With great data, comes great responsibility. At Overstock.com, lack of data has never been an issue. We know everything from the color you search most, to which room you’ll redesign next. We can see individuals transition from furnishing their first flat to building their dream home, but processing this data requires some serious firepower. It has fueled our focus on delivering real-time personalization through the unification of data and AI. Databricks is at the crux of this vision – empowering us to leverage cloud-scale with a platform that simplifies data engineering and increases the productivity of our data science team. Tune in as Chris Robison takes you through marketecture innovations in building a successful marketing technology infrastructure for instantaneous individualized marketing experiences.

1. from batch to
real time
overstock’s journey towards unifying analytics across data
engineering and data science

2. retail is nearing the end of its
digital transformation
competition is more intense
and personalization is key
if you don't know your customer
and speak directly to her you
will lose
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3. overstock.com
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pushing boundaries since 1999

4. we needed a new
marketing architecture
to unleash our data
and personalize across
silos
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Data Warehouse Hadoop
Structured Data Semi-Structured Data
ETL
Data
Eng
Analytics Use Cases
Data Science Use
Cases
Before

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N+1 Data Sources
Batch Streaming

7. comprehensive, cross-
channel marketing
powered by AI built on
Databricks
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9. Databricks Unified Analytics Platform
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• support for python and R much more robust in
Databricks
• full suite of libraries are interchangeable and easy
to install
• collaborative notebooks means less code
reproduction for common tasks
• full suite of technology from ETL to deep learning
• democratize the power of Spark across the
organization
Why
Apache
Spark on
Databricks?

10. the right tool for
the right person:
data scientist,
data analyst,
engineer, or
business owner
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11. from silos
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Business Owner Analyst Data Scientist Engineer

12. to unification
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Business Owner Analyst Data Scientist Engineer

13. delivering actionable insights when the business needs them
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• a more collaborative approach to data science and
analytics => personalized marketing
• audience/customer data in real time
• streaming infrastructure gives real-time diagnostics
and analysis
• business owners operate strategically
driving
business
value

14. parallel innovation =
shared success
at unprecedented speed
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15. results: closed the gap between POC and production
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• decreased cost of moving models to production by
nearly half
• stand up new models in one fifth the time
previously required
• can make inter-day improvements on existing
models without new deploys
• quickly spin up/down clusters through self-service,
cluster management
driving
business
value

16. right people
unified platform
personalized marketing
in real time
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17. Come see our talk!
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Data Science and Enterprise Engineering
11:00am – Enterprise Track