From Foundation to Mastery – Building a Mature Analytics Roadmap - Manav Misra

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From Foundation to Mastery
Building a Mature Analytics Roadmap
Manav Misra
Chief Data and Analytics Officer, Regions Bank
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The opinions expressed in the presentation are statements of the speaker’s opinion, are intended only for informational
purposes, and are not formal opinions of, nor binding on Regions Bank, its parent company, Regions Financial Corporation
and their subsidiaries, and any representation to the contrary is expressly disclaimed.

2. Emerging Themes in Financial Services
2
A
Paradigm Shift
CDO to
CDAO
Artificial Intelligence Journey to the Cloud Data Privacy & GDPR
Defense to
Offense
Consumer to
Consumer + Commercial + Wealth

3. Typical Challenges in FS Data & Analytics Landscapes
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• Knowledge base that allows sharing of data, and
guidance on best practices
• Each group has built its own data assets and analytics
team. Limited sharing of data assets across the
organization à inefficient & costly
Desired OutcomesChallenge
• Provide a common, standardized data science platform
with strong governance, leading to more accurate,
consistent and actionable intelligence
• Limited use of best practices around data and analytics;
hard to consistently modernize practices across the
organization
• Lack of consistent definitions of data elements across
disparate data marts
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3
• Small analytics teams provide restricted career paths
and mentoring, hard to create a broader sense of data
science community and therefore retain talent
• Centralize resources into a Center of Excellence and
then leverage resources from the COE to solve business
problems
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• Lack of easy & secure access to governed data for self
service analytics
• Limits governance on how data are used, hard to ensure
data quality and reliability
• Create self service framework with easy access to data
that allows for standardization and ability to customize
analytics
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4. From Foundation to Mastery – Building a Mature Analytics Roadmap
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Strategy and
Leadership
Team
Culture
Data
Products
Data
Management
Data Science
Platform
Steps in Creating a Data-Driven Organization

5. 1. Strategy and Leadership Team
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§ Ability to create
bridge between
business units and
data & analytics
§ Understand business
requirements
§ Build business cases
§ Create the roadmap
of deliverable data
products
§ Lead teams to deliver
data products
Captain of the Bridge The Logical Brain Data The Engineer The Navigator
§ Provides guidance on
Data Science
(Predictive AI, ML)
§ How to solve
problems, address
issues
§ Encyclopedic
knowledge of
advanced analytics
algorithms
§ How to use them to
solve business
problems
§ Deep knowledge of
the data in the org
§ Builds the structure &
controls to govern
data
§ Facilitates knowledge
sharing on how the
data is being used
across the Enterprise
§ Fosters sharing of
data assets across the
Enterprise
§ Technical wizard who
knows the data
science platform in
and out
§ Provides architectural
guidance to create
the data science
platform
§ Resourceful and the
one to go to when
you need data
architecture
guidance
§ Visualization expert
who can help a
business user choose
the right type of
visualization
§ Enhance the
effectiveness of data-
driven insight
§ Knows the tools, but
more so, the
visualization
techniques to best
depict specific results
Data Products Data Science / AI Data Management Data Engineering Visualization

6. DATA &
Analytics
Product
Discipline
Data products are value-based systems whose primary objective is to use data to facilitate end goal
“ - DJ Patil (Former Chief Data Scientists of the United States)
“
2. Data Products

7. Live Traffic
Route-Optimizations
Intelligent Rebookings
Pricing Optimization
Fraud Analytics
Commercial Analytics
ROSIE
Movie Recommendations
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Intuitive, Self Guided
UI Experience
Insights powered by
data and analytics
Recommendation
Based Alert/Action
Continuous learning
and closed feedback
loop
Multi-disciplinary Skills Needed
2. Data Products
Business / Domain
Expertise
Data
Management &
User Experience
Data Science
Customer
Obsessed

8. 3. Data Science Platform
OLAP on
Hadoop
(Flat Files,
Log Files)
(Internal)
Deposit, Loan,
Credit Wealth
(Mainframe)
RCIF
External &
Third Party
APIs/Services
Data Sources
Streaming
SFTP
Custom
Parsers
SQOOP
Batch Data
Movement
Data Wrangling
Data
Catalog
Business
Intelligence
ReportingMaster Data
Management
API Management
DataQuality
Modeling
Environment
Model
Management
UI Layer
Storage
Process
Hive
Pig
Batch
Spark
Streaming
Stream
Impala
Hive (on
Spark)
Spark SQL
SQL
Solr
Search
Spark Mllib
Python
R
Scala
Model
Compute
Grid
Current Data Environment
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9. Modern Machine Learning Platform
Data
Manipulation
ApplicationDataStorageCompute
App Database
(DynamoDB, Postrgres, Firestore)
Streaming
(Kafka, Flink)
S3, Azure Blob
Storage, GCP
Cloud Storage,
HDFS
Metadata
Management
(Glue, GCP Data
Catalog)
Feature Engineering
(Step Functions+EMR,
Airflow+Spark, Data Proc)
Spark on
Kubernetes
TPU/GPU Hosted Notebooks
BI and OLAP on
Hadoop
(AWS quicksight,
Power BI, Arcadia)
Experimentation
(Polyaxon, MLFlow,
Kubeflow)
Trained model is deployed as a
webservice in a Kubernetes environment
3. Data Science Platform
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10. 4. Data Management
Smart Data Catalog
Discover, Curate, Glossary
Ratings & Review, Self-Learn
Data Management and
Governance
Operationalize Data Lake
Removing Silos
Data Lake Zones
Data Privacy
Proactive protection
CCPA, GDPR
Data Quality
Define, Assess and Analyze
Improve, Implement and Manage
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11. 5. Culture
Data Products
Sponsorship
Buy-In
Integration
Measure
Vision
Culture
• Make Regions a best in class Data-Driven Organization
• Develop end-to-end Data Products, with Executive Sponsorship, full
Buy-In and complete Integration with in the business units
• Deliver Measurable bottom-line benefits to Regions by applying
advanced analytics on internal and external data
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12. Roadmap to a Data-Driven Future…
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1 3 5
2 4 6
ORGANIZE TO LEARN ESTABLISH KEY PRIORITIES SECURE EARLY WINS
DEFINE STRATEGIC INTENT BUILD THE LEADERSHIP TEAM CREATE SUPPORTING ALLIANCES