Reducing human factors, increasing data quality through Data Collection Automation Process
Mar 30, 2023
Human Factors
Collection Time
Yearly Costs
Factors
Background
A performance analytics engine is offered by the client’s web application. It uses validated data and mathematical modelling to improve transparency, benchmark performance and support reliable decision-making.
The raw data is gathered, assessed and validated by the client’s own team, together with analysts and outside suppliers. In order for the model to produce useful outputs, the source data had to become more structured, more consistent and less dependent on manual effort.
Before
Every month, a team member had to connect to a specific portal, collect the required data, download it in CSV format and manually import it into the web application. The process was repetitive, slow and highly dependent on one person.
Let’s analyze the facts
- One dedicated employeeThe task depended on a specific person every month.
- 35 hours monthlyA significant recurring operational effort.
- 3,000€ yearlyEstimated direct labour cost for the activity.
- Human error or availabilityData quality and timing depended on manual execution.
- 600 interest factors collectedThe process limited how much data could be used.
Using the Data
Once data collection was completed, the update of the web application was also performed manually by uploading the CSV files and waiting for the calculation results to be generated.
The employee then had to inspect the results, verify the update and proceed with any additional adjustments required by the process.
The Solution
WEDEVA introduced an automated data flow able to collect, stage, validate and process data from multiple sources in a controlled way. The objective was not only to save time, but also to create a cleaner and more dependable operational process.
The architecture reduced manual intervention and gave the client a single, more reliable view of the dataset before the information reached production use.
Data Pipeline
A structured data pipeline was designed to move information from source systems to a controlled destination, then through transformation and validation stages, and finally into production where it could support reporting and business decisions.
Data Collection
Source
Authenticated extraction and targeted filtering from external data sources.
Data Storage
Destination
Temporary repository where the collected data is staged before further handling.
Data Transformation
Processing
Standardisation, validation and verification so the data becomes fit for use.
Data Implementation
Production
Automated delivery into production with monitoring and alerting capabilities.
After
By moving away from the legacy process, the client became more data-driven, more efficient and less exposed to operational disruption. Work that previously took many manual hours was reduced to a much lighter supervisory task.
The improvement was not limited to payroll savings. The business was also able to work with a greater volume of better quality data, which had a direct positive effect on productivity and decision-making.
Let’s analyze the facts
- No dedicated employeeThe process no longer depends on one specific person.
- 1 hour monthlyOnly light supervision remained.
- 500€ yearlyReduced operational overhead.
- Automated processStable, repeatable and more resilient workflow.
- 3,000 interest factors collectedFar greater data collection capacity.
Business Impact
The update of the web application became automated, while human review remained only where business control was genuinely needed. This created a more efficient process without sacrificing reliability.
Want to reduce manual data work?
WEDEVA can review your collection, validation and reporting workflow and identify where automation will create measurable operational value.