How Macy's creates operational insights on Hadoop

1. How Macy’s
creates
Operational
Insights on
Hadoop

2. Macy’s, Inc. Background
 Macy’s, Inc. is one of the nation’s premier
omnichannel retailers
 Fiscal 2015 sales of $27.1 billion
 Operates 870 stores in 45 states
 Brands: Macy’s, Macy’s Backstage,
Bloomingdale’s, Bloomingdale’s Outlet,
Bluemercury as well as macys.com,
bloomingdales.com, bluemercury.com
 Ships products to over 100 countries
 Workforce includes over 157,000 employees

3. Digital Growth
 World went digital
 Macys generated its first billion-dollar month
of sales from digital platforms in December
2015
 Filled nearly 17 million online orders at
macys.com in November/December 2015 an
increase of about 25% over previous year
 Based on significant new fulfillment capacity,
site functionality, and aggressive digital
marketing

4. Why Hadoop at Macy’s?
 Traditional data architecture is
inflexible and not nimble
 Inability to tap into historical data
 Severe compute capacity limitations
 Significant cost implications to
scaling
 Unstructured data sources

5. Why BI on Hadoop?... Why Not?!
 Single data architecture can cater to
a comprehensive list of use-cases
 Integrated eco-system of data,
process, and tools
 Analytics, Experimentation, and
Production can be collocated
 Low total cost of ownership

6. What does it mean to be
Operational?
 Ability to move quickly from
testing/experimentation cycle to
production
 Reliable data quality, governance,
and security
 Acceptable levels of stability and
robustness to meet SLAs
 Automation to the nth degree

7. The Blueprint

8. The Blueprint

9. Need a Robust Experimentation Framework
Problem Statement
• What issue are we trying to
solve for?
• Why is it important to the
business?
Size of Problem
• What’s the $ impact?
• % customers affected?
• What can/can’t we influence?
Hypotheses
• What’s the root cause?
• What change will have the best
ROI?
• Are there alternatives?
Supporting Data
• Validate (or adjust) our
hypotheses
• Rule out false positives
Tests
• What’s the safest way to test
our riskiest assumptions?
• Who/when/how?
Predictors
• What variables are most highly
correlated with our problem
and/or solution?
KPIs / Success
• What outcome would we define
as “success”
• What’s our response to
success/failure?
Key
“Who provides?”
Team 1
Team 2
Team 3

10. Data
Domains
Orders
Customers
Products
Clicks
Marketing
External
Big Data Repository
In-memory Data
De-dupe
AggregationTransformation
Blending
Tools
Campaign Management/
Optimization
Statistical Analysis
Consumers
Merchandizing
Marketing
Product
Management
Analytics
Data Scientists
Advanced Analysis/
Modeling
Data Visualization/
Data Mining
Other Business
groups
Storage and Enrichment
Data Management
Data Security

11. Growing pains
Challenge
 Significant time spent on data
engineering
 Long analytic iteration times
 Inability for analysts to collaborate
Solve
 Need to establish a virtual semantic layer
 Seamlessly integrate with existing tools
 Deploying in-memory Big-Data OLAP tool

12. How to drive adoption?
Quality
Release
SocializeTrain
Measure

13. Adoption Checklist Center of Operations
+
Center of Evangelism
 Confidence in data quality
 Data governance, and security
 Standardized release process
 Socialize and Train
 Monitor adoption (Qualitative and
Quantitative)

14. Keys to Success
 Laser focus in delivering business
value
 Keep process overheads at check
 Continuous operational improvement
 Tolerance to a maturing solution for
the greater good
 Flexible resource model

15. Thank You!!
Seetha Chakrapany
Analytic & CRM Solutions