SUCCESS STORY｜EUROPEAN LEADER IN SHIPPING AND LOGISTICS
Shipping｜From Qlik reporting to Snowflake data products
This shipping company runs around 600 Qlik dashboards whose business rules live inside the tool.
With Qlik to Snowflake and AI tooling, KPC handles the reverse engineering.
The impact in numbers
≈600
Qlik applications in production to migrate to Snowflake
≈2 d
Analysis effort, fixed per app
0 prerequisites
The analysis still works with no up-to-date spec and no original developer.
Between 10 and 40 days
of manual work avoided.
A long, unpredictable analysis phase replaced by a tooled process.
The impact in numbers
≈600
Qlik applications in production to migrate to Snowflake
≈2 d
Analysis effort, fixed per app
0 prerequisites
The analysis still works with no up-to-date spec and no original developer.
Between 10 and 40 days
of manual work avoided.
A long, unpredictable analysis phase replaced by a tooled process.
The impact in numbers
≈600
Qlik applications in production to migrate to Snowflake
≈2 d
Analysis effort, fixed per app
0 prerequisites
The analysis still works with no up-to-date spec and no original developer.
Between 10 and 40 days
of manual work avoided.
A long, unpredictable analysis phase replaced by a tooled process.
RESULTS
Concrete results
Data products, not copies of dashboards
The data is rebuilt at the refined level on Snowflake. It stays reusable beyond the original report.
Concrete results
A fixed analysis effort (≈ 2 days)
The duration stays the same whatever the app's complexity. Missing documentation or an absent original developer does not block the analysis.
Concrete results
A result validated against Qlik
The Match Control AI agent compares the data product with what exists and validates the build end to end. The spec produced is reliable and matches the real report.
RESULTS
Concrete results
Data products, not copies of dashboards
The data is rebuilt at the refined level on Snowflake. It stays reusable beyond the original report.
Concrete results
A fixed analysis effort (≈ 2 days)
The duration stays the same whatever the app's complexity. Missing documentation or an absent original developer does not block the analysis.
Concrete results
A result validated against Qlik
The Match Control AI agent compares the data product with what exists and validates the build end to end. The spec produced is reliable and matches the real report.
RESULTS
Concrete results
Data products, not copies of dashboards
The data is rebuilt at the refined level on Snowflake. It stays reusable beyond the original report.
Concrete results
A fixed analysis effort (≈ 2 days)
The duration stays the same whatever the app's complexity. Missing documentation or an absent original developer does not block the analysis.
Concrete results
A result validated against Qlik
The Match Control AI agent compares the data product with what exists and validates the build end to end. The spec produced is reliable and matches the real report.
Background & core challenges
Rebuild as data products, without starting from scratch
This world's 3rd largest shipping company runs around 600 Qlik dashboards in production, while a Snowflake foundation has been built over three years to centralize its data.
The problem: the business rules lived in Qlik — calculations, filters, transformations, dependencies, workflows. Specs and documentation were close to non-existent, or out of line with the real report.
Bring the refined data into Snowflake as data products (not a lift & shift)
Migrate without depending on a specification or documentation that is missing or out of date
Industrialize a reverse-engineering effort that had been variable and hard to cost
Guarantee users faithfulness to the existing Qlik reporting
Background & core challenges
Rebuild as data products, without starting from scratch
This world's 3rd largest shipping company runs around 600 Qlik dashboards in production, while a Snowflake foundation has been built over three years to centralize its data.
The problem: the business rules lived in Qlik — calculations, filters, transformations, dependencies, workflows. Specs and documentation were close to non-existent, or out of line with the real report.
Bring the refined data into Snowflake as data products (not a lift & shift)
Migrate without depending on a specification or documentation that is missing or out of date
Industrialize a reverse-engineering effort that had been variable and hard to cost
Guarantee users faithfulness to the existing Qlik reporting
Background & core challenges
Rebuild as data products, without starting from scratch
This world's 3rd largest shipping company runs around 600 Qlik dashboards in production, while a Snowflake foundation has been built over three years to centralize its data.
The problem: the business rules lived in Qlik — calculations, filters, transformations, dependencies, workflows. Specs and documentation were close to non-existent, or out of line with the real report.
Bring the refined data into Snowflake as data products (not a lift & shift)
Migrate without depending on a specification or documentation that is missing or out of date
Industrialize a reverse-engineering effort that had been variable and hard to cost
Guarantee users faithfulness to the existing Qlik reporting
Our approach
Tooled reverse engineering, from dashboard to spec
Re-asking the question from a blank page would risk drifting from what exists. So KPC rebuilds from Qlik by reverse engineering, tooled by QlikToSnow. The analysis can be replayed as often as needed and produces a usable spec. That spec becomes the entry point for an AI-assisted Snowflake build with Cortex Code, in an expert-in-the-loop cycle: the AI proposes, the expert validates.
Extraction
Extract the app's logic
QlikToSnow automatically extracts the app's business and calculation rules, dependencies and workflows.
Data analysis
Profile and map
QlikToSnow profiles the data and identifies what is actually used. The tool decides the field mapping and makes the lineage navigable. Impact analysis shows which fields feed which indicators, without going back into the code.
Match control & spec
Validate and specify
End-to-end validation against Qlik (or against an existing data product) and production of a reliable spec that matches the real report.
Our approach
Tooled reverse engineering, from dashboard to spec
Re-asking the question from a blank page would risk drifting from what exists. So KPC rebuilds from Qlik by reverse engineering, tooled by QlikToSnow. The analysis can be replayed as often as needed and produces a usable spec. That spec becomes the entry point for an AI-assisted Snowflake build with Cortex Code, in an expert-in-the-loop cycle: the AI proposes, the expert validates.
Extraction
Extract the app's logic
QlikToSnow automatically extracts the app's business and calculation rules, dependencies and workflows.
Data analysis
Profile and map
QlikToSnow profiles the data and identifies what is actually used. The tool decides the field mapping and makes the lineage navigable. Impact analysis shows which fields feed which indicators, without going back into the code.
Match control & spec
Validate and specify
End-to-end validation against Qlik (or against an existing data product) and production of a reliable spec that matches the real report.
Our approach
Tooled reverse engineering, from dashboard to spec
Re-asking the question from a blank page would risk drifting from what exists. So KPC rebuilds from Qlik by reverse engineering, tooled by QlikToSnow. The analysis can be replayed as often as needed and produces a usable spec. That spec becomes the entry point for an AI-assisted Snowflake build with Cortex Code, in an expert-in-the-loop cycle: the AI proposes, the expert validates.
Extraction
Extract the app's logic
QlikToSnow automatically extracts the app's business and calculation rules, dependencies and workflows.
Data analysis
Profile and map
QlikToSnow profiles the data and identifies what is actually used. The tool decides the field mapping and makes the lineage navigable. Impact analysis shows which fields feed which indicators, without going back into the code.
Match control & spec
Validate and specify
End-to-end validation against Qlik (or against an existing data product) and production of a reliable spec that matches the real report.
Our clients
Our clients
They trust us.
They trust us.
Equipment rental
Professional Equipment Rental Company | Industrialized tender response 100% Snowflake
Gen AI
Snowflake
→
Transport & Logistics
Global shipping company — Data Science: from R&D to industrialization
AI
Data
→
Industry
Aqualung Group — Transforming the data platform
Snowflake
Data Platform
→
Business services
APAVE — Cross-office capacity planning
Pigment
EPM
→
Organic food distribution
Biocoop — Steering the forecasting processes with SAP BPC
SAP
Retail & Distribution
→
Heating, plumbing and renewable energy distribution
Richardson — Opening up access to data by moving to the cloud
Qlik
Snowflake
→
Equipment rental
Professional Equipment Rental Company | Industrialized tender response 100% Snowflake
Gen AI
Snowflake
→
Transport & Logistics
Global shipping company — Data Science: from R&D to industrialization
AI
Data
→
Industry
Aqualung Group — Transforming the data platform
Snowflake
Data Platform
→
Business services
APAVE — Cross-office capacity planning
Pigment
EPM
→
Organic food distribution
Biocoop — Steering the forecasting processes with SAP BPC
SAP
Retail & Distribution
→
Heating, plumbing and renewable energy distribution
Richardson — Opening up access to data by moving to the cloud
Qlik
Snowflake
→
Equipment rental
Professional Equipment Rental Company | Industrialized tender response 100% Snowflake
Gen AI
Snowflake
→
Transport & Logistics
Global shipping company — Data Science: from R&D to industrialization
AI
Data
→
Industry
Aqualung Group — Transforming the data platform
Snowflake
Data Platform
→
Business services
APAVE — Cross-office capacity planning
Pigment
EPM
→
Organic food distribution
Biocoop — Steering the forecasting processes with SAP BPC
SAP
Retail & Distribution
→
Heating, plumbing and renewable energy distribution
Richardson — Opening up access to data by moving to the cloud
Qlik
Snowflake
→
A SIMILAR PROJECT IN MIND?
A Qlik estate to turn into data products on Snowflake?
Our Data & AI teams reverse-engineer your Qlik apps with QlikToSnow. Your data becomes governed data products, with the effort under control and the result validated.
A SIMILAR PROJECT IN MIND?
A Qlik estate to turn into data products on Snowflake?
Our Data & AI teams reverse-engineer your Qlik apps with QlikToSnow. Your data becomes governed data products, with the effort under control and the result validated.
A SIMILAR PROJECT IN MIND?
A Qlik estate to turn into data products on Snowflake?
Our Data & AI teams reverse-engineer your Qlik apps with QlikToSnow. Your data becomes governed data products, with the effort under control and the result validated.