analytics and big data philip kim senior director, big data and analytics under armour®...

ANALYTICS AND BIG DATA

Philip KimSenior Director, Big Data and AnalyticsUNDER ARMOUR®

pkim@underarmour.com

Overview

OPPORTUNITY

CENTER THE VISION

TECHNICAL ARCHITECTURE

USER STORIES

ENGAGEMENT MODEL DESIGN

TEAM STRUCTURE

SEIZING OPPORTUNITY … CROSSING THE BIG DATA CHASM

BIG DATACHASM

70% of data generated by

customers

80% of data stored

3% prepared for analysis

0.5% being analyzed

<0.5% being operationalized

My basic chasm plan:1. Create shared vision2. Build fast & cheap3. Deliver quick wins

Source: Gartner Group

Overview

OPPORTUNITY

CENTER THE VISION

TECHNICAL ARCHITECTURE

USER STORIES

ENGAGEMENT MODEL DESIGN

TEAM STRUCTURE

UA’s VISION TO LEVERAGE BIG DATA

Center vision around the Customer/Athlete

Distill real time data into impact on customer relationship across:

• Business• Products• Channel• Geography

Enable actionable multi-channel customer engagement

Store everything to create a life time of value to the customer

AUGMENT PRODUCT INNOVATION

OPTIMIZE OPERATIONS

ERP

RETAIL

SOCIAL

ECOMM

MARKETING

CRM

WHOLESALE

3RD PARTY

PRODUCT

BRAND

CREATE AUTHENTIC CONNECTIONS

OVERVIEW

OPPORTUNITY

CENTER THE VISION

TECHNICAL ARCHITECTURE

USER STORIES

ENGAGEMENT MODEL DESIGN

TEAM STRUCTURE

OPERATIONS

BUSINESS USERSSCRUM MASTERDATA ENGINEERS

CAPTURE

Social

Retail

Wholesale

STORE & PROCESS

Hadoop clusters HDFS in the cloud

Low latency data retrieval

Big data tools / processing API

MANAGE

Master Data & Meta Data

3rd party data

Cleanse & join new data models

Single Sign-On

ANALYZE & ACT

Hi-PerformanceCache / RT engines

ETL and visualization API’s

Analytics & Visualization IDE

UA TECH SLIDE

DATA SCIENTISTS

OVERVIEW

OPPORTUNITY

BUSINESS OBJECTIVES

TECHNICAL ARCHITECTURE

CAPTURE USER STORIES

ENGAGEMENT MODEL DESIGN

TEAM STRUCTURE

EX. HARNESSING SOCIAL CONNECTIONS & DATA

Gift: 22Dec14

Last Login: 17Feb15 @11AM

Brand House Purchase: 20Dec14

Online Purchase: 1Feb15

$44.99 $59.99

$9.99Last Login: 17Feb15

@1PM

Shared Tweet: 4Jan15

Updated Run & Shared with Personal Trainer:

4Jan15

Time in Store

Products visited

Loyalty Points

EX. story #1 – Retail visualizationUser story: • As a retail analyst, I need to

perform time series analysis to establish expected variation of actuals vs forecast so I can deep dive into the top / significant outliers and save 10 hours/week

Aggregate data test:• Ingest data from <start> to <end> • Expected range of transactions ~50 million

records• ID & clean bad data algorithmically• Verify & ID seasonality – adjust for time

• Validate time series patterns with analyst

Data & transformation:• Create mockup of visualization• Ingest transactional data• Stage the data in HDFS• Perform regression to normalize data

prior to visualization

Analytic questions:1. What is the performance over

time?2. What are the key drivers or

predictors of performance?3. Can we use this model to reliably

forecast performance?

OVERVIEW

OPPORTUNITY

CENTER THE VISION

TECHNICAL ARCHITECTURE

USER STORIES

ENGAGEMENT MODEL DESIGN

TEAM STRUCTURE

PHASE 1

PHASE 3

PHASE 2

PHASE 4

Easy . . . . . . EFFORT . . . . Difficult

ENGAGEMENT MODEL

COLLECT TO PRIORITIZEUser stories – examples ONLY method:

1. As Senior Mgr of Allocation, I need to forecast store sales by size so that I can allocate inventory more accurately and decrease inventory holding cost by $xxM

2. As a retail analyst, I need to perform time series analysis to establish expected variation of actuals vs forecast so I can deep dive into the top / significant outliers and save 10 hours/week

3. As the BD analyst, I need a shareable visualization of retail performance to recommend workforce planning and no impact on retail gross sales

4. As the strategic manager, I need to map existing store sales and extrapolate new store sales so that I can identify microsegmented markets and increase my gross revenue / SQ foot

5. As the supply chain VP, I need to forecast demand versus factory deliveries so I can reduce my days of inventory by $xx /Y

6. … … …

PUT POINTS ON THE BOARD

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Phase 1 Phase 2 Phase 3 Phase 4

• Phase 1 are easy problems with big benefits

• ID champions with appetite for change

• Timebox projects; iterate fast; minimal products!

Tip:• Use Agile methodology

• Phase 2 projects are important and hard … reserve for your top talent!!!

• Larger teams; capital investments xx >$MM and payoffs xxx > $MM

Tip:• LEAN before digitize

• Phase 3 are medium

• Reduce friction in bulk with architecture … i.e. shift all projects to the easy axis by leveraging tech

Tip:• Tech shifts are next

year’s big projects

• Phase 4 projects are the fillers for other phases or backlog when resources are available

Time series for retail analytics

SC demand forecast

Forecast inventory by customer size

shareable visualization of retail

map existing store sales and new store sales

Capacity

Capacity

Capacity

Capacity

* Completed analytics labs

Anal

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TRANFER TO A ROADMAP

N*(N-1)2

Team structure … fast deliveryDefine done … Small teams … Fast iteration

Iterative development

Release to UAT

Story accepted

Story acceptance … daily standups … deliver in 2 weeks

Big Data to visualization example: