Case Study · Data Quality & Integration

Cleaner records, governed pipelines, analytics that land.

A medical equipment company's Salesforce instance had years of duplicate accounts, siloed claims data, and unprocessed documents with no path to analytics. DataSkate deployed Informatica IDMC to cleanse, integrate, and process everything into Azure, then stood up Power BI on top of governed data, in eight weeks.
Industry
Healthcare / Medical
Systems
Salesforce → IDMC → Azure
Engagement
Data Quality & Integration
Timeline
8 weeks
Integration at a glance
Salesforce
Salesforce
Accounts · Contacts · Claims · Cases
Sync · Ingest · Process
DataSkate · Informatica
Informatica IDMC
CDQ · CDI · IDP
Curated delivery
Azure + Power BI
Azure Data Lake
Power BI · AI Analytics · Review Queue

Good systems. Messy data. No path downstream.

Salesforce was the system of record for accounts, contacts, claims, and cases. But years of data imports had left thousands of duplicate account records with no deduplication process to clean them. Every report was suspect. Every outreach list was inflated.

Claims and cases data existed in Salesforce but had no integration path downstream. There was no data lake, no analytics layer, and no governed way to get data out. Leadership wanted dashboards, but there was nothing reliable enough to build them on.

Claim and case documents were arriving but sitting unprocessed. No ingestion pipeline, no content extraction, no deduplication logic. Processing was entirely manual, and falling further behind every week.

Industry
Healthcare / Medical
HIPAA-sensitive data environment
System of Record
Salesforce
Accounts · Contacts · Claims · Cases
Before DataSkate
No integrations
Duplicate records, manual doc review, no analytics

Seven workflows. Three IDMC modules. One governed pipeline.

Cleanse, deduplicate, standardize.
Salesforce → Informatica CDQ
Built a CDQ workflow to profile account and contact records, identify and collapse duplicates, standardize field values, and route exception records to a staff review queue rather than committing uncertain data downstream.
Secure pipelines for claims data.
Salesforce → Informatica CDI
Configured CDI integration pipelines to extract Claims and Cases from Salesforce and move them through HIPAA-aligned configuration parameters. Governed, structured, and auditable from source to destination.
Ingest, process, detect duplicates.
Salesforce Documents → Informatica IDP
Set up IDP to ingest claim and case documents directly from Salesforce, configure intelligent processing for content extraction and classification, and flag duplicate or repeated document submissions automatically.
Governed data, delivered downstream.
Informatica IDMC → Azure Data Lake
Established the delivery pipeline from IDMC to Azure Data Lake. Approved, cleansed, and integrated data flows on a governed schedule, ready for analytics consumption without manual intervention.
Analytics built on clean data.
Azure Data Lake → Power BI / AI Analytics
Validated the handoff path from Azure to Power BI and AI analytics tooling, enabling quality dashboards and AI-supported reporting backed by governed, cleansed records rather than raw Salesforce exports.
Every exception routed, never dropped.
Informatica IDMC → Staff Review Queue
Any record or document below confidence thresholds is automatically routed to a structured staff review queue for manual validation and correction. No data silently discarded, every exception tracked and resolved.

Three modules. One governed path from Salesforce to Azure.

Salesforce
CRM Source
4 Objects
Accounts & Contacts
Claims & Cases
Claim & Case Documents
CDQ
CDI
IDP
Informatica IDMC
CDQ
CDI
IDP
MuleSoft IDP
CDQ · Profile & Deduplicate
CDQ · Standardize Fields
CDI · HIPAA-aligned Pipelines
IDP · Ingest & Process Docs
also: MuleSoft IDP
Exception Routing
Governed
delivery
Azure + Analytics
Data Lake
Power BI
Azure Data Lake (curated)
Power BI / AI Analytics
Staff Review Queue
Extract
Profile
Integrate
Process
Deliver
Analyze
DataSkate delivery pipeline · Salesforce → Informatica IDMC → Azure Data Lake → Power BI

Four phases. Eight weeks. Zero disruption to daily operations.

1
2
3
4
Step 1 · Week 1
Discovery & Access
Requirements validated against business needs. Environment access secured across Salesforce, Informatica IDMC, Azure, and Power BI. Data security requirements reviewed.
Access confirmed · Requirements locked · Security review
Step 2 · Weeks 2-3
Design
Field mapping completed for all four Salesforce objects. CDQ, CDI, and IDP workflow designs drafted. Exception-handling logic defined and architecture confirmed before any configuration began.
Field mapping · Workflow designs · Exception rules
Step 3 · Weeks 4-6
Configuration & Development
CDQ, CDI, and IDP components configured and built end-to-end. Azure Data Lake delivery paths established and tested with representative data. Staff review queue wired in for exception routing.
CDQ · CDI · IDP · Azure delivery path · Review queue
Step 4 · Weeks 7-8
Testing & Handoff
Full unit testing completed against representative data, defects corrected, and UAT package handed to the client team. Configuration documentation and a live knowledge-transfer session delivered on completion.
Unit tests · UAT package · Config docs · KT session

Seven workflows. Eight weeks. One governed pipeline.

3
Informatica IDMC modules (CDQ · CDI · IDP)
4
Salesforce objects in scope
7
Workflow components delivered
8
Weeks from kickoff to UAT handoff
0
Disruption to daily operations

End-to-end ownership. No handoffs between vendors.

Healthcare-aware delivery
HIPAA-sensitive environment handled with care throughout. Data configuration aligned to regulatory requirements from field mapping through delivery, not bolted on at the end.
Quality as a prerequisite
Deduplication and field standardization are built into the pipeline before any data reaches the data lake. Downstream analytics run on records that have already been validated, not raw exports.
Exceptions tracked, not discarded
Review queues built into the architecture from day one. Any record or document below confidence thresholds is routed to staff for validation, closing the loop on data quality rather than silently skipping uncertain records.