Sutherland and International Institute for Analytics HIStalk Webinar - Charting a Course to Digital Transformation

W W W . T H E B R E A K A W A Y G R O U P . C O M
CHARTING A COURSE TO DIGITAL TRANSFORMATION –
START YOUR JOURNEY WITH A MAP AND COMPASS
December 7, 2016
PRESENTED BY

Sutherland Healthcare Solutions and International Institute for Analytics
Proprietary & Confidential
Today’s Presenters
2
Jack Phillips, CEO of International Institute for Analytics
• Noted advisor and writer on the impact business analytics and big data have on enterprises
• Edited the 2012 book Enterprise Analytics: Optimize Performance, Process and Decisions Through Big Data
• Focuses on how the adoption of data and analytics by certain firms leads to competitive differentiation and
higher performance
Dr. Graham Hughes, CEO of Sutherland Healthcare Solutions
• Physician with 30+ years of US and international experience leading teams in the development and delivery of
innovative healthcare IT products and services
• Has also held Chief Medical Officer and Head of Global Analytics responsibilities at SHS, GE Healthcare, and SAS
• Was responsible for strategic portfolio planning and leadership of the Center for Health Analytics and Insights, an
incubator group for creation of new advanced analytic offerings

Why We Became Partners For This Transformational Journey
3
To provide a consultative approach for our customers
who are embarking on their analytics journey
Leading provider of business transformation and
analytics services to the entire healthcare ecosystem
Leading authority on analytics maturity and best
practices working across a breadth of industries to
uncover actionable insights
+

Learning Objectives for Today
4
Leverage transformational design thinking methodologies to discover new
opportunities, optimize existing operations, and improve experiences
Measure and compare your organization's analytics maturity
Develop a strategy for leveraging analytics and design thinking as a
competitive differentiator

5
Clinical
Administrative
Financial
Patient Experience
Quality Measures ++
Supply Chain
Human Resources
Data Abundance

6
78% of Americans have a social media profile
162M Americans (over 50%) use Facebook at least once a month
59% of US adults looked online for health information this year
50+% of health costs tied to socio-demographic and behavioral attributes
Source: Stat 1, Stat 2, Stat 3, Stat 4
Patient /Consumer Data

7
From Piecemeal Solutions to Enterprise Thinking
Consulting
Design
Thinking
Platform
Solutions
Analytics

Your Map & Compass
Analytics Maturity
Analytics Journey
Analytics Strategy
8

Competing on analytics
is the inevitable future
IIA is the only research firm dedicated
exclusively to analytics
IIA provides the actionable insights and best
practices required to succeed in the new data
economy
IIA - The Authority on Analytics Maturity and Best Practices
• Co-founded by CEO Jack Phillips and Tom Davenport
with the belief that competing on analytics is difficult and
requires focused and leveraged expertise
• IIA has worked with 200+ organizations, providing best
practice, assessment, and consulting services, that
enable our clients to compete and win on analytics.
9
Tom Davenport
CO-FOUNDER, BOARD MEMBER,
FACULTY
Author of Competing on Analytics
Jack Phillips
CHIEF EXECUTIVE OFFICER
Editor of Enterprise Analytics
Robert Morison
LEAD FACULTY
Co-Author of Analytics at
Work
Daniel Magestro
VP & DIRECTOR OF RESEARCH
Previous VP / Director at
Nationwide, JPMorgan Chase,
Cardinal Health
Connect with IIA
www.iianalytics.com
503-467-0210
@iianalytics

VC Investment in Healthcare
10

VC Investment in Healthcare
11

Analytics Disruption in Healthcare - AI
12
90+ Startups Transforming
Healthcare With AI

Providence vs. Zoom+
Case Study
13

Providence
EST.
1855

EST.
1855
• Traditional healthcare
provider and regional
hospital system
• 3rd largest not-for-profit
provider in US
• Over 600 facilities in 5
states
• 82,000 employees
Providence

• Accepts Medicare,
Medicaid and private
insurance
• Offers urgent care
• Traditional access models

Zoom+
EST.
2006 2006 – Founded by two MDs
2015 – Business model update
and Zoom+ Super opening
2016 – 33 locations and
free chat care
2011 – Online scheduling and
Seattle expansion
2014 – App launch
2008 – Three locations
2010 – Seven locations
2012 – 15 locations in OR, WA, ID
2013 – 21 locations

• On-demand care, even for
patients without
insurance
• Efficient model reduces
costs
• Less insurance friction –
does not accept Medicare
and Medicaid
• Relationships with local
providers
• Digitally integrated

“Apple Store for Healthcare”• Aesthetics – “Designed
around consumer retail
experience.
• Vertical integration –
introduction of
insurance plan, wellness
programs, training,
dental, and more
• Segmentation – retail
approach, not traditional
“wholesale” approach

Streamlined
Digital
Experience
• Find a location and
schedule
appointments
• On-demand care –
Skype
appointments or
chat with doctors
• Access records and
refill prescriptions

Providence’s
Response
21

22
Power of the IIA Community
“How do I calculate (and defend) the value of
analytics projects, and investing in analytics broadly?”
“What are best practices in establishing analytics
professionals' career paths?”
“How do I get the best talent?”
“How do you manage the decision-making risk with
machine learning?”
“How have others built ‘modern’ analytics stacks and
why?”
“Is a cloud analytics environment really cheaper?”

23
The Analytics Challenge
Up to 70% of analytics projects fail to meet
objectives 1,2
Why? It’s NOT the technology; key reasons include:3,4
Disagreement on or lack of enterprise strategy
Corporate cultures that aren’t built around data
Insufficient domain knowledge
Organizational resistance
Lacking the right skills
$3B
Enterprise
Analytics
Spending 5
$10B
2015 2020
2 -1 - 3 -
4 - 5 -
$33B
Enterprise
Big Data
Spending 6
$60B
2015 2020
6 -

24
Developing Enterprise Analytics Capability
2007 2010
How?
• Adopt an unbiased framework for determining how groups
within an organization rate and prioritize analytics
capabilities
IIA Analytics Maturity Assessment (AMA)
• Uses 5-Stage Analytics Maturity Model and DELTA
Framework for assessing enterprise analytics capabilities
Analytics Maturity Assessment Goals
• Assess enterprise analytics maturity against frameworks
• Assess enterprise analytics maturity against other
industries including digital natives
• Provide actionable insight for improving analytics capability

25
5 Stages of Analytics Maturity
Analytical
Competitors
Analytical
Companies
Analytical
Aspirations
Localized Analytics
Analytically Impaired
STAGE 2STAGE 1 STAGE 5STAGE 4STAGE 3
Analytical Nirvana
Good at Analytics
See the Value of
Analytics
Use Reporting
Not Data-Driven
Established
Mainstream
Digital Native
Disruptors
Adapted from
Competing on Analytics
Davenport and Harris, 2007

26
5 Stages of Analytics Maturity
STAGE 2STAGE 1 STAGE 5STAGE 4STAGE 3
STAGE 5: ANALYTICAL COMPETITORS:
“Analytical Nirvana.” Use analytics across the enterprise as a competitive
differentiator and in strategy.
STAGE 4: ANALYTICAL COMPANIES:
“Good at Analytics.” Highly data oriented, have analytical tools, and make wide
use of analytics. Lack commitment to fully compete or use strategically.
STAGE 3: ANALYTICAL ASPIRATIONS:
“See the value of analytics.” Struggle to mobilize the organization and become
more analytical.
STAGE 2: LOCALIZED ANALYTICS:
“Use reporting.” Use of analytics or reporting is in functional or business
silos.
STAGE 1: ANALYTICAL IMPAIRED:
“Not data-driven.” Rely on gut feel and plan to keep doing so. Not asking
analytics questions and/or lack the data to answer them.
Adapted from
Competing on Analytics
Davenport and Harris, 2007

DELTA Framework
IIA’s Analytics Maturity Assessment measures an
organization’s performance using the DELTA model framework
D
E
L
T
A
DATA
ENTERPRISE
TARGETS
LEADERSHIP
ANALYSTS
BREADTH, INTEGRATION, QUALITY
APPROACH TO MANAGING ANALYTICS
PASSION AND COMMITMENT
FIRST DEEP THEN BROAD
PROFESSIONALS AND AMATEURS
DELTA = CHANGE
Adapted from Analytics at Work
Davenport, Morrison and
Harris, 2010
27

28
DELTA Competencies
0 42 6 8 10
Not important at All Extremely important
Not Effective at All Extremely effective
 Data capture
 Data quality
 Data integration
 Use of external data
 Data consistency
 Data trustworthiness
 Analytical tools
 “Big Data” utilization
 Enterprise tech
management
 Organization of talent
 Funding adequacy
 Non-management
utilization
 Data scalability
 External reporting
 Strategic input
 Executive advocacy
 Executive utilization
 Management utilization
 Enterprise collaboration
 Predictive modeling
 Goal setting
 Prioritization
 Iterative approach
 Opportunity identification
 Experimentation
 Staffing level
 Consultative approach
 Business skills
 Data science skills
 Career paths
D E L T ADATA ENTERPRISE TARGETSLEADERSHIP ANALYSTS
Adapted from Analytics at Work
Davenport, Morrison and
Harris, 2010

29
Making Analytics Maturity Scores Actionable
Shows which competencies have the greatest gaps between performance and importance and calculates opportunity
scores to show which competencies are the greatest opportunities for improvements.
OPPORTUNITY RANKING
IMPORTANCE
EFFECTIVENESS GAP
PRIORITY MATRIX

30
AMA Collected Data
Lowest Score Median
3.72
IIA ASSESSMENT
COHORT (N = 60+)
MARKET LEADERS
COHORT (N=75+)
ALL COMPANIES
(IIA ESTIMATE)
STAGE 1
1 to 1.99
STAGE 2
2 to 2.99
STAGE 3
3 to 3.99
STAGE 4
4 to 4.99
STAGE 5
5 to 5.99
Analytically
Impaired
Localized
Analytics
Analytical
Aspirations
Analytical
Companies
Analytical
Competitors
2.691.25
Highest Score
Lowest Score
1.73
Median
3.51 4.94
Highest Score

31
Targeted Market Leaders in ten
industries
• Sample Size: 515 Responses across 75 companies
• Functional Areas: IT/Systems/Analytics, Finance and
Sales/Marketing
• Minimum Data Requirements: At least 5 respondents
with 1 from each functional area
Modified Analytics Maturity
Assessment Model
• 17 of 30 DELTA competencies captured
Market Leaders Methodology
Respondent
Online B2B
PanelistsEmployed at
Participant Company
In Required
Position
With Required
Responsibility
Producer,
Enabler or User
of Data
Analytics

32
Market Leader Companies

33
2.89
2.96
3.05
3.14
3.19
3.23
3.43
3.55
3.56
3.62
3.91
4.56
Insurance
Healthcare - Insurance
Utilities & Telecom
Healthcare - Provider
Automotive
Airlines
Manufacturing
Pharma & Medical Devices
Retail
Consumer Brands
Financial Services
Digital Native
Market Leader Industry/Segment Rankings
Possible Scores:
1.00 to 5.99

34
2.89
2.96
3.05
3.14
3.19
3.23
3.43
3.55
3.56
3.62
3.91
4.56
Insurance
Healthcare - Insurance
Utilities & Telecom
Healthcare - Provider
Automotive
Airlines
Manufacturing
Pharma & Medical Devices
Retail
Consumer Brands
Financial Services
Digital Native
Market Leader Industry/Segment Rankings
Possible Scores:
1.00 to 5.99

3.12
3.75
3.08
3.43
3.63 3.61
4.06 3.96 4.05 4.05 4.09
4.94
2.35
1.73
2.97 2.86 2.89 2.89
1.95
2.89
3.31
3.19
3.51
4.07
0
1
2
3
4
5
Market Leader Industry/Segment Distributions
2.89 2.96 3.05 3.19 3.23 3.43 3.55 3.56 3.62 3.91 4.563.14
Stage 1
Impaired
(1-1.99)
Stage 2
Localized
(2-2.99)
Stage 3
Aspirations
(3-3.99)
Stage 4
Company
(4-4.99)
Stage 5
Competitor
(5-5.99)
35

36
 Key Characteristics of Digital Natives
• Experience Ratings
- No complacency > not standing still
• Overserved in some competencies
- Data Integration, executive utilization
• Underserved in investment competencies
- Data Capture, funding
 Highest score, on threshold of Stage 5
 Unique consistency of DELTA Component
Scores
Digital Natives
4.94 4.95 4.92 4.87 4.97 4.95
0
1
2
3
4
5
4.94
83% +52 +58
Must Maintain
Competitive Advantage
Competitive
Rating
Satisfaction
Rating
50–59 Good
60-69 Very Good
70+ Excellent

Digital
Natives
Effectiveness
Importance
Extreme
Opportunity
Solid
Opportunity
High
Opportunity
Appropriately
Served
Limited
Opportunity
1
2
3
4
5
6
7
8
9
Opportunity = Importance + Max (Importance – Effectiveness, 0)10
1 2 3 4 5 6 7 8 9 10
Table
Stakes
Underserved
Potentially
Overserved:
Data Integration
Executive Utilization
Limited Opportunities:
Data Capture
Funding Adequacy
Data
Enterprise
Leadership
Targets
Analysts
37

Pharma &
Med Devices
Effectiveness
Importance
Extreme
Opportunity
Solid
Opportunity
High
Opportunity
Appropriately
Served
Limited
Opportunity
1
2
3
4
5
6
7
8
9
1 2 3 4 5 6 7 8 9 10
Table
Stakes
Underserved
Data
Enterprise
Leadership
Targets
Analysts
Potentially Overserved:
“Big Data” Utilization
Solid Opportunities:
Data Integration
Analytical Tools
Analysts Business
Skills
38

Health
Insurance
Effectiveness
Importance
Extreme
Opportunity
Appropriately
Served
Limited
Opportunity
1
2
3
4
5
6
7
8
9
1 2 3 4 5 6 7 8 9 10
Table
Stakes
Underserved
Data
Enterprise
Leadership
Targets
Analysts
Potentially Overserved:
None
High Opportunities:
Data Quality
Funding Adequacy
Goal Setting
Prioritization
Opportunity
Identification
Solid Opportunities:
Data Capture
Data Trustworthiness
Analytical Tools
Data Scalability
39

Significant differences in analytics maturity
driving disruption in healthcare
High-performers continuously invest, and are
not slowing down
Maturity can be learned and repeated; not
just for the digital natives
Change management…not technology
management
Implications
1
2
4
3
40

41
Analytics Strategy Components
Phase 2
Opportunity
Definition
Phase 3
Roadmap
Phase 4
Governance
& Operations
Phase 1
Assessment /
Benchmark
Analytics Program Planning
4 Phases Designed to:
Discover and assess
Identify top opportunities
Plan and deploy
Operationalize
1
2
4
3

Our Analytics Solution Playbook
42

43
Next Steps – Get Your “Map and Compass” for the Digital Era
Objective Measurement of Analytics Maturity
Analytics Maturity
Baseline Report
Benchmarks
Recommendations
Advisory Services to Improve Analytics Maturity
Analytics Journey
Capability Evaluation
Strategic Roadmap
Business Case & ROI
Maximize the Value of your Information Assets
Analytics Strategy
Solution Design
Innovation Workshops
Insight as a Service

Questions?
44
Our Faculty Blog and Research Section
http://iianalytics.com/research
Our Analytics Section
http://www.sutherlandhealthcare.com/solutions/analytics/
Learn More

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