Technical risk
An unsuitable transformation can change the granularity of information.
Load-script structure
Mapping loads, joins, concatenations, group by and filters were selected according to the relationship between sources.
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CASE 03 / Operational intelligence
Operational intelligence: connecting data to interpret the process
I built an analytical platform connecting more than 80 tables across quality, production, maintenance, delivery and materials, reducing repeated consolidation while retaining history for analysis.
03.1 / Context
Manual consolidation of scattered sources
Engineers and technical professionals needed to consult and consolidate scattered information before interpretation could begin. The same preparation was repeated at different times by different people.
Investigating the problem
The problem preceded visualization: gathering materials, quality, production, maintenance and delivery data required repeated preparation. Modeling had to make the relationships between those industrial domains explicit.
Implementation constraints
Sources had different structures and relationships. Integration had to retain history and run within the available infrastructure, with automated routines supported by a dedicated computer.
FragmentationQuality, production, maintenance, delivery and materials in separate sources.
Repeated effortManual preparation reducing the time available for investigation.
Limited horizonDifficulty comparing periods and understanding relationships between events.
03.2 / Development
Source integration and associative modelling
The platform combined source preparation, Qlik Load Script transformations, associative modelling and analytical applications. It retained history, reduced repeated data preparation and kept relationships between operational domains explicit.
- Industrial sources
Quality · production · maintenance · delivery · materials
- Supporting routines
Python · VBA · SQL — extraction and integration
- Qlik Load Script
Mapping · joins · concatenation · filters · history
- Associative model
Controlled keys · relationships across 80+ tables
- Analytical use
Dimensions · measures · decisions in context
Select a stage to explore its role in the flow.
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03.3 / Project decisions
Associations, history and refresh routines required explicit choices.
Three decisions supported model integrity and refresh reliability.
Technical risk
Ambiguous associations can connect events without the intended relationship.
Key management
Synthetic keys were identified and resolved to preserve intentional associations and avoid ambiguity.
Technical risk
Refresh routines also depend on the availability of their execution environment.
Routine orchestration
Python and VBA supported integrations; a dedicated computer sustained automated routines within the available infrastructure.
From decision to implementation
- Prepare the sources
Python, SQL and VBA supported extraction and integration. Data preparation was separated from indicator interpretation.
- Build the relationships
Mapping loads, joins, concatenation, grouping and filters organized the load. Resolving synthetic keys preserved intentional associations.
- Make analysis available
The associative model connected industrial domains, retained history and enabled more frequent updates to support operational interpretation.
80+tables connected in the associative data model
03.4 / Evidence and outcomes
The same model began supporting analysis across quality, production and maintenance.
The platform centralised information, increased availability and enabled more frequent updates. Its main effect was shifting effort from repetitive preparation to the interpretation of patterns, risks and relationships.
- an integrated view across operational dimensions;
- less duplicated preparation and consolidation;
- more historical context for comparison;
- better context for analysis and decisions.
Verification / interpreting the evidence
What the evidence establishes
More than 80 tables were connected in the model. This describes integration coverage; it does not, by itself, measure accuracy or financial benefit.
Engineering criterion
Interpreting an indicator requires attention to the selected associations and time period. Connecting sources is useful only when relationships preserve the industrial meaning of the data.
Limits of the public evidence
The portfolio does not include corporate samples, reconciliation test records or an accuracy measurement series. None of these outcomes is inferred from the table count.
03.5 / Synthesis
Learning
Integrating sources required control of keys and history, with orchestration that fitted the available infrastructure.