How Macy’s creates

Operational Insights on

Hadoop

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

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

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

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

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

The Blueprint

The Blueprint

Need a Robust Experimentation FrameworkProblem 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

DataDomains

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

Growing painsChallenge

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

How to drive adoption?

Quality

Release

SocializeTrain

Measure

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)

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

Thank You!!

Seetha ChakrapanyAnalytic & CRM Solutions