ANALYTICS AND BIG DATA
Philip Kim
Senior Director, Big Data and Analytics
UNDER 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 DATA
CHASM
70% of data
generated by
customers
80% of data
stored
3% prepared
for analysis
My basic chasm plan:
1. Create shared vision
2. Build fast & cheap
3. Deliver quick wins
Source: Gartner Group
0.5% being
analyzed
<0.5% being
operationalized
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
CRM
ECOMM
ERP
Distill real time data into impact
on customer relationship across:
3RD PARTY
CREATE AUTHENTIC
CONNECTIONS
SOCIAL
RETAIL
AUGMENT PRODUCT
INNOVATION
WHOLESALE
•
•
•
•
Business
Products
Channel
Geography
Enable actionable multi-channel
customer engagement
MARKETING
OPTIMIZE OPERATIONS
PRODUCT
BRAND
Store everything to create a life
time of value to the customer
OVERVIEW
OPPORTUNITY
CENTER THE VISION
TECHNICAL ARCHITECTURE
USER STORIES
ENGAGEMENT MODEL DESIGN
TEAM STRUCTURE
UA TECH SLIDE
ANALYZE & ACT
Single Sign-On
Hi-Performance
Cache / RT engines
ETL and visualization
API’s
Analytics &
Visualization IDE
Retail
Low latency data retrieval
3rd party data
Wholesale
Big data tools / processing API
Cleanse & join new
data models
Social
CAPTURE
Hadoop clusters
HDFS in the cloud
Master Data & Meta
Data
STORE & PROCESS
MANAGE
OPERATIONS
DATA ENGINEERS
SCRUM MASTER
BUSINESS USERS
DATA SCIENTISTS
OVERVIEW
OPPORTUNITY
BUSINESS OBJECTIVES
TECHNICAL ARCHITECTURE
CAPTURE USER STORIES
ENGAGEMENT MODEL DESIGN
TEAM STRUCTURE
EX. HARNESSING SOCIAL
CONNECTIONS & DATA
Brand House Purchase: 20Dec14
Time in Store
Last Login: 17Feb15
@1PM
$9.99
Shared Tweet: 4Jan15
Updated Run & Shared
with Personal Trainer:
4Jan15
Last Login: 17Feb15 @11AM
Loyalty Points
$44.99
$59.99
Online Purchase: 1Feb15
Products visited
Gift: 22Dec14
EX. story #1 – Retail visualization
User story:
Data & transformation:
• 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:
Analytic questions:
• 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
1.
2.
3.
Create mockup of visualization
Ingest transactional data
Stage the data in HDFS
Perform regression to normalize data
prior to visualization
What is the performance over
time?
What are the key drivers or
predictors of performance?
Can we use this model to reliably
forecast performance?
OVERVIEW
OPPORTUNITY
CENTER THE VISION
TECHNICAL ARCHITECTURE
USER STORIES
ENGAGEMENT MODEL DESIGN
TEAM STRUCTURE
ENGAGEMENT MODEL
User stories – examples ONLY method:
1.
2.
3.
4.
5.
6.
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
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
As the BD analyst, I need a shareable visualization of retail
performance to recommend workforce planning and no
impact on retail gross sales
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
As the supply chain VP, I need to forecast demand versus
factory deliveries so I can reduce my days of inventory by
$xx /Y
………
PUT POINTS ON THE BOARD
None . . . . . . . . BUSINESS IMPACT . . . . . . . . $50M
COLLECT TO PRIORITIZE
6
5
1
PHASE 1
PHASE 2
2
3
7
PHASE 3
PHASE 4
4
Easy . . . . . . EFFORT . . . . Difficult
TRANFER TO A ROADMAP
Phase 1
Phase 2
Analytics & Visualization
Time series for retail
analytics
Forecast inventory by
customer size
SC demand forecast
Phase 4
Phase 3
shareable visualization of
retail
Capacity
map existing store sales
and new store sales
Capacity
Capacity
Capacity
•
•
•
Phase 1 are easy
problems with big
benefits
ID champions with
appetite for change
Timebox projects; iterate
fast; minimal products!
Tip:
•
Use Agile methodology
•
•
Phase 2 projects are
important and hard …
reserve for your top
talent!!!
Larger teams; capital
investments xx >$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
Phase 4 projects are the
fillers for other phases or
backlog when resources
are available
Tip:
•
Tech shifts are next
year’s big projects
* Completed analytics labs
Team structure … fast delivery
Define done … Small teams … Fast iteration
Story acceptance … daily standups … deliver in 2 weeks
N*(N-1)
2
Story
accepted
Iterative
development
Release to
UAT
Big Data to visualization example: