The document discusses data strategy and analytics for a scalable medical practice. It outlines five levels of analytical capability that practices can achieve, from analytically impaired to analytical competitors. Critical success factors for analytics include ensuring high quality data through effective data management, strong leadership commitment, focusing initial targets narrowly before broadening scope, and employing both professional analysts and others within the practice. The presentation provides examples of data structure, uniqueness, integration, quality issues, and strategies for improving an organization's data maturity.

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Presented By
Date
Data Strategy for a
scalable practice
Venkata Narayanan
venkat@bigtappanalytics.com
19/ 03/ 2018

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Know where you want to go

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Levels of Analytical Capability*
* Competing on Analytics by Thomas Davenport & Jeanne Harris
Stage 1 –
Analytically
Impaired
Stage 2 –
Localized
Analytics
Stage 3 –
Analytical
Aspirations
Stage 4 –
Analytical
Companies
Stage 5 –
Analytical
Competitors
Goal is to get
accurate data
to improve
operations
Used to
improve
one or
more
functional
areas
Use
analytics to
improve
distinctive
capability
Build broad
analytic
capability-
analytics for
differentiation
Analytical
master-fully
competing
on analytics

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Critical Success Factors – Analytical DELTA
Data . . . . . . . . breadth, integration, quality
Enterprise . . . . . . . .approach to managing analytics
Leadership . . . . . . . . . . . . passion and commitment
Targets . . . . . . . . . . . first deep, then broad
Analysts . . . . . professionals and amateurs
* Adapted from : “Analytics at Work” by Thomas Davenport,
Jeanne Harris and Robert Morison

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Data
• Data is the pre-requisite for everything
related to analytics
• For data to be useful, it has to be "clean"
in terms of reliability and validity

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Data Management Puzzle
• Structure - Nature & Storage
• Uniqueness – What others don’t have
• Integration - Consolidation of Data
• Quality - Reliability of Data
• Access – Consumption of data
• Privacy - Protecting the Data
• Governance - Controlling the Processes

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Structure
PO Header
PO Line
Supplier
Part

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Uniqueness
• What differentiates you
• First mover
• Different perspectives
• Intelligence from internal data, moving beyond the norm
• New metrics
• Data Fusion
• What is unique to you
• Continuous Journey

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Integration
Source
External
Online

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Quality
Data
Gaps
Erroneous
Data
Disparate
Data
Duplicate
Data
Data
Inconsistencies
Blk 12, #01-01, Jalan Lempeng,
Singapore – 128789.
Incorrect postal code (correct code is
128798)
System 1: Blk 12, #01-01, Jalan Lempeng, S128789.
System 2: #01-01, Block 12 Jln Lempeng, SG – 128789
Same Address
System 1: Name - Simon Tan
Gender- M
System 2: Name – Tan Simon, Meng
Gender- Unknown
Same Customer
System 1: Name - Simon Tan
System 2: Name – Tan Simon, Meng
Same Customer
#40-00, 1 Raffles Place, Singapore
Postal code is missing

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Data Maturity
Inconsistent,
poor quality,
poorly
organized
Data
useable,
but in
functional
or process
silos
Organizati
on
beginning
to create
centralized
data
repository
Integrated,
accurate,
common
data in
central
warehouse
Relentless
search for
new data
and
metrics

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