Departement of Work and Social Economy
A growing support with Data & AI.
Within this department, the Data division oversees a range of data-related activities. A key component is the Data Science and AI team, which focuses on innovative use cases that enhance the value of the Inspection and operational departments. This involves leveraging data research, advanced data science techniques, and AI engineering.
What benefits do these innovations bring to Ex Ante (preemptive) and Ex Post (retrospective) inspections? What impactful use cases can be created for the operational departments? And how have Vectr.Group consultants played a role in this?
Vectr.Consulting explored these questions with Emmanuel Thienpont, Product Manager for Data Science and Artificial Intelligence, and Roeland Juchtmans, Project Manager for the WSE Data division.
In 2016, the initial steps were taken to explore innovative approaches using data and data science methods. By late 2017, as various use cases gained traction, the need for additional expertise arose. This led to a promising collaboration with Vectr.Consulting, providing the necessary support. Around the same time, WSE made a pivotal decision to adopt a Cloud provider, with the Data Science and AI team playing a key role. This move was essential, as a Cloud environment is critical for the successful development of Data and AI use cases.
Project-specific platforms and technologies were chosen as needed.
Team Prometheus –
Greek myth or Science fiction?
A significant achievement in 2018 was the development of a fraud detection system for service vouchers, which continues to successfully support the Flemish Social Inspection. More information can be found here. This confirmed the immense value of evidence-based data research for WSE. The success of this tool immediately sparked new ideas and use cases, such as budget forecasts for service vouchers, process flow analysis through process mining, and research into parliamentary inquiries. Recently, the rise of Generative AI has opened even more possibilities.
In early 2024, the team welcomed Roeland, whose experience with the Social Economy department and expertise in data analysis brought fresh insights into what truly adds value for WSE’s operational services.
Around this time, the team was renamed Team Prometheus, symbolizing the future and the potential they see in combining existing data with modern technologies. Neither myth nor science fiction, Team Prometheus strives for practical and impactful use cases within their domain.
Over the years, numerous projects and test cases have been developed for the business, with increasing success and proven value for both the Inspection department and operational departments. These two focus areas, represented by a slight division within the team, reflect the variety of data and audiences they serve.
Evidence-based data inspectors
As mentioned earlier, the fraud detection system has proven highly valuable for the Flemish Social Inspection within WSE, inspiring the department to seek more data-driven support.
Previously, inspectors handled their own data analysis during investigations, but this approach began to hit its limits as the complexity and volume of data from increasingly large companies and organizations grew.
Since 2023, Team Prometheus has been assisting inspectors in larger cases, such as service vouchers and labor and professional cards, by delivering comprehensive analyses from multiple angles. They integrate complex datasets from various sources, including internal, government, judicial, and inspection-specific data, to uncover insights and connections between data points. By deeply analyzing and enriching the data, these aggregates provide inspectors with additional evidence and validation for their investigations. This close collaboration positions the team as “data inspectors” working alongside actual inspectors throughout the investigative process.
The results are compelling, with major cases regularly making media headlines. In the future, this type of investigation is expected to expand across various domains, with modern technologies like generative AI and graph analytics offering significant added value. Several use cases are already being explored, such as providing data-driven recommendations or alerts to case managers for recognition applications.
“The experts from Vectr are not worth their weight in gold;
they are gems. Each one is a reservoir of knowledge, further strengthened by their colleagues within the Vectr group.”
“The experts from Vectr are not worth their weight in gold; they are gems. Each one is a reservoir of knowledge, further strengthened by their colleagues within the Vectr group.”
Emmanuel Thienport, Product Manager Data Science & Artificial Intelligence
A positive contribution to operational services
As in many other organizations, operational employees are continuously occupied with planning, completing tasks, and responding to inquiries from customers and colleagues. While they are already data-driven and rely on their deep domain expertise, the fast-paced environment can make it difficult to think innovatively, particularly when it comes to technological advancements outside their usual focus. To overcome this challenge, Team Prometheus actively collaborates with the departments, targeting areas where they can deliver meaningful added value.
Frequently given answers
A highly promising use case with significant potential lies in the realm of Frequently Asked Questions (FAQ). With the growing availability of NLP technologies and new possibilities brought by generative AI, textual data can now be analyzed more efficiently and accurately.
At WSE, a large volume of unstructured data—such as the numerous daily emails—makes it nearly impossible to manually process and analyze everything, causing valuable insights to be overlooked.
One initial test case, developed in collaboration with the Department Organization Management and Development, focuses on a segment of this data: email inquiries related to training vouchers. The questions are categorized into themes, and a top five of the most frequently asked questions per theme is generated, along with the corresponding answers provided by the department. This process leverages data engineering, data science, NLP techniques, and generative AI. The outcome serves as a foundation for an FAQ truly based on actual questions and commonly given answers. If an FAQ already exists, this approach can help assess its quality and update it using rapidly available information.
Another compelling case involves the 1700 helpline, a frontline customer service that handles numerous inquiries—some structured through a scenario menu—while others, such as case status requests, are not answered directly. These are forwarded to various departments, including WSE. Annually, thousands of questions, such as those regarding encouragement bonuses, flood the operational service. By categorizing and filtering the most common queries, internal processes can be streamlined, and the information provided improved. This helps reduce the volume of incoming inquiries or enhances the 1700 line’s script. Once again, the combination of generative AI techniques with data science methods like clustering and classification has proven to be a key to success. The team collaborates closely with the Secretary General’s departments to iteratively enhance this knowledge base.
What does the future hold?
The increasing volume and availability of data present expanding opportunities for discovering connections and gaining deeper insights in the future. One potential application is calculating risk scores using available data, integrating findings and anomalies from past analyses that surpass what a human analyst could achieve. This will be accomplished through a combination of data science techniques, machine learning engineering, and domain expertise. Besides providing valuable insights, this approach will significantly enhance efficiency by automating a substantial portion of the research work, allowing
employees to focus more on cases requiring additional attention.
Additionally, the use of generative AI enhances the accessibility and comprehensibility of the vast amounts of textual data. This opens up opportunities to implement both established use cases and explore new applications in this field. For many, including those at WSE, this represents a new frontier, which can make it challenging to gauge the potential or overcome resistance to changing traditional methods. By starting with smaller-scale projects, a gradual shift in perspective can be encouraged, provided these projects deliver genuine value to the departments involved. Without clear added value, departments may be reluctant to dedicate their time and resources to validating and testing new solutions, an engagement that is essential for success.
Generatieve AI one-size-fits-all?
The answer is straightforward: No, Generative AI (GenAI) is not the solution for everything. While working with large volumes of textual data is new for many, the term “Generative AI” is often quickly associated with these scenarios. In reality, however, the real value lies in integrating traditional data science methods with existing data environments, where AI or GenAI is applied to specific aspects of the process. It therefore requires in-depth knowledge of different data domains. Expertise in GenAI alone is far from sufficient.
This perspective aligns with the notion that GenAI should not be the starting point of a project but rather a tool to enhance a solution. For instance, using MS Azure OpenAI as a GenAI application to clarify core questions from often ambiguous initial queries exemplifies this approach. While Natural Language Processing (NLP) can clean up language by removing stopwords, it cannot rewrite the text or the question itself. GenAI, on the other hand, offers significant benefits by restructuring or rewriting source data (text), which improves the efficiency of data (the questions) categorization. Consequently, vague questions can be organized more effectively, allowing a Large Language Model (LLM) to provide relevant content within the correct context.
“The consultants at Vectr.Consulting
are highly motivated and supportive, excel in teamwork,
and contribute valuable insights.”
Roeland Juchtmans, Product Manager Data
AI strategy meets bottom up data innovation
Team Prometheus works closely with colleagues responsible for delivering services and conducting inspections. This collaboration is increasing awareness of data science and AI possibilities among more team members, who are keen to leverage these technologies. For successful outcomes, senior management’s support and active promotion of this process are crucial. This commitment is evident from both the current and former Secretary-Generals, as well as from the management’s dedication to a robust AI strategy. Consequently, a “coalition of the willing” has formed, and it is hoped that this will continue to expand. Support from the workforce, combined with the strategic vision from senior management, is essential to making these applications operational.
The Department of Work and Social Economy is responsible for employment and social economy policies. This includes policy preparation, follow-up, monitoring, and inspection, along with managing the European Social Fund (via Europe WSE) in Flanders.
More information on vlaanderen.be
Sharing is caring –The data science working group
Founded by Emmanuel Thienpont in 2017, the Data Science Working Group within the Flemish Government aims to foster collaboration on innovative data initiatives. This initiative led to a partnership with MOW (Mobility and Public Works) to address parliamentary questions, building on a proof of concept (POC) previously developed within WSE at the request of the Secretary-General. Vectr.Consulting’s consultants played a key role in supporting both departments to achieve this project.
The strength of Partnership
Vectr.Consuling is committed to establishing enduring, high-quality relationships with its clients. Building trust, collaborating effectively, and recognizing each other’s strengths and weaknesses are key. Delivering the right expertise at the right time is vital.
Sharing is caring –The data science working group
Founded by Emmanuel Thienpont in 2017, the Data Science Working Group within the Flemish Government aims to foster collaboration on innovative data initiatives. This initiative led to a partnership with MOW (Mobility and Public Works) to address parliamentary questions, building on a proof of concept (POC) previously developed within WSE at the request of the Secretary-General. Vectr.Consulting’s consultants played a key role in supporting both departments to achieve this project.
The strength of Partnership
With the support of Cronos Public Services, Vectr.Consuling is committed to establishing enduring, high-quality relationships with its clients. Building trust, collaborating effectively, and recognizing each other’s strengths and weaknesses are key. Delivering the right expertise at the right time is vital.
The Department of Work and Social Economy is responsible for employment and social economy policies. This includes policy preparation, follow-up, monitoring, and inspection, along with managing the European Social Fund (via Europe WSE) in Flanders.
More information on vlaanderen.be