9/13/2017
Creating a Data-Driven Decision-Making Culture
Ken Raetz Principal Think Data Insights, LLC
Who are we?
• 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 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 What should? What could? Prescriptive Predictive Diagnostic Why?
Descriptive What?
Descriptive Analytics
CentralizedCloud options Data andon Reportingdemand
Reduced cost Historical Only and complexity
Reduced cost Mass Distribution and complexity
Reduced cost Limited Alignment and complexity
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Diagnostic Analytics
CentralizedCloud options Data andon Reportingdemand
Reduced cost Cubes, Drill Down and complexity
Reduced cost Limited Experts and complexity
OneReduced Version cost of andthe complexity Truth
Predictive Analytics
Cloud options Forecasting on demand
Reduced cost Statistics Driven and complexity
Reduced cost Data Mining and complexity
IdentifyReduced Outliers cost andand complexity Patterns
Prescriptive Analytics
Reduced cost Data-Driven and complexity
AutomatedCloud options Data Integrationon demand
Reduced cost Machine Learning and complexity
Reduced cost Prescribe Actions and complexity
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The
opportunity is Diverse bigger than you data may think
New How? $1.6T analytics speed data dividend available to businesses that embrace data More over the next four years people
Data source: Microsoft and IDC, April 2014
Analytics Market Trends
Data monetization Cloud / Product Streaming/ real-time Complex data data and Modern data prep BI/Analytics Traditional Platform BI Spending Emerging $22.8b by Declining 2020
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Data Storytelling
• Know your objective • Know your audience Narrative • Highlight key findings Data • Eliminate fluff • Not all graphs alike • Simplify
Visuals
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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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What is Data Culture?
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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Poor 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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Changing your data culture
Perception Business- Owned IT Insights 20
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Functional Analytics… At Work
Commitment Teams Cloud Data Quality
Data veracity Data-driven Focus on Leverage cloud requires data decision-making innovation with for scalability and quality data flexibility commitment
New Data Sources Prototype Education
Short, targeted Educate Incorporate all data possible (external prototype organization on and internal) projects analytics
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Customer Examples – Healthcare Up to Sepsis Epidemic $24b Annual $88k spend Per Case 75%
Longer patient stay 23
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
Survival Rate By Hour Undiagnosed 90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0% 0 5 10 15 20 25 30 35 40 25
Customer Examples – Supply Chain
Lost revenue due to insufficient On-Time Delivery supply Demand Planning Supplier Performance
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Customer Examples – Supply Chain
Product Optimization
Customer/Geography Planning
Optimized Supplier Sourcing
Cost-Reducing Logistics Planning
Profit-driven inventory 27
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Customer Examples – Supply Chain
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Customer Examples – Finance
Capital Strategy
Customer Segments
Streamline
Processes Stock Price 29
Customer Examples – Finance
• Identify and limit high risk markets, customers Manage Risk • Leverage data in acquisition scenarios • Collection strategy for high-risk customers
• Reduce cost, increase revenue Streamline Processes • Use data to identify inefficiencies • Model approaches to optimize processes
• Data-driven manufacturing Capital Strategy • Tie capital to KPI/objectives
• Monitor historical/future events Stock Price • Manage key accounts that could affect stock price
• Optimize target marget acquisition Customer Segments • Model-driven customer segmentation • Identify new segments 30
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Decentralizing Analytics – The team of the future
Citizen Data Data Science Scientists Director/Manager
Data Storytellers Analytics Consultant 32
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)
Analytics – A New Role Citizen Data Scientist (aka “Data Wrangler”) • 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 36
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Creating a Data-Driven Decision-Making Culture
Ken Raetz Principal Think Data Insights, LLC
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