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Databricks Financial Services United States

Highstreet Insurance Partners

Data Lake / Data Warehouse Modernization · Marketing Personalization · Real-time Analytics

To meet that challenge, Highstreet created Ora, a proprietary, unified digital intelligence experience on Databricks that brings together trusted data, analytics, intelligent automation, and Al in a single environment.

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Imagine the daunting task of integrating billions of disparate data points, from over 200 local agency acquisitions and a continuously expanding network of new agency partners.
Since 2018, Highstreet Insurance Partners has grown through partnerships with more than 200 independent agencies to become one of the top 25 insurance brokerages in the United States.
Today, Ora manages more than 49 billion data points spanning Highstreet’s client, policy, and carrier history.
Within twelve months of its May 2025 launch, over 95 percent of Highstreet colleagues gained access to Ora, and active use is climbing steadily.

Reported impact

Business outcome

Highstreet Insurance Partners achieved measurable business impact on Databricks, including Since 2018, Highstreet Insurance Partners has grown through partnerships with more than 200 independent agencies to become one of the top 25 insurance brokerages in the United States.; Imagine the daunting task of integrating billions of disparate data points, from over 200 local agency acquisitions and a continuously expanding network of new agency partners.; Today, Ora manages more than 49 billion data points spanning Highstreet’s client, policy, and carrier history..

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

To meet that challenge, Highstreet created Ora, a proprietary, unified digital intelligence experience on Databricks that brings together trusted data, analytics, intelligent automation, and Al in a single environment. Highstreet reduced the time required to onboard a new agency partner's data into Ora from as much as six months to less than two weeks. Highstreet now gains near-immediate visibility into a partner's book of business, allowing analytics, AI, and operational insights to begin supporting client service, growth, and decision-making far earlier in the integration process. Ora is central to Highstreet's day-to-day operations. On the Databricks Lakehouse architecture, Ora brings together structured and unstructured data in a single governed environment that supports analytics, predictive insights, machine learning, intelligent automation, and AI.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed through managed pipelines stored in Delta Lake under Unity Catalog governance

Solution architecture

3 components · 3 layers
  1. Storage
    • Delta Lake Stores Highstreet Insurance Partners's curated datasets for analytics, machine learning, and operational use cases.
  2. Orchestration
    • Lakeflow Jobs Orchestrates recurring ingestion and transformation pipelines inside Highstreet Insurance Partners's lakehouse.
  3. Governance
    • Unity Catalog Provides access control, governance, and lineage across Highstreet Insurance Partners's shared data products and AI workloads.

Architecture clues

  • Cloud: Azure
  • Key product: Delta Lake
  • Key product: Lakeflow Jobs
  • Key product: Unity Catalog

Evidence from the source

Imagine the daunting task of integrating billions of disparate data points, from over 200 local agency acquisitions and a continuously expanding network of new agency partners.
Since 2018, Highstreet Insurance Partners has grown through partnerships with more than 200 independent agencies to become one of the top 25 insurance brokerages in the United States.
To meet that challenge, Highstreet created Ora, a proprietary, unified digital intelligence experience on Databricks that brings together trusted data, analytics, intelligent automation, and Al in a single environment.
Today, Ora manages more than 49 billion data points spanning Highstreet’s client, policy, and carrier history.
Within twelve months of its May 2025 launch, over 95 percent of Highstreet colleagues gained access to Ora, and active use is climbing steadily.
Transparency Evidence & methodology 90/100 reference value · 89% 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 value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Databricks

    Highstreet Insurance Partners customer story

    First-party
    https://www.databricks.com/customers/highstreet-insurance-partners
    Source type
    Vendor-published customer story
    Published
    Jul 24, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 24, 2026 · Databricks · First-party
    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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