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9/13/2017
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Creating a Data-Driven Decision-Making Culture
Ken Raetz
Principal
Think Data Insights, LLC
• Data Platform architects
• Data Storytellers
• Microsoft SQL Server BI, Power BI
• Business Consultants – We see the pain
• IT Consultants – We know the challenges
• “Data Culture Disrupters” “Data Wranglers”
Who are we?
Who are you?
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Agenda
Our time today…
• Data Analytics Continuum – A History Lesson
• What’s happening today in analytics?
• Data Storytelling and Data Culture
• From Poor to Healthy Data Culture
• Focus on customer studies:
• Healthcare
• Supply Chain
• Finance
• Personal/Individual Strategy
A History Lesson
Descriptive
Diagnostic
Predictive
Prescriptive
Why?
What?
What could?What should?
Descriptive Analytics
Cloud options
on demand
Centralized Data
and Reporting
Reduced cost
and complexityHistorical Only
Reduced cost
and complexityMass Distribution
Reduced cost
and complexityLimited Alignment
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Diagnostic Analytics
Cloud options
on demand
Centralized Data
and Reporting
Reduced cost
and complexityCubes, Drill Down
Reduced cost
and complexityLimited Experts
Reduced cost
and complexity
One Version of
the Truth
Predictive Analytics
Cloud options
on demandForecasting
Reduced cost
and complexityStatistics Driven
Reduced cost
and complexityData Mining
Reduced cost
and complexity
Identify Outliers
and Patterns
Reduced cost
and complexityData-Driven
Prescriptive Analytics
Reduced cost
and complexityPrescribe Actions
Cloud options
on demand
Automated Data
Integration
Reduced cost
and complexityMachine Learning
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The opportunity is bigger than you may think
$1.6T data dividend available
to businesses that embrace data
over the next four years
speed
More people
New analytics
Diverse data
How?
Data source: Microsoft and IDC, April 2014
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Analytics Market Trends
$22.8b by
2020
Traditional
BI Spending Declining
Modern
BI/Analytics Platform
Emerging
Complex
data and data prep
Streaming/
real-time data
Cloud
Data
monetization / Product
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Data Storytelling
Data
Visuals
Narrative
• Know your objective
• Know your audience
• Highlight key findings
• Eliminate fluff
• Not all graphs alike
• Simplify
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Data Storytelling
• Know your objective
• Know your audience
• Highlight key findings
• Eliminate fluff
• Not all graphs alike
• Simplify
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Data Storytelling
• Know your objective
• Know your audience
• Highlight key findings
• Eliminate fluff
• Not all graphs alike
• Simplify
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Data Culture
A set of core beliefs and principles that determine how data will be
incorporated into key business processes.
The degree to which factual information is relied upon for decision-
making determines the healthiness and maturity of an organization’s
data culture.
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What is Data Culture?
• Assumption-driven decision-making
• Siloed reporting systems
• IT holds keys to sophisticated analytics
• Enterprise Data Warehouse with little self-service access
• Unreliable and inaccurate figures
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Poor Data Culture
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Perception
Business-
Owned
InsightsIT
Changing your data culture
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Education
Educate
organization on
analytics
Commitment
Data-driven
decision-making
Teams
Focus on
innovation with
data
New Data Sources
Incorporate all data possible (external
and internal)
Cloud
Leverage cloud
for scalability and
flexibility
Prototype
Short, targeted
prototype
projects
Data Quality
Data veracity
requires data
quality
commitment
Functional Analytics… At Work
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Customer Examples – Healthcare
Sepsis
Epidemic
Up to
$88kPer Case
75%
Longer patient stay
$24bAnnual
spend
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Customer Examples – Healthcare
Solution
• Early detection
• Use data to provide additional predictors of sepsis
• Arrival data
• Historic hospital data
• External data
• Notify delivery team at point-of-care for at-risk patients
Return on Investment?
• Costs lowered to $3,000 per case
• Early detection significantly reduces risk of fatality
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Customer Examples – Healthcare
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
0 5 10 15 20 25 30 35 40
Survival Rate By Hour Undiagnosed
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Customer Examples – Supply Chain
Lost revenue due
to insufficient
supply
Supplier
Performance
On-Time Delivery
Demand Planning
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Customer Examples – Supply Chain
Product Optimization
Customer/Geography Planning
Optimized Supplier Sourcing
Cost-Reducing Logistics Planning
Profit-driven inventory
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Customer Examples – Supply Chain
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Customer Examples – Finance
Streamline
Processes
Customer Segments
Capital Strategy
Stock Price
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• Identify and limit high risk markets, customers
• Leverage data in acquisition scenarios
• Collection strategy for high-risk customers
Manage Risk
• Reduce cost, increase revenue
• Use data to identify inefficiencies
• Model approaches to optimize processes
Streamline Processes
• Data-driven manufacturing
• Tie capital to KPI/objectivesCapital Strategy
• Monitor historical/future events
• Manage key accounts that could affect stock priceStock Price
• Optimize target marget acquisition
• Model-driven customer segmentation
• Identify new segments
Customer Segments
Customer Examples – Finance
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Decentralizing Analytics – The team of the future
Director/Manager
Analytics
Consultant
Data Science
Data
Storytellers
Citizen Data
Scientists
Citizen Data Scientist
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Citizen Data Scientist
“A person who creates or generates models that use
advanced diagnostic analytics or predictive and
prescriptive capabilities, but whose primary job
function is outside the field of statistics and analytics.”
Gartner
Individual/Personal Strategy
• Identify data opportunities (monetization, productize)
• Promote culture of Citizen Data Scientist with peers
• Engage business
• Learn about data quality
• Master the art of data storytelling
• Learn UX principles
• Learn business – MBA, books, videos
• Learn data science (school, online video)
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• Speaks business and IT
• Rapid solutions
• Growing in data science
• Adheres to standards
• Support data quality initiatives
• Trains/mentors others
• Promotes culture of Data
Democratization
Citizen Data Scientist (aka “Data Wrangler”)
Analytics – A New Role
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Creating a Data-Driven Decision-Making Culture
Ken Raetz
Principal
Think Data Insights, LLC