The document outlines Columbia Sportswear's enterprise information management (EIM) vision to connect individuals with data for informed business decisions, emphasizing a robust data management technology stack. It highlights advances in data processing speed, access, and scalability, which have significantly improved reporting capabilities and facilitated data science engagement. Key lessons learned involve maintaining a flexible team structure, ensuring security, and managing senior leadership expectations during the transition to a more data-driven organization.

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