Tables and databases
Relational databases and CSV. Records, fields and relationships to align.
Data Structuring
Different sources.
One consistent picture.
We transform diverse data into a consistent structure, combining sources, identifying key fields and preparing information for analysis and your systems.
Sources → Structure → Analysis
Order in your dataFrom source formats to a common schema
Four source typesDifferent formats.
Different formats.
A shared goal.
Review structure, volume and access rules, then select processing for each source.
JSON, XML and logs
Events and data from different services, whose structures may vary.
Documents and emails
PDFs, text documents and email. Extract required fields under agreed rules.
Media
Images, video and audio. Use metadata and, where required, specialised content processing.
Transformation illustratedPreviously fragmented.
Previously fragmented.
Now comparable.
Select a source. This illustrative example shows source values mapped to a common schema.
Source data
item_id; title; state DEMO-01; Module A; ready DEMO-02; Module B; hold
Mapped fields
| ID | Name | Status |
|---|---|---|
| DEMO-01 | Module A | Ready |
| DEMO-02 | Module B | Validation |
Agree field names, types, status values and record keys.
Source data
{"id":"DEMO-01", "status":"ok"}
<record key="DEMO-02" state="pending"/>
Mapped fields
| ID | Source | Status |
|---|---|---|
| DEMO-01 | JSON | Ready |
| DEMO-02 | XML | Validation |
Map equivalent fields in different formats to the common schema.
Source data
Document: DEMO-01 Item: Module A Status: ready for processing
Mapped fields
| ID | Item | Status |
|---|---|---|
| DEMO-01 | Module A | Ready |
Extracted fields and validation methods are agreed using document samples.
Source data
demo-video.mp4 · frame 00:12 demo-image.png · object A
Mapped fields
| Type | Time | Event |
|---|---|---|
| Video | 00:12 | Object A |
| Image | — | Object A |
Combine metadata and recognition results with other sources. Content processing depends on the task.
How we workA clear process.
A clear process.
A verifiable result.
Preserve source data meaning and agree transformation rules before automation.
-
01
Transformation
Convert data into agreed structures and types.
-
02
Combining sources
Map equivalent fields and align source keys.
-
03
Priorities
Identify the data that matters for your task.
-
04
Visualisation
Present results clearly for review and analysis.
What your team receivesStructured data.
Structured data.
Clear deliverables.
Outputs are defined for the project: prepare a dataset or build repeatable processing.
Data dictionary
Names, types, values and interpretation rules.
Mapping rules
How source fields and statuses relate to the common schema.
Consistent dataset
Combined data in a format suitable for your system or analysis.
Result verification
Clear presentation, quality reporting and identified ambiguities.
Quality controlStructure should
Structure should
preserve meaning.
Renaming fields is not enough. We check that data remains comparable and useful.
Check
consistency.
Identify missing values, duplicates and format mismatches. Examine values, units and record relationships.
- Types and permitted values
- Key and merge rules
- Sources and field lineage
Resolve
ambiguity.
Agree uncertain mappings and exception handling. Missing information is not replaced by guesswork.
- Ambiguous and incomplete records
- Validation against agreed examples
- New data processing procedures
Experience and related solutionsPrepare data.
Prepare data.
Open up possibilities.
Projects involving dataset preparation and information extraction from images.
Data preparation
Earth remote sensing
Creating datasets to analyse the Earth's surface.
Read the project
Industrial recognition
QR codes in streaming video
Developing and improving production line marking recognition.
Read the project
Next stepPrepared data.
Prepared data.
New tasks.
A consistent dataset can support forecasting, personalisation and computer vision.
Questions answeredDiscuss the essentials.
Discuss the essentials.
Before launch.
Which sources should we start with?+
Those relevant to a specific decision. Define the business task, available sources and intended outcome, then select the first stage.
Can you process documents and media?+
Yes. Extraction scope and methods are defined using samples. Metadata, text and recognition outputs need different methods and quality checks.
What happens to missing values and mismatches?+
We identify them and agree handling rules: exclusion, clarification or another acceptable method. Imputed values are not automatically treated as verified facts.
Can data be updated regularly?+
We can design repeatable processing. Frequency, sources, integration and quality checks are agreed for the process.
Start with your challengeBring your data together.
Bring your data together.
For your requirements.
Tell us about your sources, formats and the picture you need to build.
- Sources and format examples
- Fields, relationships and mapping rules
- Output and target system
Solution scope, timelines and terms are agreed for your project.
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