Have Data—Need Analysts. Lessons Learned From The Woodworking Industry

Have Data – Need Analysts.
Lessons Learned from the
Woodworking Industry.
John Wadsworth, Senior Vice President

© 2017 Health Catalyst
Proprietary and Confidential
I Fell in Love in the 7th Grade
• Woodshop class.
• Transformation of raw material into a finished product.
• The power of a plan and template.
• It’s also when I met my future wife.

So You Want to Do Woodworking?
The must-have tools:
• Jointer  square edge + flat face
• Planer  flatten board to thickness
• Table saw  rip board to width
• Miter saw  cut boards to length

A Premiere Woodworking Shop

Wood Shop Layout for Efficiency
• ‘Flow rate’ is a derivative of the shop layout.
• Each station requires skilled labor that understands its role relative to the
stations preceding and following it.
• Interoperability of the tools is a function of the layout.
• Shop layout is the result of years of experience and months of deliberate
planning.

The Analytic Ecosystem Is More Than Tools
• Tools: the technology used to capture, store, analyze, and present data.
• People: the technical staff doing analytics work in the organization.
• Skills: the proficiencies and understanding of tool operators.

• The must-have tools:
• Electronic Medical Record  Document care provided to patient
• Costing  Capture and track costs of care delivery
• Patient Satisfaction  Capture patient perspective of care experience
• Billing & AR  Bill and receive payment for care delivery
• Enterprise Data Warehouse  Tether clinical, operational, and
financial
So You Want to Do Healthcare Analytics?

Must-have Technical Skills and Context
• Data query.
• Data movement.
• Data modeling.
• Data analysis.
• Data visualization.
+
• Healthcare data capture
and flow of information.
• Contextual understanding
of what is measured in the
analytics provided.

Data Query - What and Why
• “The language of data relationships.”
• SQL allows you to explore the rich, relational models behind transaction
systems (where data is captured).
• SQL lets you move beyond pre-defined structures designed by the vendor
to answer questions THEY think you want answered.
• If you can access and manipulate data, you can own your destiny.
• DON’T outsource your fluency in data query to a vendor!
Structured Query Language (SQL)

Data Movement – What and Why
• Data movement has two objectives:
• INTEGRATION. It’s bringing together multiple data narratives to tell the right
story versus just data from one source (e.g. EMR + claims + costing).
• ACCESSIBILITY: To make actionable information accessible in the most
efficient manner
• The effort required for data movement is the largest source of waste and
frustration for the overwhelming majority of health care analysts. Set this
bar as low as possible.
Extract, Transform, Load (ETL)

Data Modeling – What and Why
Greater than 90% of data modeling by health care analysts is done in Excel.
• Custom formulas cobble manually extracted data for manipulation.
• Wasteful and risky.
• People leave roles and it’s difficult to trace the logical bread-crumb trail.
• Lack of transparency around the logic buried in tabs fosters secrecy and
breeds mistrust of information/analysis.
• Logic should be stored at the database level with read-only access for the
masses.
• Make logic visible to SMEs to refine your assumptions.
• Transparency of logic accelerates engagement from those that you are
measuring!
Model Real-World Concepts in a Database

Data Analysis - What and Why
• Analysis is KING.
• And, it has functional dependency on data query, data movement, and
data modeling skills.
• Meaningful analysis requires deep understanding of the processes being
measured.
• If you don’t have that understanding, pair up with a domain expert.
• Never provide analysis without deep, contextual understanding.
Turn Data Into Actionable Information

Data Visualization - What and Why
Visual representation of underlying data models and data analysis is how
99% of health care professionals interact with analytics.
• Data visualization is the conduit for non-technical people to connect with
your analytic ecosystem.
• Dashboards, KPI reports, and applications should surface findings derived
from data analysis in data models.
• Eye-candy is worthless. Visualizations are only as good as the
understanding AND trust of the underlying data. Never attempt to decouple
those.

Expert
Novice
0
1
2
3
4
5
Data Query Data Movement Data Modeling Data Analysis Data Visualization
Senior Mid Junior
Must-have Technical Skills for Healthcare Analysts

Analytic Streams of Interoperability
Answering anticipated questions. Single
vended system. Tells what happened.
Measures outcomes through custom data
models populated from multiple sources.
Highlights gaps between current state and
best practices.
Coupling of technical expertise and
domain knowledge. Illuminates the ‘why’
behind results. Discovers new paths for
future improvements.
Reactive
Descriptive
Prescriptive
AnalyticComplexity
Technical Skill + Contextual Understanding

Required Technical Skill by Analytic Work-Stream
0
1
2
3
4
5
Reactive Descriptive Prescriptive
Data Query Data Movement Data Modeling Data Analysis Data Vizualization
Expert
Novice

0
1
2
3
4
5
Expert
Novice
= Client Score
Skill
Capacity
Skill
Gap
Skill
Gap
Recent Health System Technical Assessment

0
1
2
3
4
5
Expert
Novice
= Assessed Client Skill
Excess skill capacity results in
the following behaviors:
• Disengaged, bored employees.
• Best employees leave.
• Staff can coast through the day.
• Leadership perception – efficient
staff with quick turnaround times.
Reactive Analytics and Mismatched Skill

0
1
2
3
4
5
Descriptive
Expert
Novice
Moderate skill deficiency results
in the following behaviors :
• Highly-stressed employees.
• Work-arounds.
• Leadership perception – inept
technical staff, “slow to deliver.”
• Data models  ‘Excel’ data sheets
rampant. Protective, secretive.
• Excessive time spent defending data
analysis against incongruous
reports.
Descriptive Analytics and Mismatched Skill

0
1
2
3
4
5
Prescriptive
Expert
Novice
Significant skill deficiency results in
the following behaviors :
• High employee turnover  re-orgs.
• Data models  Outsourcing to vendors
(typically EMR) with associated
professional services for customization.
• Consumers of analytics underwhelmed
by the lack of root-cause analysis &
subsequent recommendations for
improvement.
• System perceives outputs as academic
and expensive.
Prescriptive Analytics and Mismatched Skill

Analytic Ecosystem Layout for Efficiency
• “Analytic flow rate” is a derivative of analytic work stream layout.
• Each work stream requires skilled labor that understands its role relative
to the work streams surrounding it.
• Interoperability is a function of the tools and work stream layout.
• The layout should reflect years of experience and months of deliberate
planning.

Key Takeaways and Lessons Learned
• Understand the analytic total cost of ownership
• (TCO) = tool + operator + skill.
• EDWs and EHRs are not competitors; they’re complementary tools in your
analytic ecosystem.
• Don’t force your best analysts into management.
• Data analysis is more valuable than data query, data movement, data
modeling, and data visualization.
• Pay your best analysts like you pay your physicians.

A
23
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Answers

Have Data—Need Analysts. Lessons Learned From The Woodworking Industry

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