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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How data prep onAWS empowers
Autodesk customer analytics
John Gardner
D E M 2 9 - S
Senior Business Intelligence Analyst
Autodesk
Agenda
Introduction
• Autodesk
• Market Intelligence
Challenge/Use Case
• Developing a 360 view of a sub-set of customers to drive marketing and sales strategy
• Current process and its issues
Solution
• How our team has leveragedTrifacta & Snowflake on top ofAWS to streamline our previously manual processes
Results
• Time/effort reduction
• Increased stakeholder collaboration
• Deeper analysis
Autodesk-WhoWeAre
MarketIntelligence
SampleProject Lifecycle
Extract
/Compi
le
Manipulate Publish RefreshPhase
Descripti
on
Issue
Segmenting customers based on
marketing & salespotential
Sample Request
Create a prioritized list of current construction customers that do not own our construction processing tool
(BIM 360)
Challenge
• To create proper prioritization, we need to create a full 360 degree view of our construction customer
base
• Data used to track customer financial metrics is housed in a separate data warehouse than account and
product ownership data
• These two data sets also have different unique identifiers mapping needs to be done using a separate
mapping table with >1m rows
• Separate data sources w/ various BI access points + large data sets + inability to automate = lengthy,
unstable process
Theoldprocessisunstableandinefficient
Extract Manipulat
e
Publish
Refresh
Internal Enterprise Sources
External Marketing and
Market Research Sources
Manipulat
e
Publish
Refresh
Extract
Theoldprocesswasunstableandinefficient
Transform,
Automate
Organize
Thenewprocess isasimple,streamlined,and dynamic
Powered by
RefreshPublish
Manipulate
Results
 

Thank you!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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How data prep on AWS empowers Autodesk customer analytics

Editor's Notes

  1. Extract: Require account profile data and financial data which are only accessible via separate BI tools. Thus the info must be downloaded into excel Datasets ultilize different unique IDs so a separate mapping table must be created to union the data. This mapping table is built off of a larger mapping table and must be created using an IN function for every ID (sometimes over 100k), creating a slow and inefficient process.