Data Science Principles

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What we believe generates business effect is immediately reflected in the strategy component. We want to concentrate on the correct problems, the ones that have a commercial impact and provide value to our clients, because producing data products requires a significant commitment.

  • The importance of customer and company value outweighs fancy solutions.
  • One of our objectives is to do research.


The next question is how we will work and develop on these important and relevant topics now that we have them. To put it another way, what is our process? Our guiding principle is to offer value as rapidly as possible and to establish next steps using data and analytics.

  • Our project plans are dynamically informed by data analytics.
  • In our planning, we actively manage the unknowns.


The model is and will always be at the heart of our work. Of course, a model should perform as intended, but there’s more to it. A superb model should be expandable, robust, and, if possible, explainable, similar to the distinction between code and clean code.

  • Small iterations on existing models are valued by us.
  • Explainability is more important to us than absolute correctness.
  • The effectiveness of the service has been demonstrated on the internet.


Monitoring, testing, and quality assurance We are concerned about maintainability and quality assurance as a mature organisation. We are the ones that maintain and operate our models, similar to the engineering principle of “you make it, you run it.” As a result, we’d like to do ourselves a favour by making them as strong as possible and automating as much as we can.

  • We adhere to the ideals of clean coding.
  • We create reliable, dependable deployment processes.


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The Data Products skill combines data science, software development, and data engineering. But, for data scientists, what components of the engineering skill are important? We focus on best practises for designing scalable data pipelines because we spend a lot of our development time with technologies like Spark and Airflow.

  • We work with the engineering community and make use of open-source software.
  • Data workflows are efficient and cost-effective because we value reproducibility and modularity.


We don’t expect our product, engineering, or business partners to have a mental image of what a strong Data Products organisation performs because data science, and especially data products, is still new to many people. That is why we want to engage with them in a really proactive manner. We also rely on them for data — think of the hierarchy of demands – so collaboration is essential.

  • We encourage people to think of data as a product.
  • We work closely with data stakeholders.