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 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: Create shared vision Build fast & cheap 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 CRM ECOMM ERP 3RD PARTY CREATE AUTHENTIC CONNECTIONS SOCIAL RETAIL AUGMENT PRODUCT INNOVATION WHOLESALE MARKETING OPTIMIZE OPERATIONS PRODUCT BRAND

OVERVIEW OPPORTUNITY CENTER THE VISION TECHNICAL ARCHITECTURE USER STORIES ENGAGEMENT MODEL DESIGN TEAM STRUCTURE

UA TECH SLIDE ANALYZE & ACT CAPTURE STORE & PROCESS MANAGE OPERATIONS Single Sign-On Hi-Performance Cache / RT engines ETL and visualization API’s Analytics & Visualization IDE CAPTURE STORE & PROCESS MANAGE Retail Low latency data retrieval 3rd party data Wholesale Big data tools / processing API Cleanse & join new data models Social Hadoop clusters HDFS in the cloud Master Data & Meta Data 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 Last Login: 17Feb15 @1PM Time in Store $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: 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 Data & transformation: Create mockup of visualization Ingest transactional data Stage the data in HDFS Perform regression to normalize data prior to visualization 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 Analytic questions: 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 COLLECT TO PRIORITIZE PUT POINTS ON THE BOARD PHASE 1 User stories – examples ONLY method: 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 PHASE 1 PHASE 2 6 5 1 2 PHASE 3 PHASE 4 3 7 Easy . . . . . . EFFORT . . . . Difficult 4

TRANFER TO A ROADMAP Phase 1 Phase 2 Phase 3 Phase 4 Time series for retail analytics Forecast inventory by customer size shareable visualization of retail Capacity Analytics & Visualization SC demand forecast 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 Tip: Tech shifts are next year’s big projects Phase 4 projects are the fillers for other phases or backlog when resources are available * Completed analytics labs

N*(N-1) 2 Team structure … fast delivery Define done … Small teams … Fast iteration Story acceptance … daily standups … deliver in 2 weeks Iterative development Release to UAT Story accepted N*(N-1) 2

Big Data to visualization example: