Investing In Understanding The Customer Journey At American Express
Data Engineering Podcast
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Data Engineering Podcast - Customer 360 Platform
According to a recent survey by S and dot IO, 95% reported being at or over capacity. 72% of data experts reporting demands on their team going up faster than they can hire. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months.
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Summary
For any business that wants to stay in operation, the most important thing they can do is understand their customers. American Express has invested substantial time and effort in their Customer 360 product to achieve that understanding. In this episode Purvi Shah, the VP of Enterprise Big Data Platforms at American Express, explains how they have invested in the cloud to power this visibility and the complex suite of integrations they have built and maintained across legacy and modern systems to make it possible.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Purvi Shah about building the Customer 360 data product for American Express and migrating their enterprise data platform to the cloud
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what the Customer 360 project is and the story behind it?
- What are the types of questions and insights that the C360 project is designed to answer?
- Can you describe the types of information and data sources that you are relying on to feed this project?
- What are the different axes of scale that you have had to address in the design and architecture of the C360 project? (e.g. geographical, volume/variety/velocity of data, scale of end-user access and data manipulation, etc.)
- What are some of the challenges that you have had to address in order to build and maintain the map between organizational and technical requirements/semantics in the platform?
- What were some of the early wins that you targeted, and how did the lessons from those successes drive the product design going forward?
- Can you describe the platform architecture for your data systems that are powering the C360 product?
- How have the design/goals/requirements of the system changed since you first started working on it?
- How have you approached the integration and migration of legacy data systems and assets into this new platform?
- What are some of the ongoing maintenance challenges that the legacy platforms introduce?
- Can you describe how you have approached the question of data quality/observability and the validation/verification of the generated assets?
- What are the aspects of governance and access control that you need to deal with being part of a financial institution?
- Now that the C360 product has been in use for a few years, what are the strategic and tactical aspects of the ongoing evolution and maintenance of the product which you have had to address?
- What are the most interesting, innovative, or unexpected ways that you have seen the C360 product used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on C360 for American Express?
- When is a C360 project the wrong choice?
- What do you have planned for the future of C360 and enterprise data platforms at American Express?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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