Berno van Dijk

Data Analyst
There are still many processes that Data Science could automate at HTG. There’s plenty for us to do!
  1. Berno, you joined HTG as an intern and now you are a permanent member of our team! How did that come about?

    That’s right, I actually completed my graduation internship at HTG. During that time, my mentor regularly hinted that I could stay on. I had to retake some classes so I continued to work as a temp, and later I was offered the chance to work directly for HTG.

  2. You were given an entirely new role within the company; what does your work entail?

    Good question. There are increasing questions from Procurement and Sales about how to gain insight into market developments. Lots of data are being generated, but how can we put them to good use? My role is to analyze these data. My job starts as soon as there are questions from within the company. This involves several projects, sometimes running concurrently, that allow me to work closely together with other departments. One question could be to make a prediction about future sales. I compose a mathematical model using historical sales, which calculates a prediction. Then I check whether this prediction is correct. Another question may relate to the optimal route through our warehouse. I’ll search through the data to find out which products are often sold together and continue from there.

  3. What is the importance of Data Science within HTG?

    Over time, a mammoth volume of data is gathered that contains very valuable information, like trends or patterns. Sometimes, people just enter anything because it’s required, but we can’t work with information like that. It is important that companies start to realize the value of data, because the use of algorithms allows for more efficiency: proactive instead of reactive working. There are still many processes that Data Science could automate at HTG. There’s plenty for us to do!

  4. What is the biggest challenge your department faces?

    To make sure what you generate is validated. You create a mathematical model, you fill it with data, and a prediction appears. But is it correct? We draw up a purchase recommendation: what quantity should be bought over the period ahead. Does this align with reality in retrospect? That’s the biggest challenge. As well as our data, there is also the knowledge and experience from the departments that have an impact on the recommendation.

  5. Can you tell us the must-haves for being part of the Data Science team?

    Commercial skills are certainly important, so you can communicate clearly with your client. It’s not enough to merely understand data, you also have to be able to translate it into layman’s terms. Of course you need a mathematical mind, but languages too. I am not referring to German or Dutch, but Data Science languages (R, Python, SQL). Not the typical languages people usually refer to, but good knowledge of these is essential.