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1. Kumar Ramanathan, Gautam Gupta
How to Build An AI Based
Customer Data Platform:
Learn the design patterns for Real Time Use Cases
September 2020

2. ©2020 Intuit Inc. All rights reserved. 2
Introduction
Kumar Ramanathan Gautam Gupta
Group Manager @ IntuitDirector Engineering @ Intuit

3. ©2020 Intuit Inc. All rights reserved. 3
Intuit’s mission: Powering Prosperity around the World
AI-DRIVEN EXPERT PLATFORM
4.5M QBO
CUSTOMERS

4. Personalize the journey for
everyone in ecosystem
1 2
What do we know about
our Customer?
What do our customers
need?

5. ©2020 Intuit Inc. All rights reserved. 5
High Level Architecture
Read TPS
20K
FCI
0.0003%
TP99
60 ms
Ingest TPS
60K
Ingest Latency
TP99
1s
(2kb payloads)

6. ©2020 Intuit Inc. All rights reserved. 6
Financial Ownership
WHAT IS THE FINANCIAL OWNERSHIP OF THIS USER?
WHO IS THIS USER OR VISITOR? WHAT IS THEIR INTENT?
Identity Resolution
WHERE IS THIS USER IN THEIR JOURNEY WITH US/LIFE
Customer Journey
Unlock Relationships and ML driven Insights
visitor
Financial
attributes
user
company
financial ownership
offerings
journey stage
Intent
persona
transactions
Graph Queries
Analytics
& ML
Graph Mining

7. ©2020 Intuit Inc. All rights reserved. 7
Why did we switch to Tiger Graph?
● Developer Friendly Platform
● Better 1-hop query
performance & efficient
multi-hop queries
● 77% reduction in AWS
infrastructure costs; 10x less
IOPS
● Excellent, responsive customer
support
Improved OPEX Savings
Developer
Friendly
Excellent
Support

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Use Case: Increase Sign-in Success Rate for Tax Prep
If we Leverage Identity Graph in the Risk Scoring Service Sign-in Flow for
TurboTax Online
Then We can recognize more unknown visitors
So that
We can provide a lower friction sign-in experience for those visitors
Increase Sign-in Success Rate

9. Intuit Confidential and Proprietary 9
Identity Graph
Stitching anonymous visitor to known user
Returning Customer
Recognition
Frictionless Sign In/up Personalization
visitor user
user <> visitor :: stitch
Clickstream: 159 columns x ∞ rows
Users: 142M Nodes
Input
Model
Pairwise binary classification
Let:
Learn if pair (IVID, UID) is “matched” to each other
where Θ parameter vector of the learned model
Optimize resulting quadratic complexity
by selecting subset
Final prediction function:
Chose unique UID, if exists: 99.9982%
Ranked multiple UID candidates: 98.8609%
Results● Identity graph able to recognize ~4% more visitors
● Sign-in Success rate for unrecognized cohort went from
89% to 94%

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1. Readily Accessible Data
Publishing data from source systems through batch and eventing to stream processing infrastructure & data lake
Top 3 challenges for creating Identity Graph
2. Lack of Universal First-class Entities
Creating universal definition of key entities like User, Visitor, Account etc. across product lines
3. Entity Resolution & Attribute Normalization
Data across multiple sources is not resolved and normalized, through deterministic & predictive algorithms

11. Design Patterns

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1. Data Movement: Platform not Pipelines
Why?
Rapid increase of new data sources and existing ones changing fast
Domain ownership for publishing data
High quality, large scale, domain agnostic data infrastructure
How?
Generic event processing pipeline with built-in configurable stages
Standardized implementation of domain agnostic stages
● Sessionization, Geo-Coding, Entity standardization & resolution, encryption/decryption,
compression, schema validation, authentication/security controls, governance & compliance
checks and many more.
● Metadata repository with beautiful UI for discoverability, lineage tracking & data trust
● Scalable operational platform - ability consume data from batch and stream sources with built-in
auto scaling, monitoring, alerting, error handling etc.
Support for adding custom stream computation stages for unique needs for specialized pipelines - ML
feature computation, dynamic traits computation
Step 2
Deduplication
Step 3
Data Validation
Step 4
NotificationAuthentication
Step 1

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2. Data Storage: Polyglot not Monolithic
Why?
Performance at scale is critical in a real-time data platform serving customer experiences
Shaping data to match access patterns allows for optimal and efficient access patterns
Tools for operating distributed systems and native NoSQL (KV, Search, Graph) DBs have matured
How?
One can use following patterns for creating a data store to handle all the information about Customer:
● Leverage a KV data store for entities and attributes
● Use Search based persistence for search queries on the attributes
● Store relationships using a Graph database
We can solve a wide variety of use cases with low latency. Using same database for handling Entities,
Relationships and Search capabilities leads to performance bottlenecks and higher latencies.

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3. Data Access: Right-for-me not One-size-fits-all
Why?
Data products are used in very different contexts. Need right interface for the right context.
How?
Support as many patterns below as possible:
● UI Widgets: Ability to data-enable products/features by embed winning experiences quickly
● Request/Response: Provide direct API access for synchronous communication
● Pub/Sub: Publish CDC notifications/messages to consumers who store data for specific use case
● Data Lake: To support offline model training or historical bootstrap for new pub/sub consumers

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4. AI Toolchain: Deeply integrated not Bolted on
Why?
AI models evolve over time and need access to new data
AI data needs grow with the evolution of models
AI models need real time access to data in production
How?
Develop a self serve mechanism for onboarding new AI models and Features required for them
● Create an input for a rich Feature store from Entities, Attributes and Relationships
● Provide aggregation and functional formulation on attributes
● Capture feedback from AI models to provide 360 view of model performance
● Solve for Real time availability of Data to AI models
● Data Exploration -> Featurization -> Training -> Model Optimization - > Model Deployment ->
Model execution

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5. Data Entities: Self-serve not Product Backlog
Why?
Time to market for new data products or expanding the features of existing ones is an important driver
of growth
How?
● Mindset: Data is product. Domain teams think of data consumers as their customers just like the
end users of their products through UI and other developers through their APIs.
● Producer and consumer work directly with each other to create value quickly using shared
domain knowledge
● No new engineering work is needed in the domain agnostic part of the platform to add new data
entities or attributes or inferring new relationships.
● Self-describing semantics for data - set of robust metadata capabilities that configures the
platform behavior for specific use cases. Metadata determines which stages of the pipeline is
executed, which version of business logic is run in a particular stage
● You’re successful when non-technical business users like product managers are able to discover
data, understand the business meaning of it and expand the data set in a self-serve form

17. Thank You!