In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off. Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare. In this context, Dale will talk about: His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools

1. © 2015 Health Catalyst
www.healthcatalyst.com
April 2015
Dale Sanders
Microsoft: A Waking Giant In
Healthcare Analytics and Big Data
Creative Commons Copyright – Attribution Required

2. “In today’s world, business
moves at the speed of
software.” -- Dale Sanders
Great people and great facilities are not enough
anymore.
Software is the enabling or disabling factor to
success in today’s business world.
2
Why should C-levels care about
topics like this one?

3. For Example
Think about the impact that good and bad software is having on the
speed of these critical business transformations in healthcare, alone
• Healthcare.gov
• Population Health Management
• Accountable Care & Value Based Reimbursement
• EHR interoperability
• Detailed cost accounting
• Patient engagement
• Analytics at the point of care that can help raise
quality of care, lower cost of care, and speed
translational research
3

4. On a scale of 1-5, what is your overall perception of
Microsoft as a company– its people, products, and
culture? 307 respondents
1. Very negative – 3%
2. Negative – 10%
3. Neutral – 32%
4. Positive – 45%
5. Very positive – 11%
4
Poll Question

5. Agenda
• My up and down experiences with Microsoft
• Microsoft’s cultural and technological transformation
• Microsoft’s analytics options
• SQLServer, PDW, APS
• Microsoft Azure
• PowerX product line
• Data visualization and manipulation tools
5

6. © 2015 Health Catalyst
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My Life On Microsoft 
6

7. A few times, I wanted to poke my eye out 
7
Thank you for the cartoon, Darren Merinuk

8. © 2015 Health Catalyst
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Analytic Roadmaps for
Healthcare

9. 9
*-- Sanders D, Protti, D, Electronic Healthcare, 11(2) 2012: e5-e6

10. Poll Question
At what Level does your organization consistently
and broadly operate in the Analytics Adoption Model?
248 respondents
0 – 14%
1-2 – 34%
3-4 – 33%
5-6 – 14%
7-8 – 5%
10

11. “Closed Loop Analytics”
“Presenting data in the workflow of decision making,
such that the data optimizes the outcome of the
decision.” (Sanders, HIMSS 2015 )
Physicians are 15x more likely to modify their
decisions about patient orders and protocols if
presented with data at the point of care, as opposed
to presenting data in “offline” clinical quality
improvement meetings. (Komomoto, BMJ, 2007)
11

12. Embedded best practices,
decision support & care
team coordination that
support the Triple Aim
EHR embedded popula-
tion analytics tailored for
personalized medicine at
the point of care
EHR
Clinical Decision Support
EDW
Clinical Quality Analytics
Define clinical best practices
& requirements for embed-
ded decision support &
care team coordination
Use aggregate views of
clinical data for case mix
& protocol optimization
Derive population-based
health system models for
predicting demand
5
8
11
6
9
12
i ii
Executive & Clinical
Leadership
Create a cultural
expectation for evidence
based medicine and use of
clinical pathways &
standard protocols
10
Enterprise Clinical
Teams
Act on process & outcome
data using protocol-based
practice standards
Identify new cohorts &
gauge practice variations
Clinical, EHR &
Analytical Teams
Generate comparative &
outcomes data, implement
order sets, protocols and
decision support rules.
Develop & validate
clinical models 4
7
Start
Here
‘Closing the Loops’ on Clinical Outcomes to Optimize Quality
Using an Electronic Health Record, Enterprise Data Warehouse & Clinical Analytics to Generate Local Evidence
Information Systems
Supporting Data
Decisions & Actions
© 2015 Contributing authors, listed alphabetically: Eggert C, Moselle K, Protti D, Sanders
Align practice informed by analytics
Tailor protocols using better data
ManageServicesOptimizeCapacityDeliverCare
Loop A: Patients
Loop B: Protocols
External Evidence
Literature, Research
Other Data Sources
External, Financial
iv
InternalEvidence
InternalEvidence
EHR: Electronic Health Record
EDW: Enterprise Data Warehouse
MTTI: Mean time to improvement
SOPA: Span of providers affected
COptimize system on quality & cost
Loop C: Populations
InternalEvidence
iii
Confidential Draft
Mar 21, 2015
Previous Box 6: Assess data quality, cohorts
& interventions linked to outcomes
Include socio-economic
determinants of health in
clinical care management
best practices
CLINICAL QUALITY GOVERNANCE
Set improvement priorities 1

13. “Closed Loop Analytics”
Mean Time To Improvement and Span of Population Affected
Loop C: Populations
● MTTI: Years, decades
● SPA: Millions, several hundred thousand
● Analytic consumers: Board of Directors, executive leadership team,
Strategic plans and policy
Loop B: Protocols
● MTTI: Weeks, months
● SPA: Subsets of patients– hundreds, thousands
● Analytic consumers: Care improvement teams, clinical service lines
Loop A: Patients
● MTTI: Minutes, hours
● SPA: Individual patients
● Analytic consumers: Physicians and patients at the point of care
13

14. Big vs. Small Data
The ROI of data to Population Health
14

15. Volume, Ability, Act
The volume of data far outpaces our ability to
analyze and act on data… but we think otherwise
15

16. Finding optimal data volume
16

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Microsoft’s Cultural and
Technological Transformation
17

18. 18

19. The Innovator’s Dilemma
Microsoft has shown the repeated ability to overcome this…
19

20. Sacrificing the Sacred Cows
20

21. Microsoft Openness
21
• Over the last three years, the single largest contributor of code to Open Source
• 20% of Azure infrastructure runs on Linux
• .Net is now in the Open Source community
• Tight integration with Hadoop through Hortonworks
• Support for Dockers containers
• Microsoft owns 310 Android patents
• Supports Facebook’s Open Compute data center project
• Third largest contributor to the Linux kernel
Steve Ballmer, 2001: “Linux is a cancer”
Satya Nadella, 2014: “Microsoft loves Linux”

22. 22

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Microsoft’s Analytics Options
In The New World
23

24. The Transition Period
• Too early to go all-in on Hadoop & NoSQL
• Too late to go all-in on relational databases
• You have to straddle both and this is where Microsoft’s products and
strategy excel
24

25. Microsoft’s Analytics Product Lines
Lots of good, familiar patterns and integration across these products
PowerBI
Excel
PowerView
PowerM
PowerQ&A
PowerQuery
PowerPivot
The Future of Computing:
Azure
Hybrid Architecture:
Analytics Platform
Services (APS)
Old Reliable on Steroids:
Parallel Data Warehouse
(PDW)
Old Reliable: SQLServer
25

26. Price-Performance Numbers*
• EMC Greenplum
• IBM PureData
• Microsoft PDW
• Oracle Exadata
• Teradata Data Warehouse Appliance
26
* -- Thank you, Value Prism Consulting, Oct 2013

27. 27

28. 28

29. © 2015 Health Catalyst
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Hybrid Architecture:
Analytics Platform System
(APS)
29

30. Microsoft APS
(Analytics Platform System)
A brilliant hybrid architecture
30

31. Polybase Bridges The Skills Gap
31
Thank you John Kreisa, Hortonworks

32. HDInsight =
Hortonworks in Microsoft APS
32
Thank you, James Serra

33. Interactive Analytics Delivered To
Any Device

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Azure: The Future of Computing

35. What is Azure?
One of the key missing concepts in this definition is that Azure is a
hybrid cloud, meaning you can bridge data and applications between
on-premise and the Azure cloud.
As of April 11, 2015, there are 3,019 applications in the Azure
Marketplace. These are overwhelmingly business-level apps, not
consumer apps as we are accustomed to in the Apple and Google app
stores.

36. 36

37. Azure is Big and Mature
37

38. Huge Azure Infrastructure
 100+ datacenters
 One of the top 3 networks in the world (coverage, speed, connections)
 2x AWS and 6x Google number of offered regions
Operational Announced
Central
US
Iowa
West US
California
North
Europe
Ireland
East US
Virginia
East US 2
Virginia
US Gov
Virginia
NorthCentral
US
Illinois
US Gov
Iowa
South Central
US
Texas
Brazil
South
Sao Paulo
West Europe
Netherlands
China North
Beijing
China South
Shanghai
Japan
East
Saitama
Japan
West
Osaka
India
West
India
East
East Asia
Hong
Kong
SE Asia
Singapore
Australia
West
Melbourne
Australia
East
Sydney
38

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42. 42

43. 43

44. 44

45. Azure Security, Privacy,
Compliance
45
• ISO 27001/27002 Audit and Certification
• SOC 1/SSAE 16/ISAE 3402 and SOC 2
Attestations
• Cloud Security Alliance (CSA) Cloud Controls Matrix (CCM)
• Federal Risk and Authorization Management Program
(FedRAMP)
• Federal Information Security Management Act (FISMA)
• Federal Bureau of Investigation (FBI) Criminal Justice
Information Services (CJIS)
• Payment Card Industry (PCI) Data Security Standards
(DSS) Level 1
• United Kingdom G-Cloud OFFICIAL Accreditation
• Australian Government Information Security Registered
Assessors Program (IRAP)
• Multi-Tier Cloud Security Standard for Singapore
(MTCS SS 584:2013)
• HIPAA Business Associate Agreement (BAA)
• EU Model Clauses
• Food and Drug Administration 21 CFR Part 11
• Family Educational Rights and Privacy Act (FERPA)
• Federal Information Processing Standard (FIPS)
• Trusted Cloud Service Certification developed by China Cloud
Computing Promotion and Policy Forum (CCCPPF)
• Multi-Level Protection Scheme (MLPS)

46. The Visualization and Analysis Layer
Part desktop, part cloud, part mobile
46

47. Natural Language Queries
47

48. Closing Thoughts
• Business moves at the speed of software
• Older C-levels don’t generally grasp this… I’m old so I can say
that 
• I don’t impress easily when it comes to IT vendors,
especially Microsoft
• History will show that the new Microsoft is one of the
biggest cultural and technological re-toolings of all time
• Their hybrid “data lake” analytics and big data vision and
execution are unmatched
• Azure is the future of computing
• It’s going to completely disrupt organizational IT strategies and
the role of the CIO, in a good way
48