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Snowflake Financial Services Australia

Open Insurance

Data Lake / Data Warehouse Modernization · Real-time Analytics

replace time-consuming, siloed insurance data management with a secure self-service platform that could scale analytics, mask sensitive data, and accelerate partner and acquisition integration.

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1 month to integrate 16 data sources from an acquired company
50% less time creating BI dashboards
70% data self-service across the business
the number of BI requests at Open has also fallen by nearly a third

Reported impact

Business outcome

Open Insurance achieved 70% data self-service across the business, cut BI dashboard delivery time by 50%, reduced BI request volume by nearly a third, and integrated 16 acquisition data sources in one month.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Open Insurance uses Snowflake, Streamlit, dbt Labs, and ChatGPT to turn fragmented insurance, customer, and partner data into a governed self-service analytics platform. Transactional data, product information, and inputs from Customer.io, Stripe, CRM systems, and about 10 UK third-party sources are centralized in Snowflake, where common definitions and reusable models are built so teams can query a single source of truth. Streamlit applications replace custom-coded BI dashboards and expose role-based views that mask personally identifiable information, while GPT-powered query experiences translate natural language into SQL and help staff explore data without waiting on the data team. The platform supports partner data sharing, rapid acquisition integration, and faster dashboard delivery across finance, commercial, and leadership teams.

Data flow

Customer transactions, product data, and partner-system feeds from tools such as Customer.io, Stripe, CRM platforms, and UK third-party sources land in Snowflake. dbt Labs models the data into consistent business definitions, and Streamlit plus ChatGPT-powered workflows surface governed self-service analytics and dashboard experiences to employees and partner teams.

Solution architecture

4 components · 3 layers
  1. Compute
    • dbt Labs Standardizes common requests and consistent definitions by modeling data directly in Snowflake.
  2. Storage
    • Snowflake Provides the centralized and governed data platform where insurance, transaction, and partner data are stored and queried.
  3. Serving
    • Streamlit Delivers self-service applications that let staff query and visualize governed data without custom dashboard code.
    • ChatGPT Supports natural-language query experiences that turn user questions into SQL and summarize results for business users.

Architecture clues

  • Open integrated internal analytics tools and SDKs plus roughly 10 third-party systems for UK operations.
  • PII is masked so only selected users can access sensitive information.
  • The platform uses multiple working data warehouses to support self-service and Streamlit development.

Evidence from the source

70% data self-service across the business
By using Streamlit, Open spends 50% less time creating BI dashboards
created a lightweight GPT model that could convert queries into natural language on SQL
integrated a UK insurtech ... in just one month
Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Snowflake

    Open Insurance customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/open-insurance/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 16, 2024 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jan 23, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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