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Customer Case The Research Institute for Nature and Forest (INBO)

The Research Institute for Nature and Forest (INBO) is the Flemish research and knowledge centre for nature and its sustainable management and use. INBO conducts research and supplies knowledge to all those who prepare or make the policies or are interested in them as a stakeholder.Through its scientific research, INBO supports all agencies dealing with open space in the Flemish government, as well as in organisations involved in nature conservation, forestry, agriculture, hunting and fishery. INBO publishes its results as open data and provides data for international reporting. It participates in (inter)national research networks such as LTER, ALTER-Net, LifeWatch, …


"The Research Institute for Nature and Forest (INBO) is radically transforming its IT-tools and services to a cloud-oriented and data driven/data science approach. For a new project about the real-time survey of invasive species, we were looking for a partner that was willing to think with and challenge our business users.

Valion brought us the perfect combination of young and eager developers willing to go the extra mile and the maturity of a business analyst/project manager to keep the scope and hold the budget. We very much appreciated the open communication and the drive to make this project a success at a reasonable price. The project gave us a lot of valuable insights and concepts for future projects and applications and to adjust to our overall data science approach."

Daniel Du Seuil Department head INBO

Know your customer

The department head of a government research institute wanted to create a semi-automated data workflow to facilitate a more efficient reporting that is compliant with the European Regulations.

This case was on the reporting of invasive species in Europe. There is a list of invasive species that need to be monitored and reported as soon as they are spotted anywhere in Europe.

Proof of Value

Based on a kick-off session, we identified the key stakeholders and their expectations.

After understanding the business context and the available data sources, we developed a prototype based on a selection of the data and a visualisation in Python and R.


After the validation of the PoV, we expanded the data architecture and iteratively added the remaining data (sources) and transformations to facilitate the desired end result. This was presented to a multi-disciplinary team, who approved and signed off on the delivery.


The result was a semi-automated data workflow tailored to the needs of the different stakeholders:

  • The business stakeholders can use their preferred tools (web portal & R)
  • The IT department can maintain the application in Python
  • Complete workflow is processed and managed in the AWS ecosystem 


  • One version of the truth, based on the raw data
  • Improved quality of the reporting towards the European Institutions
  • Improved time allocation of team members
  • Per researcher: 10% time savings per year
  • Elimination of manual efforts reduces chance of human errors