DXC Commercial and Professional Services Ashburn, VA, USA

DXC Technology

Data Lake / Data Warehouse Modernization · Document Processing · Enterprise Knowledge Search

DXC's history of mergers left the company with fragmented data repositories and thousands of inconsistent dashboards, which slowed analytics, weakened trust in reporting, and made it difficult to scale advanced AI capabilities across the business.

80 dashboards were considered reliable
Three AI agents are already in production
eight more in pilot across HR, finance and delivery workflows
over 5,000 dashboards reflected different perspectives

Reported impact

Business outcome

DXC replaced fragmented reporting with a single trusted cloud data platform, improved access to near-real-time insights, reduced the confusion caused by thousands of inconsistent dashboards, and put production AI capabilities into daily use through natural-language analytics, staffing optimization, and document analysis.

  • Cost Reduction
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

DXC rebuilt its internal data foundation on a cloud-hosted lakehouse powered by AWS and the Databricks Data Intelligence Platform. The program migrated data from systems including Azure Synapse and SQL Server into one secure environment, where Spark modernized ingestion and preparation and Qlik Sense handled visualization. DXC also changed the operating model around the platform, adopting DataOps, automated CI/CD, unified batch and streaming workloads, FinDevOps visibility, and secure sharing through Delta Sharing. Once the data was centralized and governed, DXC put AI to work on top of it: Databricks Genie let employees query data in natural language, and production AI agents began supporting staffing optimization and unstructured document analysis across HR, finance, and delivery workflows. The result is a single trusted data platform that supports both near-real-time analytics and enterprise AI use cases at scale.

Data flow

Data from Azure Synapse, SQL Server, and other repositories is ingested with Spark into an AWS-hosted Databricks lakehouse, where it is standardized, governed, and prepared for analytics. Qlik Sense serves dashboards on the unified data, Delta Sharing exposes secure shared datasets, and Databricks Genie plus production AI agents consume the governed information for natural-language analysis, staffing optimization, and unstructured document workflows.

Solution architecture

7 components · 4 layers
  1. Ingest
    • Spark Modernizes data ingestion and preparation as source data is moved into the cloud platform.
  2. Compute
    • AI agents Run production use cases for staffing optimization and unstructured document analysis.
  3. Serving
    • Qlik Sense Provides visualization and reporting on top of the centralized data environment.
    • Databricks Genie Gives employees a conversational AI interface to query business data in natural language.
    • Delta Sharing Enables secure day-one collaboration and controlled data sharing across teams.
  4. Uncategorized
    • AWS Hosts the secure cloud environment for DXC's unified internal lakehouse platform.
    • Databricks Data Intelligence Platform Unifies DXC's data, analytics, AI, and governance in a single lakehouse environment.

Architecture clues

  • AI consumption includes natural-language querying with Genie and production agents for staffing and document workflows.
  • DXC adopted DataOps, automated CI/CD, unified batch and streaming workloads, and FinDevOps monitoring around the platform.
  • Secure collaboration is delivered through Delta Sharing and collaborative AI workspaces.
  • The platform is described as a secure cloud-based lakehouse hosted on AWS and Databricks.

Evidence from the source

"Databricks unlocked AI solutions for data analytics and predictive insights accessible at scale. Genie, a conversational AI assistant, allows employees to query data in natural language."
"Databricks was positioned as a Unified Data Platform within DXC to facilitate internal data collaboration."
"Three AI agents are already in production, supporting staffing optimization and unstructured document analysis, with eight more in pilot across HR, finance and delivery workflows."
"With all the data now located in one secure home in the cloud, information flows continuously instead of sitting in silos, so teams can see what’s happening in near real time and make faster, smarter decisions."
"over 5,000 dashboards reflected different perspectives, causing inconsistent reporting and making it challenging to identify trusted, unified insights."
Transparency Evidence & methodology 75/100 reference value · 93% confidence · 2 sources

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 confidence93%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 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 transparency10/10
    2 source references 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. DXC Technology

    Databricks enterprise data migration at DXC | DXC Technology Customer Stories

    First-party
    https://dxc.com/insights/customer-stories/accelerating-dxc-enterprise-data-vision-with-databricks
    Source type
    Vendor-published customer story
    Published
    Nov 26, 2025
    Supports
    • Customer
    • Industry
    • Region
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • Publication date
    • evidence quotes
  2. Built In

    DXC Technology Careers, Perks + Culture | Built In

    Independent secondary
    https://builtin.com/company/dxc-technology

    The Organization JSON-LD lists addressLocality 'Ashburn', addressCountry 'US', and numberOfEmployees value 86261.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence