Introduction: The Role of Tenders in the Modern EU Economy and the Importance of Aggregation
Tender procurement plays a critical role in the economy of the European Union (EU), promoting transparency, fair competition, and the effective use of public funds—particularly within the public sector. EU legislation mandates that public procurement be conducted through open tenders to prevent corruption and ensure value for money for buyers. With the tender market accounting for approximately €2 trillion—roughly 14% of the EU’s GDP—this sector is a core element of economic activity across the region.
Despite common legal requirements, tender announcements are scattered across multiple platforms, including national and private websites. For example, TED (Tenders Electronic Daily) serves as the official portal for EU tenders, while platforms like Deutsche eVergabe in Germany or BOAMP in France cater to national-level procurement. This diversity in sources presents challenges for businesses seeking relevant opportunities, as navigating these fragmented systems can be time-consuming and inefficient.
To address this problem, specialized SaaS platforms have emerged, aggregating tenders from different sources and simplifying access for businesses. These platforms help companies operate more efficiently by offering tools for analytics and personalization—enabling businesses to quickly process data and make informed decisions about participation in tenders.
Aggregated tender data is especially useful for consulting firms and intermediaries involved in procurement, as it helps them provide clients with tailored insights and automated solutions. This ultimately improves the efficiency of procurement processes and enhances companies’ ability to identify relevant tender opportunities.
Key Challenges in Tender Data Unification
Even though the EU has strict guidelines for publishing tenders, data aggregation still faces several challenges. Tenders are announced across various platforms to meet legal requirements and reflect national market differences. High-value public tenders must be published on TED, ensuring transparency across member states. However, each country also maintains its own procurement platforms—such as BOAMP in France, Consip in Italy, or Deutsche eVergabe in Germany—where tenders may also be posted.
If a tender falls below certain thresholds, businesses are free to publish it on other websites, such as private portals or their own sites, in compliance with EU directives. This multiplicity of sources creates difficulties for companies, as announcements are often published in different formats and structures.
While the EU mandates essential elements—like project descriptions, deadlines, and budgets—formats may vary across platforms, posing further challenges for aggregation. Businesses must sift through disparate data to ensure consistency and relevance.
OCDS in European Public Procurement: Benefits and Adoption Trends
The Open Contracting Data Standard (OCDS), developed by the Open Contracting Partnership in 2014, aims to increase transparency by providing a standardized format for procurement data. While many countries in Latin America, Africa, and the U.S. have adopted this standard, the EU follows its own public procurement regulations based on EU Directives.
Some EU member states and individual organizations use OCDS voluntarily to enhance transparency and streamline data analysis. Although it facilitates easier comparisons between markets, OCDS is not mandatory for all EU tenders.
So, given the complexity of tender data collection, three main challenges stand out, as discussed below.
Main Tender Data Aggregation Challenges
Data Format Inconsistencies: Each platform uses its own data structure. Some announcements may lack critical details like categories or contact information.
Complex Data Analysis: Collected data often requires enrichment—such as categorizing tenders using AI or filling in missing fields automatically.
Dynamic Updates: Tender announcements are frequently updated, and aggregation systems need to continuously track changes to ensure the latest information is available.
Specialized SaaS platforms help solve these challenges by unifying tender data from various sources into a single format, improving access to information and enabling better decision-making for businesses.
Who Benefits from Aggregated Tender Data?
Both SaaS platforms offering subscription-based access and large enterprises integrating data into their systems rely heavily on aggregated tender information. SaaS providers enable users to search and participate in tenders easily, while enterprises use this data to streamline internal processes, reduce analytical costs, and maintain up-to-date access to tender opportunities—critical for staying competitive.
Below are common approaches businesses use to access tender data:
1. Streamlined Data Access via API
Businesses can purchase ready-made datasets or subscribe to APIs from aggregators who gather data from various sources.
Advantages:
- Quick access to data with integration into internal systems.
- Lower upfront costs, though long-term expenses may increase.
Disadvantages:
- Limited control over data quality and flexibility.
- Competitors may use the same data, reducing competitive uniqueness.
2. Building an In-house Data Aggregation System
Some companies develop custom platforms that collect data through web scraping or API integration. This provides full control over the process.
Advantages:
- Maximum control over data and customization.
- Cost-effective in the long term.
Disadvantages:
- Time-consuming development process.
- Requires specialized expertise and investment in infrastructure.
3. Outsourcing Data Aggregation to Specialists
Outsourcing the development of a data aggregation system is an efficient option for many businesses. GroupBWT specializes in providing such services, ensuring a comprehensive approach from data collection to analysis and integration.
Advantages:
- Faster implementation with customization.
- High-quality data gathered from various sources.
- Lower maintenance costs post-development.
Disadvantages:
- Higher initial investment.
How the Tender Data Aggregation System Works
When you choose to outsource the development of a tender data collection system, the process will look like this. Since GroupBWT specializes in such services, we can share our expertise and ensure the efficiency of each step. It’s important to note that it’s not enough to simply reach out to a web scraping service provider — scraping is only part of the broader process of collecting, processing, and unifying tender data. Our approach covers every necessary phase, from data collection to in-depth analysis and integration.
Key Steps of the Process:
Selecting and Configuring Sources:
The client identifies the data sources to be aggregated, based on their business goals and target markets. These may include hundreds of sources, such as national tender platforms like France’s BOAMP, Germany’s Deutsche eVergabe, international platforms like TED, or specialized private databases. GroupBWT’s team can also advise on additional sources that may benefit the analysis.
Data Scraping and Integration:
Various technologies, including web scraping and API integrations, are employed to collect tender data. These processes automate the extraction of information in real-time or on a set schedule. Even when APIs are unavailable, the system ensures comprehensive data extraction, regardless of the platform’s structure or technical constraints.
Data Unification and Standardization:
Since the collected data comes from different sources, formats may vary. At this stage, the system converts everything into a unified structure — for example, normalizing date formats, currencies, and contact information. This ensures compatibility for subsequent analysis and processing, using specialized data transformation algorithms.
AI-Driven Data Enrichment:
AI plays a critical role in enriching and refining the data. If a tender announcement lacks specific details, the system can use contextual information to automatically fill in gaps, such as product categories or industry classifications. AI also tracks document versions and updates, ensuring access to the latest information at all times.
Monitoring Updates and Changes:
The system regularly checks for updates in tender announcements, automatically refreshing the data when terms, deadlines, or specifications change. This is crucial for businesses operating in competitive markets, where quick, accurate decisions are essential.
Analytics and Visualization:
Once the data is collected, cleaned, and enriched, the system offers convenient tools for analysis. Users can access dynamic tables, graphs, or interactive dashboards to quickly find relevant tenders, assess opportunities, and make informed decisions. Automated alerts for new or modified tenders can also be configured based on custom criteria.
Integration with Client Systems:
GroupBWT’s solutions seamlessly integrate with internal ERP, CRM, or other management tools, automating workflows and data-driven actions. This allows businesses to incorporate the collected data smoothly into their daily operations.
In summary, GroupBWT’s tender data aggregation system stands out by unifying hundreds of parsers under one platform, a task far more complex than building a single scraper. With over 15 years of experience, we’ve perfected the art of creating scalable systems that provide companies with quick access to up-to-date tender information, deep insights, and automation. This gives businesses a competitive edge, while SaaS platforms can offer their users unique datasets that are often unavailable from other providers.
Data QA: Automation and Manual Control in Tender Aggregation Systems
Ensuring data quality (QA) is a critical part of the tender information aggregation process, guaranteeing data accuracy and completeness. Both automated and manual quality checks are applied to prevent data loss and quickly respond to changes. Below are practical examples based on real-world experience.
Metrics for monitoring are defined by the team based on business needs or client requests. For instance, if it’s essential that all tenders include the contact details of buyers, a monitor is set up to track entries lacking this information. Metrics can be adjusted or added during the process, depending on what is critical to the client.
Automated QA
Automated monitoring involves the regular checking of key metrics using tools like Grafana, which connect to databases, run queries, and visualize results. This approach helps identify anomalies, such as a sudden drop in data volume or missing categories, while also generating alerts automatically, saving time on manual inspections. It’s especially effective for projects with a large number of sources, though it has limitations — it only responds to predefined metrics and requires adjustments when data structures change.
Monitoring Key Metrics:
The system analyzes the number of tenders daily, broken down by category, checks for missing titles, and tracks the absence of contact information.
Example:
The system automatically calculates how many tenders lack contact information for buyers or organizations.
Using Grafana for Monitoring
Grafana is configured based on the required frequency and method of monitoring by executing database queries and displaying results in graphs.
Example:
Grafana identified that on a particular day, the system failed to retrieve the buyer’s name in only one entry. This was classified as a non-critical issue since, after checking the source, it was confirmed that the information was not collected because it was missing at the source itself.
Anomaly Alerts
The system notifies teams of unusual situations, such as a significant drop in collected tenders or a rise in missing data.
Example:
If errors in data collection from a specific site suddenly increase, the system generates an alert, prompting the team to investigate whether the site’s structure has changed.
Manual QA
Manual monitoring remains a key component of the data verification process, as specialists regularly analyze results, even when automated tools are in place. Automation is applied wherever possible to streamline the process, but manual oversight ensures the detection of specific issues or changes, such as modifications in website structure that may cause data collection errors. Specialists not only identify such changes but also promptly create bug reports and adjust scrapers.
Manual monitoring is especially important for analyzing non-critical errors (e.g., incorrect currency formats or missing buyer names) and for projects with limited automation. This manual approach complements automated processes, ensuring high data quality and quick responses to changes that the system might not account for.
Monitoring Changes on Data Sources
Automation can highlight discrepancies but cannot pinpoint exact changes on a site, requiring manual intervention.
Example:
On a given day, specialists discovered that the structure of a source site had changed, preventing the scraper from recognizing the new format for buyer names. This prompted adjustments to the scraper’s settings.
Manual Review of Non-Critical Errors
When the team notices unusual deviations in the data graphs.
Example:
Even if Grafana does not flag an issue, specialists may still open the relevant site to manually verify whether the buyer name is genuinely missing or if the format has changed.
Error Fixes via Bug Reports
If critical errors are found, the team creates bug reports to update the scraper.
Example:
Changes on a site required updates to the data collection algorithm, as the system no longer recognized currency fields where the dollar sign now appeared before the number instead of after it.
Interaction Between Automation and Manual Control
Automated systems, such as Grafana, help reduce the workload on the team but do not replace it entirely. The team responds to alerts and checks data that requires manual intervention.
When dealing with a large number of sources (over 250), automation is essential for efficient operations. However, for smaller projects with only a few sources, automation may be excessive.
Automated QA significantly reduces the time spent on data verification, especially for large projects with numerous sources. Still, manual control is necessary to address specific issues and adapt to changes. Using Grafana in large-scale projects helps streamline the verification process and ensures timely responses to anomalies, while manual control remains essential for handling non-standard situations.
Conclusion
Unifying tender data across the EU is a complex but vital process that involves collecting, standardizing, and deeply analyzing data from multiple sources. Custom tender aggregation software helps businesses optimize procurement processes, reduce costs, and make better decisions. Despite challenges like data inconsistency and dynamic updates, AI-powered and automated tender data enrichment significantly streamlines the process.
Need a custom aggregation solution for EU tenders? Schedule a free consultation with GroupBWT experts to discuss your specific needs. Learn more about how we’ve helped other businesses succeed with similar projects—contact us today and discover how our tailored solutions can transform your procurement strategy into a competitive advantage!
