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Data & Analytics · Transportation & Logistics

13 data sources.One place to work with them.

IMSKC brought business information together in an AWS and Snowflake data warehouse for a transportation and logistics group operating across 51 locations in the United States and Canada.

Talk about your dataDownload entire case study herePDF
connected data sources
13connected data sources
shared data warehouse
1shared data warehouse
locations across the US and Canada
51locations across the US and Canada
  • AWS
  • Snowflake
  • Data integration
  • Managed services

The challenge

The information existed. Getting answers took work.

The business relied on separate platforms for information about its finances, workforce, fleet, marketing, payments, and operations. Answering a question that crossed those areas meant logging into different dashboards, downloading data, and assembling a report by hand.

As the business prepared to grow, it needed a more consistent way to access and use its data.

Client: A transportation and logistics group operating across the United States and Canada.

Before and after

A simpler starting point for reporting.

  • Financeown platform
  • Workforceown platform
  • Fleetown platform
  • Marketingown platform
  • Operationsown platform
  1. 01Separate platforms
  2. 02Individual exports
  3. 03Manually assembled reports
.

The solution

Connected, organized, and ready to work with.

IMSKC designed the platform to do more than collect data. It needed to prepare usable information, protect access, and support reliable operations as the business grew.

  1. 01

    Collect automatically

    AWS integrations bring information from source systems into a controlled data environment.

  2. 02

    Make information usable

    Validation and transformation prepare incoming data for analysis.

  3. 03

    Protect access

    Identity, encryption, and data access policies control how information is accessed.

  4. 04

    Keep it supported

    Monitoring, alerts, and resource controls help the team maintain reliability and manage consumption.

Technical walkthrough

Inside the platform.

The architecture combines custom integrations, controlled processing, and distinct Snowflake data layers. Each part has a defined role in turning source information into usable business datasets.

  1. Stage 01

    Extract and orchestrate

    Bring information in without making people gather it manually.

  2. Stage 02

    Land and prepare

    Give incoming information a controlled place to arrive and prepare it for use.

  3. Stage 03

    Load into Snowflake

    Move staged information into the shared warehouse through managed ingestion.

  4. Stage 04

    Structure for analysis

    Separate incoming records from the prepared information people use for reporting.

    1. RAW_DB

      Source-aligned landing data.

    2. STAGE_DB

      Cleansing, validation, normalization, and transformation.

    3. CURATED_DB

      Business-approved analytical models, subject areas, and standardized KPIs.

    4. REPORTING_DB

      Secure reporting views and consistent datasets for business intelligence tools.

The work beyond connecting the data.

Controlled access

AWS IAM applies least-privilege roles and service permissions. Secrets Manager protects integration credentials and supports rotation policies. AWS KMS manages encryption keys.

Snowflake’s security design includes SSO, MFA, role-based access, row access policies, dynamic data masking, and secure views.

Visible operations

Amazon CloudWatch centralizes logs, metrics, alarms, and notifications. AWS CloudTrail records administrative and API activity.

Snowflake monitoring covers Snowpipe health, failed loads, data freshness, query performance, storage growth, and resource alerts.

Workload and cost controls

Separate Snowflake compute warehouses support loading, transformation, reporting, ad hoc analysis, and sandbox work.

Auto-suspend, auto-resume, resource monitors, credit limits, and notifications provide controls over consumption.

Delivery approach

A focused team. An accelerated schedule.

8 weeks · delivery target

An eight-week delivery target called for close coordination between IMSKC and the client’s team.

IMSKC managed architecture, AWS deployment, Snowflake configuration, integration development, testing, documentation, and knowledge transfer. The client provided source-system access, business requirements, and input on how its information should be structured.

Daily standups, progress reporting, and direct communication kept dependencies visible and decisions moving.

Delivery included

  • Unit and integration testing
  • User acceptance testing and production validation
  • Architecture and security documentation
  • Operational runbooks and disaster-recovery procedures
  • Administration guidance and knowledge transfer

Outcomes

A stronger foundation for business decisions.

Reporting users can access data from 13 connected sources through one warehouse environment, bringing information from multiple business areas into the same analysis.

Ongoing support

A monthly managed-services engagement covers AWS and Snowflake administration, pipeline troubleshooting, role and policy maintenance, warehouse tuning, and platform monitoring.

Support activities include monthly platform health and cost-optimization reviews, capacity planning, and quarterly access reviews.

Next phase Planned

Business dashboards and reporting experiences built on the shared data foundation.

Take the full story with you.

Download the complete case study, including the AWS and Snowflake architecture, security controls, and ongoing support approach.

Download entire case study herePDF

Bring your business data together.

Spending too much time pulling information together? Let’s talk about a simpler starting point for your reporting.

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