analytics and big data philip kim senior director, big data and analytics under armour®...
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ANALYTICS AND BIG DATA
Philip KimSenior Director, Big Data and AnalyticsUNDER ARMOUR®
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
ytics
& V
isua
lizati
on
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: