Columbia is a data-driven enterprise, integrating data from all line-of-business-systems to manage its wholesale and retail businesses. This includes integrating real-time and batch data to better manage purchase orders and generate accurate consumer demand forecasts.

1. We Connect Active People With Their Passions
COLUMBIA SPORTSWEAR – SPARK + AI SUMMIT 2020
JUNE 2020
LARAMINOR AND BILAL OBEIDAT

2. 2
Enterprise Information Management’s vision is to connect the RIGHT PEOPLE with the RIGHT
DATA at the RIGHT TIME to support informed business decisions.
Increase Columbia’s ability to:
• Be data driven in support of business strategy & operations
• Deliver enterprise data assets that meet global information needs
• Scale, share and grow using governed data, aligned processes and shared products
WHAT IS EIM?
VISION

3. 3
Technology
• Azure Data Management Stack:
–Azure Data Factory
–Azure Data Lake
–Azure Databricks
–Azure Synapse Data Warehouse
• SAP BW / Hana
DATA DELIVERY
abc
Development
• Integration:
–Columbia source systemsinto Azure
–Integration to 3rd party analytic
systems and applications
–Partnered with Columbia’s integration
team
• Data Models:
–Relational and dimensional models for
business reporting and analytics
–Data models for data science /
analytics

4. 4
WHERE WE STARTED
abc

5. 5

6. 6
BUSINESS ACCESS
abc
Info Consumer
Business users
(4k+)
Data Analyst
<15
Info Consumer
Business SME’s
50 -100
Databricks
/DataLake
Data
Warehouse
Azure Analysis
Services
PowerBI
Internal / Open Restricted
Internal / Select Restricted
Internal / Select Restricted

7. 7
DATA LAKE LAYOUT AND SECURITY
abc
Raw source
Internal
• Source system name
• Object Name (table
name)
• Type (full, incremental)
• Partition (date)
Restricted_domain
• Source system name
• Object Name (table
name)
• Type (full, incremental)
• Partition (date)
Curated
Internal
• Data Domain (sales)
• Schema
• Table name
Restricted_domain
• Schema
• Table name
Computed(Analysts
directory)
Dtc_restricted
• Analyst determine

8. 8

9. 9
• For bringing in data from source systems, what used to take a week takes a
day
• All computed data on the lake and available for use. Microservice drops to
the lake for real time reporting through Databricks Streaming.
• Databricks external metastoreallows for sharing with EIM
• Everything we do is through CICD integration
• Cloud based and elastic, speed/scalability enabled growth and efficient
data processing at low cost
• Expanded data access for business, self serve reporting, business analysis
• Prepped for data science resourcesto engage
• Easy team expansion and onboarding, we’ve increased dev team from 8 to
20 in a 1.5 years
POSITIVE OUTCOMES
abc

10. 10
General
• Security groups
• Security Model
• Security Audit
• Costs
Vendor Engagement
• Leverage vendors, but know there’s
a limit
• Professional services
• Vendor experts and agreements
LESSONS LEARNED
abc
Data Lake
• Security
• Organization, Enterprise
• Audit, Monitoring
• Backup,DR
Team
• A solid leader or two
• Keep the team open to change, open to
chaos
• Allocate time for discovery
• Managing expectationswith senior
leadership

11. Questions?
11
Confidential