A Journey towards Self-Service Analytics

© 2022 Change Healthcare LLC and/or one of its subsidiaries. All Rights Reserved.
© 2022 Change Healthcare LLC and/or one of its subsidiaries. All Rights Reserved. PROPRIETARY & CONFIDENTIAL
Patrick Deglon, Ph.D.
Vice President, Engineering, Analytics Foundation & Services
Aug. 8, 2022; 4:20–5:05 p.m.
A JOURNEY TO
DEMOCRATIZE ANALYTICS

2
• Ph.D. in Particle Physics from CERN
• Director of Customer Analytics & Internet
Marketing Analytics with eBay
• Senior Director of Engineering, Analytics with
Motorola Mobility, Google Inc
• Vice President of Strategic Offering Management,
Advanced Analytics with Teradata
• Vice President of Engineering, Analytics
Foundation & Services at Change Healthcare
Vice President of Engineering, Analytics
Foundation & Services at Change
Healthcare
Dr. Patrick Deglon
Speaker Bio

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Why Did Mankind Invent Data 20,000 Years Ago?
https://www.wonderopolis.org/wonder/who-invented-numbers

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Ishango Bone, Democratic Republic of the Congo, Africa

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Maybe for counting assets (accounting)?
Ishango Bone, Democratic Republic of the Congo, Africa

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Usual Challenges in Data-Driven Decision-Making Across
Industries
Business Team Analytics Team IT Team
• Over-complexify the problem
• Insights are not actionable
• Solves the wrong problem
• Stalls on data and services
access
• Does not support custom
infrastructure and tools
• Abdicates business
operationalization
• Does not take part in solving
the problem
• Does not understand the results
• Does not implement
recommendations
• Poor data quality and latency
• New data flow takes too long
• Don’t authorize user data
Data/Ops Team

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Usual Challenges in Data-Driven Decision-Making Across
Industries
Business Team Analytics Team IT Team
• Over-complexify the problem
• Insights are not actionable
• Solves the wrong problem
• Stalls on data and services
access
• Does not support custom
infrastructure and tools
• Abdicates business
operationalization
• Does not take part in solving
the problem
• Does not understand the results
• Does not implement
recommendations
• Poor data quality and latency
• New data flow takes too long
• Don’t authorize user data
Data/Ops Team
• Remove “IT” & “Data/Ops” from the equation for 80% of the analytics projects
• Enable analytics and business teams to create and share analysis in a self-service manner
– Train the business in becoming citizen business analysts
– Train analytics team members to become citizen data scientists
– Raise data literacy across the company through 12 critical capabilities

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12 Capabilities to Democratize Analytics in the Enterprise
People Process
Platform
Data
• Training
• Community
• Analytics expert
• Control
• Self-service reporting
• Self-service querying
• Bring your own data
• Scheduling and monitoring
• My home
• Low-code/no-code data science
• Data sets for dummies
• Data catalog and
ownership

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9
People
Training
Business
Understanding
Data
Understanding
Data Preparation
Modeling
Evaluation
Deployment
Data
Cross-Industry Standard Process
for Data Mining (CRISP-DM)
1

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10
People
Training
Ask the
Right Questions
Get the
Right Data
Tell the
Right Story
1 2
4
Drive the
Right Insights
3
• Business Understanding • Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deployment
Business
Understanding
Data
Understanding
Data Preparation
Modeling
Evaluation
Deployment
Data
Analytics at Change Healthcare
Training Framework
1

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11
Training People
1
Providers of Insights
Data Engineer, Business Analyst,
Data Scientist, BI Developer, …
• Privacy and Security
• Healthcare 101
• Business Operation
• Statistics
• BI Tool Style Guide
• Advanced SQL
• Python and R
• …
Consumer of Insights
Marketing Manager, Sales Director,
Product Manager, VP of Legal, CxO, …
• Privacy and Security
• Data 101
• Analytics Frameworks
• Statistical Thinking
• Basic SQL-Light
• Introduction to BI Tool (low-
code/no-code)
• Data Storytelling
• …

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12
Community
Analytics Organization Types
Siloed
Analytics
Federated
Analytics
Hub and Spoke
Analytics
Centralized
Analytics
People
2

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Community
Analytics Organization Types
Siloed
Analytics
Federated
Analytics
Hub and Spoke
Analytics
Centralized
Analytics
People
2

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Community
Omni-Channel Analytics Community
Training
Social Forum
How-To
Articles
Analytics Summit
and Events
Q&A
Analytics
Expert
End
User
BI Stories and
Comments
Data
Catalog
People
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15
Analytics Expert (Internal Consultants)
End Users Analytics Expert
• 80% of the use case
• In’n’Out—within 2 hours to find
an answer by oneself
• Simple problem
• Sit in business units
• Example:
– Business result trends
– Campaign impact
– User/product list
– …
• 20% of the use case
• More than 1 day of work
• Complex problem
• Sit in the centralized analytics team
• Example:
– Customer segmentation
– Customer lifetime value
– Recommendation engine
– …
People
3

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Data Set for Dummies
Classical Query (3rd norm factor, normalized) Analytical Data Sets Query (denormalized)
• Simpler to read and understand
• Business logic encoded upstream
• Lower risk of double-counting (JOIN error)
• Faster to run (joins are optimized by IT
specialists)
Data
4

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Data Catalog and Ownership
Enterprise Data
Insights
Analytics
Platform
Reports
Fountain of Analytics
Data
5

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Enterprise Data Master
Data
Mgmt
Centralized Analytics Team
Data Custodian
Insights
Analytics
Platform
Reports
Data
5

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19
Enterprise Data
Business Analyst
Adhoc Data Provider
Master
Data
Mgmt
Data Custodian
Insights
Analytics
Platform
Reports
Data
5

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20
Enterprise Data
Business Analyst
Adhoc Data Provider
Business SME
Data Stewards
Master
Data
Mgmt
Data Custodian
Insights
Analytics
Platform
Reports
Data
5

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Enterprise Data
Business Analyst
Adhoc Data Provider
Business SME
Data Stewards
Master
Data
Mgmt
Data Custodian
Insights
Analytics
Platform
Reports
Business End User
Enterprise Data Owner
Data
5

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Self-Service Reporting
Enterprise Reports
• Built (or promoted) by a centralized IT team
• Searchable/browsable from main corporate portal
• 24-hour support with NOC playbooks from BI team (gold
reports)
• Training, style guides, coaching/feedback to help BI
developers build high-quality reports with standards
• Enable natural language, a la Alexa, to browse data sets
and see charts/tables (Alexa-like browsing versus Excel
pivoting)
• Use AI-BI convergence features to enable prescriptive
analytics to provide bullet points in English about:
– What is going on (outlier/trend detection)
– Why it is going on (automated root-cause detection)
– What we should do about it (fuzzy match into
knowledge base)
• Built by (trusted) community users
• Searchable/browsable from main corporate portal
• 1-business-day support level (silver reports)
• Best effort community support (bronze reports)
• Training, labs, and live Q&A forum to help non-
technical SME to build report by themselves
Community Reports
Data
6

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Self-Service Querying Tool
Low-Code Tool (light SQL users) No-Code Tool (drag-and-drop users)
• Browser-based SQL portal
• Simple and limited features
• Catalog of examples within the tool and other
knowledge-based system through deep link
(single click from) Data Catalog and Wiki
• “Nobody should write SQL from scratch”
• Simple single-click download into CSV and
Excel file
• Simple bookmark of queries with sharing
capabilities
• Training, labs, and live Q&A forum to help
non-technical SME to query data through SQL
• Drag-and-drop user experience
• No SQL needed
• Simple single-click download into CSV and
Excel file
• Simple bookmark of recipes with sharing
capabilities
• Training, labs, and live Q&A forum to help
non-technical SME to query data through drag
and drop
Platform
7

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Identify data source in a data catalog (Wiki, Alation, etc.)
1
2 Modify, run, and download in web editor
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3
Self-Service Querying Tool
Deep link to open query in web editor
Platform
7

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Low-Code/No-Code Data Science
Change Healthcare (LinkedIn Statistics)
• 48 Data Scientists
• 1,100+ Business Analysts
Data Scientist
Platform
8

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Change Healthcare (LinkedIn Statistics)
• 48 Data Scientists
• 1,100+ Business Analysts
Data Scientist
github.com/aws/amazon-sagemaker-examples
Data Scientist Notebook
Platform
8

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Low-Code No-Code
Example: Redshift ML Example: Amazon Sagemaker Canvas
Platform
8

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Low-Code No-Code
Did someone say
my name?
Example: Redshift ML Example: Amazon Sagemaker Canvas
Platform
8

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Bring Your Own Data
User Lists
She loads a list of
target emails into the
Self-Service Analytics
Platform
Emma, a marketing
analyst, wants to
understand the
performance of a recent
email campaign
Trusted Internal Users
She finds on the Data and
Analytics Data Catalog an
SQL example merging
Salesforce data with a
custom list
Knowledge Sharing
Time to Market
1 hour
versus 2+ weeks
going through an IT
request
She edits and runs that query
on the Self-Service Analytics
Platform and then clicks
“Saved as CSV” to download
the results by customer
Self-Service Querying
She analyzes the results
using pivot in Excel and
builds a deck summarizing
the findings and
recommending how to
improve the next
campaign
Preferred Analytics Tools
Build of Materials
• End-Users Training • User Sandbox for Her Data
• Self-Service Data Load
• Data Catalog
• Data and Analytics
Wiki
• Self-Service Analytics
Environment
• Browser-Based SQL Editor
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3
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5
Platform
9

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Enterprise Data Warehouse
Analyst’s
Sandbox
Bring Your Own Data
Analytics teams have access to
sandboxes within the
Enterprise Data Warehouse to
enable agile analytics:
• Enable to keep the “Single Point of Truth” philosophy
• Improved Time to Market — Days/weeks vs months
• Enable the business to do Agile prototyping
• Enable the users to “Fail Fast” — make it easy to try out new ideas
• Eliminate Isolated Data Marts
Platform
9

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Console—My Home
My Local Sync
Folder
(a la Dropbox)
My EDW Sandbox
(schema/folder
with R/W)
My Personal Bucket
(SMB, SFTP, S3, GCS,
Azure Blob)
My Team Bucket
(SMB, SFTP, S3, GCS,
Azure Blob)
API Connector for my data
(e.g., Weather Underground, Twitter
Search, Bing News Search, …)
My Scripts and
Notebooks Drag and Drop
Download/Upload
for my data Command Line
Download/Upload
for my data
(local and remote)
My Costs
(storage, compute)
Control usage of
my shared personal
and team data
My Stories,
Comments, Insights
My Personal and
Team
Tables/Views
Platform
10

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Scheduling and Monitoring
• Semi-controlled environment for the
query/measurement (e.g., notebooks, SQL/Python
code in Gitlab, “SQL” or “How To” sheet in Excel, …)
• Repository of clean analysis where unnecessary
lines of code (“wandering”) are removed (“scratch” vs
“production” folder in Notebook, Gitlab deployment)
• Self-service ETL tool to repeat refresh of a critical
team table (e.g., Windows Scheduler, CRON on a
Linux machine, Tableau Data Prep, …)
• Ability to chain jobs
• Get alerts when a job fails
Platform
11
Business
Understanding
Data
Understanding
Data Preparation
Modeling
Evaluation
Deployment
Data
“Rinse and
repeat”

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Control—How to Manage the Pandora Box?
Epimetheus opening Pandora's box
Self-Service Analytics
Process
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Control—Trust but Verify
Maximum Control
• All use-cases are lockdown
• Only a centralized IT team can have
access to the data and create report
• Least amount of risk
• Least amount of innovation
• Slow to react to change
• High certainty in outcome
Maximum Flexibility
• Any use-case is possible (historical
or new)
• Anybody can do anything on the
data
• Maximum amount of risk
• Maximum amount of innovation
• Fast reaction to change
• Low in outcome principle
Control
Flexibility
Process
12

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Control—Trust but Verify
Maximum Control
• All use-cases are lockdown
• Only a centralized IT team can have
access to the data and create report
• Least amount of risk
• Least amount of innovation
• Slow to react to change
• High certainty in outcome
Maximum Flexibility
• Any use-case is possible (historical
or new)
• Anybody can do anything on the
data
• Maximum amount of risk
• Maximum amount of innovation
• Fast reaction to change
• Low in outcome principle
Mixed Control
• Zero Trust Principle—No access by default;
but on successful training and authorization,
various access levels can be granted
• Control Certainty in Outcome (i.e., ask 5 people,
get 1 answer)—“gold badge” for enterprise
outcome, “bronze badge” for community outcome;
training and documentation
• Self-Service for day-to-day operations
• Browser-based tool (no extra tools)
• Enable Low-Code/No-Code
• New policies (e.g., each data slide has a data SME
and business SME name in the footer)
Control
Flexibility
Process
12

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12 Capabilities to Democratize Analytics
in the Enterprise
People Process
Platform
Data
• Training
• Community
• Analytics expert
• Control
• Self-service reporting
• Self-service querying
• Bring your own data
• Scheduling and monitoring
• My home
• Low-code/no-code data science
• Data sets for dummies
• Data catalog and
ownership

37
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A Journey towards Self-Service Analytics

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