Databricks Commercial and Professional Services Global

KPMG

Data Lake / Data Warehouse Modernization · Security and Compliance · System Migration

KPMG needed a better way to support enterprise data federation, governance, and analytics modernization because legacy Hadoop infrastructure and firmwide data silos made it difficult to serve analytics, machine learning, and AI use cases with the speed clients expected.

Reported impact

Business outcome

KPMG improved enterprise data federation, governance, and analytics modernization with measurable results including faster delivery from raw data to production decisions, while giving teams a faster path from governed data to production decisions.

  • Compliance
  • Productivity Improvement
  • Scalability

Solution

KPMG uses Databricks Data + AI Platform to support enterprise data federation, governance, and analytics modernization. The implementation replaced a legacy state where legacy Hadoop infrastructure and firmwide data silos made it difficult to serve analytics, machine learning, and AI use cases with the speed clients expected. On-premises sql data and cloud warehouse data move through Spark Declarative Pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog and Delta Sharing governs access, lineage, and compliance across sensitive datasets. Databricks SQL, MLflow, and Agent Bricks operationalize models, agents, and analytics that deliver federated governed datasets and Power BI dashboards. KPMG improved enterprise data federation, governance, and analytics modernization with measurable results including faster delivery from raw data to production decisions, while giving teams a faster path from governed data to production decisions.

Data flow

On-premises sql data and cloud warehouse data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver federated governed datasets and Power BI dashboards.

Solution architecture

8 components · 5 layers
  1. Ingest
    • Spark Declarative Pipelines Spark Declarative Pipelines standardizes on-premises SQL data and cloud warehouse data into production-grade tables and features.
  2. Compute
    • Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for enterprise data federation, governance, and analytics modernization.
    • MLflow MLflow tracks experiments, versions, and release workflows for models that support enterprise data federation, governance, and analytics modernization.
  3. Storage
    • Delta Lake Delta Lake stores curated on-premises SQL data and cloud warehouse data so every team works from one trusted data foundation.
  4. Serving
    • Databricks SQL Databricks SQL serves curated dashboards and ad hoc analysis to federated governed datasets and Power BI dashboards.
    • Agent Bricks Agent Bricks packages domain-specific agents that automate parts of enterprise data federation, governance, and analytics modernization.
  5. Governance
    • Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.
    • Delta Sharing Delta Sharing securely distributes governed data to federated governed datasets and Power BI dashboards and partner workflows.

Architecture clues

  • Automated ingestion and transformation pipelines keep source data synchronized for downstream analytics and AI.
  • Centralized governance, lineage, and access controls are enforced through Unity Catalog.
  • Delta Lake provides the shared storage layer for curated lakehouse datasets.
  • Operational AI workflows are deployed directly beside governed data and analytics assets.

Evidence from the source

AI will continue to dominate our business, from the way we run ourselves internally to taking advantage of the right tooling to deliver impact to our engagement teams and ultimately to our customers.
For the first time, we’ve created a true enterprise platform that can support the dynamic needs of both our front- and back-office professionals at KPMG.
We are a professional services organization leading others on their AI journey, and it is imperative that we excel in this domain,
We truly use Databricks as the foundation for everything we do,
Transparency Evidence & methodology 75/100 reference value · 87% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 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 specificity8/10
    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

    KPMG customer story

    First-party
    https://www.databricks.com/customers/kpmg
    Source type
    Vendor-published customer story
    Published
    May 22, 2024
    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 May 22, 2024 · 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.