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Data analysis at DSM: Reducing staff turnover with Ormit Talent

Data Traineeship & Talent Pool

Arnold works as a Data Analyst at DSM through the traineeship in Ormit Talent’s Data Scientist talent pool. With his expertise in data analytics, he has successfully addressed the high turnover rate within the organisation.

Arnold’s Journey

Data analytics and model development

Arnold embarked on a project to analyse attrition and predict future turnover rates. A model was created to analyse historical data and identify patterns and factors contributing to attrition. However, when the model was ready to go live, it was discovered that the data used for the analysis were not accurate. To address this, Arnold carried out a comprehensive data cleansing process to ensure the reliability and accuracy of the data.

Analyse flow analysis

To understand the root causes of the data contamination, Arnold analysed the entire data chain. Incorrect or untimely data entry was identified as a major problem. The inconsistent use of definitions, especially with regard to revenue calculations in different regions, further complicated the analysis. This led to numerous meetings and discussions to reconcile the discrepancies and interpretations.

Proposed solution

To address the data quality and management challenges, a comprehensive data management proposal was developed. The proposal required all business units to transfer their data to a centralised data lake so that there was a single source of truth. Data transformations were performed using Extract, Transform, Load (ETL) software, which enabled adjustments and quality checks. In addition, a data catalogue was created centrally and data stewards were appointed to maintain the catalogue and ensure data integrity.

Facts & figures about our talent pools

95% of talents join a client after successfully completing a traineeship.

After 5 years, 80% of talents are still employed by the same organisation.

1,453 alumni guided successfully as they launch their careers.

The result


By implementing the proposed solution, DSM realised significant improvements in data quality and management. The standardised data flow and centralised data catalogue reduced inconsistencies and interpretation issues. This streamlined the analytics process and made it possible to accurately predict turnover rates. Moreover, Arnold’s goal of creating a model that minimised human involvement was realised as the data became more reliable and trustworthy. 

Thanks to Arnold’s efforts and the implementation of a robust data management strategy, DSM resolved its high staff attrition problem. By analysing the data flow, identifying challenges and implementing a centralised approach to data management, the organisation achieved improved data quality and accuracy. This case study highlights the importance of data management and the role of data analytics in solving complex business problems.

Other happy clients

Why trainees are a valuable addition to your organisation

You…

  • prevents downtime due to insufficient resources
  • fills vacancies
  • ensures a constant flow of talented people
  • saves costs
  • stimulates a culture of learning and development

Trainees…

  • Want to be challenged
  • Have a fresh perspective
  • Bring about change
  • Make a real difference
  • Are eager
  • Connect different parties with stakeholders and managers

Do you also want to strengthen your team and really push forward?

Help! Gen Z op de werkvloer

Wil je weten waar young professionals behoefte aan hebben? Download onze gratis whitepaper over Gen Z op de werkvloer. Lees alles over…

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