Data Science
and Machine Learning Platforms
“DataRobot sets the standard for
augmented data science and ML.”

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AI Opportunities in Single Division
Data Scientists
AI Opportunities in Single Division
Data Scientists + Citizen Data Scientists
UnrealizedPotentialScaling the AI-driven enterprise
Automation shifts team focus to delivering more business value, faster.

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Many different job titles
Data Engineer
Database Developer
ETL Developer
Systems Engineer
etc.
Many different job titles
BI Professional
Data Analyst
Business Analyst
Actuary
etc.
Common AI team roles
Executive
Sponsor
Data EngineerBusiness Analytics
Professional
Software
Developer
Data Scientist
AI projects rarely require participation from all roles. Citizen data scientists can do
most work with automation first data science, mentoring and IT help to deploy.
Citizen Data Scientists

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Stores scored model
results in database
Deploys governed models
in data / ETL pipelines
Shares predictions in
dashboards or apps
Business analytics
professional
Explores data for insights
Builds predictive models
DataRobot for enterprise business analytics
Delivering on the AI Promise with your existing team and tools

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Use governed predictive models
Example of DataRobot AI-powered dashboard with Tableau

15. $400MILLION
in annual savings from
better demand forecasts
<3MONTHS
achieved in less
than 3 months
+9.5%ACCURACY
DataRobot generated
higher quality models
Global Retailer
"We don't want our talent working on theoretical data science research projects.
We need to be ruthlessly practical at delivering value to the business."

16. $150MILLION
in annual revenue and
cost savings
<12MONTHS
achieved in under
12 months
1DIVISION
in one division of
the bank
Top Global Bank
“With DataRobot, results that formerly might take months to obtain by means of complicated
procedures can now be obtained in minutes” - Executive Director

17. <$875THOUSAND
used to be dismissed
since returns too small
<1WEEK
2 employees created
and deployed a model
95%ACCURACY
DataRobot instantly
ROI positive
TD Ameritrade
“DataRobot justifies its place by providing value and returning significant ROI immediately.”
Beaumont Vance, Head of Enterprise Analytics

18. $10MILLION
in annual cost savings
for 8 of 38 hospitals
<12MONTHS
achieved in under
12 months
0.1%REDUCTION
in patient
length of stay
Steward Healthcare
“DataRobot is very much a part of our growth strategy.” - Erin Sullivan, Executive Director of
Information Systems and Software Development

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Just add data and hit Start

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Built-in best practices,
warnings and guardrails
reduce human errors
DataRobot protections help
avoid AI gone wrong in ways
that may or may not be
immediately obvious
Guardrails prevent
common mistakes

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22. Confidential. ©2018 DataRobot, Inc. – All rights reserved
Empowering teams to deliver governed, responsible AI you can trust
Key capabilities for citizen data science
❏ Easy to get started yet deep and flexible for all levels of data science talent
❏ Best practices, warnings, and guardrails prevent common mistakes
❏ Automated data quality detection and issue handling
❏ Optimized feature engineering for generating high-quality models
❏ Full model transparency and compliance documentation
❏ Human-friendly model explanations, visualizations, rules, and text mining
❏ Built-in auditing, model health monitoring, and management
❏ Governed access for usage, APIs, and extensions
❏ Support for collaboration between team members

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Demo

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Fuse AI DNA into unique enterprise DNA
Democratization CollaborationSkeptic Single solution Automation-first
Data Science
AI-driven
$VALUE
#OFUSECASES
“AI will crush you!”
Ok, we’ll explore
one AI POC.
.
Wow. $20M from
one use case!?!
What else can we do?
Great. We’re delivering
far more value to
business, faster.
Let’s empower and
mentor more
data-savvy talent.
Success. We’re
changing the game
in our industry.
Our groups are working
together on AI projects
driving better outcomes.

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Phase 2 Phase 3
Phase 1
Training
Train users on DataRobot platform
Train executives to identify AI opportunities
Assistance with initial project
Enable users to execute pilot projects
Assist with data prep and results interpretation
Roadmap and Architecture
Prioritize use cases into a 3-year AI roadmap
Map organizational capabilities
Set up infrastructure and tools
Building the
AI-Driven
Enterprise

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Set up AI project best practices
Operationalize high-value models
Develop applications to consume predictions
AI change management processes
Explore other AI opportunities
Phase 2
Additional Training
Train new users
Train existing users on new features
Establish AI Project Best Practices
Define AI Change Management
Building the
AI-Driven
Enterprise

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Phase 3
Ongoing Mentoring
Ongoing mentoring and operationalization
As Needed Support
Offer engineering and data science assistance
as needed
Accountability
Set up and track business success metrics
Review progress, estimated and actual results
Building the
AI-Driven
Enterprise

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Contact us to explore a
use case
Try DataRobot with your
own data
Attend DataRobot
University
Next Steps
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Q&A