01
Sources and data model
We inventory sources, define fields and document mappings. Interfaces with source systems are reviewed before choosing a collection method.
- Data dictionary
- Access and retention requirements
Automate Bolder · Collection, preparation and quality
Data Pipelines
Data pipelines and data preparation
Your needs
Know where a number comes from and what it means.
Prepare usable data for your analysis and tools.
Scattered files, different formats and irregular updates make analysis difficult. We organise data collection and preparation around a defined use case, with checks and visibility into how current the data is.
Bring sources together
Collect relevant data from existing software, files or databases.
Two sources may use the same word for different things. Definitions and matching keys are reviewed before consolidation.
Make preparation more consistent
Replace recurring manual preparation with documented transformations.
Rules cover duplicates, missing values and unexpected formats. Correcting data should not hide a problem in the source.
Support a defined use
Provide data suited to a dashboard, analysis or assistant.
Each use has its own requirements for detail, freshness and access. We avoid collecting more data than the scope needs.
What we deliver
From identified sources to usable data.
02
Collection and transformation
We build collection, matching and validation processes. Outputs can feed internal dashboards or analysis tools.
- Processing scheduled to fit the need
- Data quality checks
03
Monitoring and documentation
We prepare alerts, traceability and recovery procedures. For an AI assistant, preparation may also cover document updates and preservation of access permissions.
- Monitoring for delays and anomalies
- Transformation documentation
How we work
Start with the use case and work back to the sources.
We first define the information needed and the decisions it should support.
1. Examine the data
Check sources, quality and differences in definitions.
A sample helps identify unexpected formats or values. Available history and export limitations affect the achievable scope.
2. Build and compare
Implement transformations, then compare outputs with sources.
Checks may cover volumes, totals and required fields. Discrepancies should be explained before results are shared.
3. Monitor over time
Schedule processing and organise anomaly monitoring.
A renamed field or changed format can interrupt a flow. Documentation and alerts help identify which dependency needs updating.
Your questions about data pipelines
How does a data pipeline differ from an integration?
An integration lets tools exchange information to work together. A pipeline organises collection and transformation for a use case, often analysis. The two can complement each other and share some connections.
Do we need a data warehouse?
Not always. The choice depends on volumes, history, source count and planned analysis. We review existing tools before adding infrastructure. Hosting, operation and maintenance costs are part of the decision.
How often is the data updated?
Frequency is defined by the use case and source capabilities. Daily updates may suit an analysis, while an operational use may need fresher data. The system should make delays or incomplete processing visible.
Can we reuse historical data?
We review availability, quality and compatibility with current definitions. Historical migration may require specific rules and additional checks. Reliable periods and known limitations should be documented alongside the data.
Which data do you need to bring together?
Tell us about your sources, the intended use and current preparation difficulties.
This service is part of Automate Bolder. Explore related services: Internal Tools & Dashboards · System Integrations.