SUCCESS STORY | EUROPEAN LEADER IN MARITIME TRANSPORT AND LOGISTICS
Maritime Transport｜From Qlik reporting to Snowflake data products
This maritime transport player operates ≈ 600 Qlik dashboards whose business rules live within the tool.
With Qlik to Snowflake and AI-powered tooling, KPC performs the reverse engineering.
The data impact
~600
Qlik applications in production to be migrated to Snowflake
≈2 d
Fixed analysis fee per app
0 prerequisites
The analysis remains possible without up-to-date specs or the original developer.
Between 10 and 40 days
of manual load avoided.
A long and unpredictable analysis phase replaced by a tool-supported process.
The data impact
~600
Qlik applications in production to be migrated to Snowflake
≈2 d
Fixed analysis fee per app
0 prerequisites
The analysis remains possible without up-to-date specs or the original developer.
Between 10 and 40 days
of manual load avoided.
A long and unpredictable analysis phase replaced by a tool-supported process.
The data impact
~600
Qlik applications in production to be migrated to Snowflake
≈2 d
Fixed analysis fee per app
0 prerequisites
The analysis remains possible without up-to-date specs or the original developer.
Between 10 and 40 days
of manual load avoided.
A long and unpredictable analysis phase replaced by a tool-supported process.
RESULTS
Concrete results
Data products, not dashboard copies
The data is reconstructed at the refined level on Snowflake. It remains reusable beyond the original report.
Concrete results
A fixed analysis fee (≈ 2 days)
The duration remains fixed regardless of the app's complexity. The absence of documentation or the original developer does not block the analysis.
Concrete results
A validated result against Qlik
The Match Control AI agent compares the data product to the existing one and validates the end-to-end development. The produced spec is reliable and aligned with the actual report.
RESULTS
Concrete results
Data products, not dashboard copies
The data is reconstructed at the refined level on Snowflake. It remains reusable beyond the original report.
Concrete results
A fixed analysis fee (≈ 2 days)
The duration remains fixed regardless of the app's complexity. The absence of documentation or the original developer does not block the analysis.
Concrete results
A validated result against Qlik
The Match Control AI agent compares the data product to the existing one and validates the end-to-end development. The produced spec is reliable and aligned with the actual report.
RESULTS
Concrete results
Data products, not dashboard copies
The data is reconstructed at the refined level on Snowflake. It remains reusable beyond the original report.
Concrete results
A fixed analysis fee (≈ 2 days)
The duration remains fixed regardless of the app's complexity. The absence of documentation or the original developer does not block the analysis.
Concrete results
A validated result against Qlik
The Match Control AI agent compares the data product to the existing one and validates the end-to-end development. The produced spec is reliable and aligned with the actual report.
Context & central challenges
Les enjeux
This 3rd largest shipowner in the world operates around 600 Qlik dashboards in production, while a Snowflake foundation has been under construction for three years to centralize its data.
Problem: the business rules lived within Qlik; calculations, filters, transformations, dependencies, workflows. The specs and documentation were virtually non-existent or misaligned with the actual report.
Bring refined data back into Snowflake in the form of data products (not a lift & shift)
Migrate without depending on non-existent or outdated specifications or documentation
Industrializing a reverse engineering workload that was previously variable and difficult to estimate
Guaranteeing users consistency with existing Qlik reporting
Context & central challenges
Les enjeux
This 3rd largest shipowner in the world operates around 600 Qlik dashboards in production, while a Snowflake foundation has been under construction for three years to centralize its data.
Problem: the business rules lived within Qlik; calculations, filters, transformations, dependencies, workflows. The specs and documentation were virtually non-existent or misaligned with the actual report.
Bring refined data back into Snowflake in the form of data products (not a lift & shift)
Migrate without depending on non-existent or outdated specifications or documentation
Industrializing a reverse engineering workload that was previously variable and difficult to estimate
Guaranteeing users consistency with existing Qlik reporting
Context & central challenges
Les enjeux
This 3rd largest shipowner in the world operates around 600 Qlik dashboards in production, while a Snowflake foundation has been under construction for three years to centralize its data.
Problem: the business rules lived within Qlik; calculations, filters, transformations, dependencies, workflows. The specs and documentation were virtually non-existent or misaligned with the actual report.
Bring refined data back into Snowflake in the form of data products (not a lift & shift)
Migrate without depending on non-existent or outdated specifications or documentation
Industrializing a reverse engineering workload that was previously variable and difficult to estimate
Guaranteeing users consistency with existing Qlik reporting
Our approach
Tool-supported reverse engineering, from dashboard to spec
Re-evaluating the need from scratch would risk a gap with what currently exists. KPC therefore rebuilds from Qlik through reverse engineering, powered by QlikToSnow. The analysis is repeated as often as necessary and produces a usable specification. This specification becomes the entry point for an AI-assisted Snowflake build using Cortex Code, in an expert-in-the-loop cycle: the AI proposes, the expert validates.
Extraction
Extract the app's logic
QlikToSnow automatically extracts business and calculation rules, dependencies, and workflows from the app.
Data analysis
Profile and map
QlikToSnow profiles the data and identifies what is actually used. The tool decides on the field mapping and makes the lineage navigable. The impact analysis shows which fields feed which metrics, without diving back into the code.
Match control & spec
Validate and specify
End-to-end validation against Qlik (or an existing data product) and production of a reliable spec, aligned with the actual report.
Our approach
Tool-supported reverse engineering, from dashboard to spec
Re-evaluating the need from scratch would risk a gap with what currently exists. KPC therefore rebuilds from Qlik through reverse engineering, powered by QlikToSnow. The analysis is repeated as often as necessary and produces a usable specification. This specification becomes the entry point for an AI-assisted Snowflake build using Cortex Code, in an expert-in-the-loop cycle: the AI proposes, the expert validates.
Extraction
Extract the app's logic
QlikToSnow automatically extracts business and calculation rules, dependencies, and workflows from the app.
Data analysis
Profile and map
QlikToSnow profiles the data and identifies what is actually used. The tool decides on the field mapping and makes the lineage navigable. The impact analysis shows which fields feed which metrics, without diving back into the code.
Match control & spec
Validate and specify
End-to-end validation against Qlik (or an existing data product) and production of a reliable spec, aligned with the actual report.
Our approach
Tool-supported reverse engineering, from dashboard to spec
Re-evaluating the need from scratch would risk a gap with what currently exists. KPC therefore rebuilds from Qlik through reverse engineering, powered by QlikToSnow. The analysis is repeated as often as necessary and produces a usable specification. This specification becomes the entry point for an AI-assisted Snowflake build using Cortex Code, in an expert-in-the-loop cycle: the AI proposes, the expert validates.
Extraction
Extract the app's logic
QlikToSnow automatically extracts business and calculation rules, dependencies, and workflows from the app.
Data analysis
Profile and map
QlikToSnow profiles the data and identifies what is actually used. The tool decides on the field mapping and makes the lineage navigable. The impact analysis shows which fields feed which metrics, without diving back into the code.
Match control & spec
Validate and specify
End-to-end validation against Qlik (or an existing data product) and production of a reliable spec, aligned with the actual report.
Our clients
Our clients
They trust us.
They trust us.
Retail & Beauty
Rag & Bone — Data platform optimization and preparation for AI use cases
Data
Data Platform
→
Retail & Beauty
Marc Jacobs — Redesign of the data foundation in the midst of ERP transformation
Data
Data Platform
→
Finance & Banking
FLOA — A Data platform reviewed and brought back to the state of the art
Finance & Banking
Snowflake
→
Maritime transport
CMA CGM | A single OneStream platform to consolidate, budget, and steer
OneStream
Statutory consolidation
→
Car financing
Toyota Financial Services｜170,000 customers, 100% automated customer journeys on imagino
CRM
Snowflake
→
International public sector
CEB｜ERP transformation on SAP Cloud, at the service of social cohesion
SAP
ERP
→
Retail & Beauty
Rag & Bone — Data platform optimization and preparation for AI use cases
Data
Data Platform
→
Retail & Beauty
Marc Jacobs — Redesign of the data foundation in the midst of ERP transformation
Data
Data Platform
→
Finance & Banking
FLOA — A Data platform reviewed and brought back to the state of the art
Finance & Banking
Snowflake
→
Maritime transport
CMA CGM | A single OneStream platform to consolidate, budget, and steer
OneStream
Statutory consolidation
→
Car financing
Toyota Financial Services｜170,000 customers, 100% automated customer journeys on imagino
CRM
Snowflake
→
International public sector
CEB｜ERP transformation on SAP Cloud, at the service of social cohesion
SAP
ERP
→
Retail & Beauty
Rag & Bone — Data platform optimization and preparation for AI use cases
Data
Data Platform
→
Retail & Beauty
Marc Jacobs — Redesign of the data foundation in the midst of ERP transformation
Data
Data Platform
→
Finance & Banking
FLOA — A Data platform reviewed and brought back to the state of the art
Finance & Banking
Snowflake
→
Maritime transport
CMA CGM | A single OneStream platform to consolidate, budget, and steer
OneStream
Statutory consolidation
→
Car financing
Toyota Financial Services｜170,000 customers, 100% automated customer journeys on imagino
CRM
Snowflake
→
International public sector
CEB｜ERP transformation on SAP Cloud, at the service of social cohesion
SAP
ERP
→
A SIMILAR PROJECT IN MIND?
A Qlik environment to transform into data products on Snowflake?
Our Data & AI teams reverse-engineer your Qlik apps with QlikToSnow. Your data becomes governed data products, with controlled costs and validated results.
A SIMILAR PROJECT IN MIND?
A Qlik environment to transform into data products on Snowflake?
Our Data & AI teams reverse-engineer your Qlik apps with QlikToSnow. Your data becomes governed data products, with controlled costs and validated results.
A SIMILAR PROJECT IN MIND?
A Qlik environment to transform into data products on Snowflake?
Our Data & AI teams reverse-engineer your Qlik apps with QlikToSnow. Your data becomes governed data products, with controlled costs and validated results.