Data Science: How JADS MKB Datalab is acting as a bridge between theoretical and practical application in the Netherlands

In the AI Startup of the Week, the editorial staff of ai.nl is featuring promising AI startups, their innovations, solutions and challenges. In this sixteenth episode, we are taking a look at JADS MKB Datalab, which is trying the difficult task of bridging the gap between latest developments in the field of data science and their practical applications.

One of the clearest trends post pandemic is small and medium enterprises gearing up to embrace technology and digitisation like never before. The companies that once wanted to stay away from adapting digital tools are lining up to digitise themselves. However, when it comes to adopting AI and embracing data science, these companies tend to struggle understanding the need or relevance in their industry.

The MKB Datalab at JADS, the Jheronimus Academy of Data Science, wants to make it easier for companies at different levels to adopt data science projects. The Datalab particularly wants to take the knowledge and skills of a data scientist and make it accessible to SMEs.

Helping Dutch SMEs in their digital transition

A Salesforce report from early this year showed that around 83 per cent of SMBs have at least some of their operations online now. The report also revealed nearly 95 per cent moved a portion of their operations online in the past year. However, a report from Fellowmind revealed that the Netherlands is lagging behind its European counterparts when it comes to adoption of AI and robotics.

With AI and robotics being a central part of data science, the Jheronimus Academy of Data Science wants to fix this gap. The JADS MKB Datalab has the vision of supporting Dutch SMEs in their digital transition and aims to help them embrace data-driven ways of working.

The JADS MKB Datalab is a collaboration between the Jheronimus Academy of Data Science, Tilburg University, and Eindhoven University of Technology (TU/e). The reason for the existence of JADS MKB Datalab is the fact that a number of entrepreneurs don’t yet understand the concept of data science.

The MKB Datalab not only serves the purpose of helping these entrepreneurs understand data science and implement it in their business but also helps the data science students. The JADS Data Science master students take up concrete defined projects within these SMEs to help them understand and implement data science.

JADS SMB Datalab: how does it help SMEs?

The mission and vision of datalab is so clear that it has built its own four step roadmap to help SMEs learn and adopt data science. These four steps are intake interview, workshop, project, and evaluation. Here is a look at each of these steps in detail.

  • Intake interview: The first step in its effort to help Dutch SMEs begin with an intake interview where the data science students at JADS look at all the possibilities for an organisation. Some organisations tend to have a concrete idea for a project while many others discover the potential during this interview.
  • Workshop: The workshop is essentially the step between understanding the potential and creating a real project. The data-driven business workshop sees SMEs fill in a data maturity scan and get insights into where they stand based on their data. The workshop also acts as a vehicle to define challenges and opportunities.
  • Project: The intake interview and workshop are meant to create a concrete issue to get started with. Then a master student in Data Sciences from JADS is asked to tackle this issue in a time period of 60-80 hours spread over 8 weeks. The end result, according to JADS, is to reach a conclusion that allows companies to embrace data-driven working.
  • Evaluation: The evaluation, as the name gives away, is meant to help companies evaluate whether there is a workable result. At JADS SME Datalab, this stage is also used to look for any potential ways to continue the collaboration and explore the next challenge.

What are the tools offered by JADS MKB Datalab?

In order to bridge the gap between data science being taught at schools and universities, and the ones being applied at companies, JADS MKB Datalab has designed a set of tools. These tools are like barometers to understand the preparedness of a SME and build on its abilities.

The first tool is data maturity scan, which requires SMEs to answer five questions to complete a quick scan. This quick scan offers an immediate answer to data maturity of your company. Based on this score, the datalab estimates the logical next step for your company.

The second tool is called data project canvas, which offers a snapshot of opportunities and challenges with regards to working with data. Data sources, the third tool looks at availability of all public data sources while the final tool is designed to help SMEs in their data-driven working by understanding where to start and what knowledge has been collected for this transformation.

JADS MKB Datalab and its data science implementation

While JADS SME Datalab has done a number of data science projects in collaboration with small and medium enterprises, its vacancy monitoring tool for AcademicTransfer is an equally interesting product.

The PowerBI dashboard analyses the interest of academics in vacancies from different universities in a time-consuming process. The dashboard offers a direct link with Google Analytics and the vacancy data allowing AcademicTransfer to compare vacancies at a glance and easily share the insights with its members.

The SME Datalab has also worked with RAVU, one of the largest ambulance facilities in the Netherlands. The datalab has helped RAVU with scheduling employees on shifts, which is particularly challenging since RAVU needs to plan for day, evening, and night shifts, needs to ensure that work continues on weekends, and most importantly, avoid assigning an employee four night shifts in a row.

The JADS MKB Datalab designed a tool that begins with an excel file containing grids made by the employee specifying their ideal 8-week grid. The tool based on a Python script reads the preferences and then optimises the schedule based on the required capacity. The schedule is made for half a year by this tool and RAVU needs to only account for preferences of its employees.

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