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Rob McKendrick, The Co-Operative Group
Co-op’s Transformation from
Bricks & Mortar to AI with
Databricks
Co-op: Always a Business Disruptor
2
3
Today’s Retail: Massive Tech Disruption
4
Co-op’s innovations…
5
Self-driving machines
for delivery
In-aisle mobile
payments
New digital services for
members
Innovations Require a New Approach
to Data
• Align data vision to creating customer value
• Embed trust and ethics into da...
We agreed a new data vision
Our vision is to be totally trusted
with data, and to use it to create
value for our customers...
Talked about Data and Ethics
8
Modern and Agile Data Ecosystem
Group Marketing/CRM
Food FunercalcareInsurance Electrical StoreLegal
One Co-op Functions
O...
Accelerate ML competencies
10
● New tooling for data science users to leverage machine learning
● Achieve faster time to v...
Our first use case:
Personalisation
11
These are pre-live designs for what people will
see on their membership portal and ...
Lots of models and analysis…
12
Stretch New product
recommendation
Product affinity
Availability
Favourite shops /
times
Got going really quickly
• Databricks running in Azure
• Training for Data Scientists
/ Analysts / Engineers
• Atomic data...
14
Product affinity
Every product
Every member
Every store
Every basket
11 Trillion rows of
intermediate data
15
Product affinity
Azure Blob
T-SQL
PySpark
Databricks
Force directed graph
d3js
16
Product affinity
Computed affinity
for combinations
of products for all
stores for all
weeks
Working more with Databricks
• We’ve already used ALS & Neural Networks
for developing recommendation algorithms
• Other t...
Thank you…
• Co-op Retail Insight, Data Science,
Data Engineering and Cloud Engineering
• Databricks
• Microsoft
18
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Co-op’s Transformation from Brick and Mortar to AI with Databricks with Rob McKendrick (Co-op)

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Keynote from Co-op at Spark + AI Summit Europe

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Co-op’s Transformation from Brick and Mortar to AI with Databricks with Rob McKendrick (Co-op)

  1. 1. Rob McKendrick, The Co-Operative Group Co-op’s Transformation from Bricks & Mortar to AI with Databricks
  2. 2. Co-op: Always a Business Disruptor 2
  3. 3. 3
  4. 4. Today’s Retail: Massive Tech Disruption 4
  5. 5. Co-op’s innovations… 5 Self-driving machines for delivery In-aisle mobile payments New digital services for members
  6. 6. Innovations Require a New Approach to Data • Align data vision to creating customer value • Embed trust and ethics into data usage • Build a modern and agile data ecosystem • Develop machine learning competencies 6
  7. 7. We agreed a new data vision Our vision is to be totally trusted with data, and to use it to create value for our customers, members and communities 7
  8. 8. Talked about Data and Ethics 8
  9. 9. Modern and Agile Data Ecosystem Group Marketing/CRM Food FunercalcareInsurance Electrical StoreLegal One Co-op Functions Operational Systems Data& Analytics Campaign Management Other Group Data Science Membership Customer Service Analytics SecurityandAccess Food FNCCI EStoreLegal Operational Systems DataEcosystemCustomer Other Customer Service Campaign Management Content Management Other Structured Data Unstructured Data Data Integration Master Data Management Tooling Supplier/PartnerIntegration Membership • Silo’ed • On-premise • Unified • Cloud first • Learn quickly through experiments
  10. 10. Accelerate ML competencies 10 ● New tooling for data science users to leverage machine learning ● Achieve faster time to value by creating analytic workflows from data to interactive exploration to production ● Exploring collaborative working and increased pace in delivery ● Standardizing on Azure cloud services ….
  11. 11. Our first use case: Personalisation 11 These are pre-live designs for what people will see on their membership portal and app
  12. 12. Lots of models and analysis… 12 Stretch New product recommendation Product affinity Availability Favourite shops / times
  13. 13. Got going really quickly • Databricks running in Azure • Training for Data Scientists / Analysts / Engineers • Atomic data transferred into Azure blog storage • Started Machine Learning 13
  14. 14. 14 Product affinity Every product Every member Every store Every basket 11 Trillion rows of intermediate data
  15. 15. 15 Product affinity Azure Blob T-SQL PySpark Databricks Force directed graph d3js
  16. 16. 16 Product affinity Computed affinity for combinations of products for all stores for all weeks
  17. 17. Working more with Databricks • We’ve already used ALS & Neural Networks for developing recommendation algorithms • Other teams now looking at other business cases • Our Digital offers trial goes live very soon • And we’ll learn out in the open https://digitalblog.coop.co.uk 17
  18. 18. Thank you… • Co-op Retail Insight, Data Science, Data Engineering and Cloud Engineering • Databricks • Microsoft 18

Keynote from Co-op at Spark + AI Summit Europe

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