Self-Service Analytics Product Initiative at Healthtrust
A comprehensive walkthrough of the Product Management approach from discovery to launch and beyond
Today's Journey
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5
1
Background & Problem Discovery
Background, Catalyst, Customer Research, Market Trends, & Strategy Drivers
2
Validation
Testing hypotheses and gathering feedback
3
Prioritization
Strategic alignment and resource allocation
4
Development
Cross-functional collaboration and execution
5
Launch & Measurement
Go-to-market strategy and success metrics
Background:
Understanding Healthtrust
Healthtrust is a Healthcare Group Purchasing Organization (GPO). They leverage collective buying power of their membership (ie. hospitals) to negotiate preferred pricing through extensive vendor contracts.
Win-Win: Membership is free, with a commitment from members to route 80% of their total spend through Healthtrust contracts. Healthtrust revenue is generated via 1-3% administrative fees on this spend, aiming to deliver 7-10% in cost savings for members.
Contracts
Member Savings
Admin Fees
Background:
Current State of Analytics
Problem Discovery & Drivers
Customer Research
Accelerate Discovery & Decision Making
Business Teams Doing Data Work
Slow IT Turn-Around
Poor Data Quality
Market Trends
Democratize Data / DaaS
Empower Business Teams with Tools
Move to Data-Driven Culture & Increase Data Literacy
Strategy Drivers
Empower Members with Data
Improve Operational Efficiencies
Significant Technology Investment
Not Fully Leveraging Available Data
Catalyst for Change
It all started with a "simple" request…
Finance Director…wanted 10-12 new Admin Fee reports.
Outdated Data Delivery
Monthly .CSV data extract onto a network drive.
Manual, Complex Processing
Finance team relied heavily on a series of complicated macros to transform raw data into actionable insights.
Request for "Canned" Reports
The core request was for me to build new "canned" reports.
There just had to be a better way!
Product Validation
Self-Service Analytics
01
Pilot Use Case
  • Admin Fee Analysis
02
Develop POC
  • Pull Data on Demand
  • Create visualizations
03
Get Feedback
  • Director of Finance
  • Use in Weekly Sync with CFO
04
MVP
  • Save/Access/Share Reports
  • User Access Restrictions
  • Performance
  • Automated Data Refresh
Product Vision
My vision was to transform the way data and insights were delivered to customers from traditional, centralized IT teams building & maintaining every report/dashboard to a self-service analytics model.
"If I had asked people what they wanted, they would have said faster horses."
— Henry Ford
Self-Service Analytics
Data Exploration
Navigate and investigate datasets independently, discovering hidden patterns, trends and outliers.
Data Visualization
Transforming complex data into intuitive charts, graphs, and dashboards.
Data Blending
Seamlessly integrate multiple, disparate data sources into a single view.
Data "Wrangling"
Data Preparation & Cleansing
Prioritization Framework
Analytics Steering Committee Purpose
Influx of Self-Service Analytics requests. We needed a strategy to guide future work. An Analytics Steering Committee was formally established, bringing together leaders from various departments.
Discussed Findings from Discovery & Market Trends
Analytics Best Practices
Review Backlog
Address Risks
My Role
  • Recommended Idea
  • Determine Attendees
  • Create Schedule & Agenda
  • Lead/Facilitate Meetings
  • Take Action on Outcomes
Prioritization Framework
Analytics Steering Committee Outcomes
Strategy Pillars
1
Focus on delivering internally while addressing data quality & organizational scaling issues.
2
Begin building new inSight™ immediately.
Release to Members when Data Quality is acceptable & Org. has scaled.
Aligned on Goals
Contract Gaps & Expansion
Add Item(s) to Contract
New Contract Categories
Contract Strategy
Member Savings Opportunities
Off-Contract Conversions
Tiered Pricing Goals
Clinical Analytics
Improve Outcomes
Maximize Reimbursements
Iterative Value Delivery
Aggregate Member Spend Data
Standardize & Enrich
Categorize
Partition Client Data
Item Cross Ref.
Pricing
Medicare & Medicaid Data
Case Data
Addressed Risks
Data Quality
Invest in New Technology Upstream
Data Governance & Stewardship
Scale the Organization
Business Solutions
Platform Training
Improve Data Literacy
Cross-Functional Alignment
Engineering Partnership
Developed roadmap & aligned priorities with Data Architects
Created & maintained backlog. Set sprint priorities. Participated in SCRUM activities.
UX Prototypes
Approved BUAT Plan & Results
Recommended restructuring of delivery teams to be cross-functional (Data & Analytics).
Coordinated with upstream teams to improve data quality (MDM, Spend Recognition, Categorization).
Retired Old Reports/Dashboards as they were replaced.
Stakeholder Communication
Bi-Weekly Sprint Reviews
Regular Updates to VP of IT Strategy
Regular 1:1 with Primary Stakeholders
Analytics Steering Committee
Quarterly Planning
Change Mgt.
Evangelize SSA
Wrote initial Data Governance Policies —> Handed Off to New Roles
Implemented initial Stewardship guidelines —> Handed Off to New Roles
Coordinated with VP of new dept. (Business Solutions)
Go-to-Market Launch (inSight™)
Launch Strategy
  • A/B Launch Approach
  • Comprehensive training materials, data dictionaries, support
  • Training Sessions for Account Reps.
  • Issue Reporting & Triage Process
Risk Mitigation
  • Escalation process for rapid response
  • Dedicated Production Support Dev Team
  • Comprehensive monitoring systems for rouge user queries & data refresh activities
Measuring Success
+10%
On-Contract Spend Coverage
6 mos.
Reduce Contract Cycle
15 —> 9 mos.
+100%
inSight™ Utilization
Members Utilizing inSight™ for Savings Opportunities
Critical Success Factors
Continuous user feedback, cross-functional collaboration, and organizational alignment.
Analytics are only as good as the quality of the underlying data.
Organization Change was critical for sustainable success