Data science & data engineering

“Want to know how data science can add value for your organization?”
“How many of your data science projects eventually make it to production?”
“What is the status quo of adopting smart algorithms within your organization?”

What is data science & data engineering?

Where the focus was on ‘collecting data’ over the last years, we are now entering a phase where we can start to cash in. By using smart algorithms we find patterns in the collected data, which can be of value to the business right away. But to make a quick analysis of the ever increasing amounts of data, you need a stable and scalable infrastructure. This is where data engineering comes in.

Our mission with data science and data engineering

Cmotions enables organizations to become an ‘analytical competitor’ by means of data-driven (science) value discovery. We integrate data science & data engineering into technological and organizational processes that exist within the organization. The experimental character of data science needs to have its own place within the organization, once it has proven its value.


Cashing in on data

We believe that both technology and customer expectations will grow more over the next decade, than it has grown over the last century. Data will be a catalyst for this.

Because of the focus on collecting data over the last years, the potential applications are without limit. We extract information from data by making use of machine learning algorithms. This is similar to extracting information from images or video’s (image recognition), autonomous car and speech recognition. This way, we can extract value from data to keep up with the increasing customer expectations. Examples are predicting customer behaviour, like churn (cancelling), cross-sell conversion, NBA and algorithms for personalization. All of this serves the same purpose: increasing acquisition and/or upselling or retention. But also predicting visitor numbers for efficiency. Or extracting relevant information from contact with customers, so we can start automating quality control and compliancy checks.

We think it’s vital that these solutions are not one-offs or manual, but that they are both automated and reliable. This way, the model can continue to create value, while you shift focus towards the next project.


What can we do for you?

Our mission is threefold:

  1. Identify how and where data and data science can contribute within your organization to reach targets and KPI’s. This is where data science can truly add value.
  2. Implementing algorithms into technical and organizational processes.
  3. A durable solution. We will make sure that the algorithm keeps working and that it is continually updated. When the best solution does not match your current IT-infrastructure, we offer managed machine learning. A solution where we can continue to use your models. Any algorithm only gets to prove its value, when it is put into practice.


Always up-to-date for our field: data science & data engineering

Naturally, we keep up with the latest developments in our field. With hands-on seminars (homework included) a colleague takes the lead to discuss a predetermined topic. We have already organized multiple seminars like this, with topics ranging from neural networks to machine learning pipelines, as well as bringing these techniques into production.

Whenever we are certain of the added value of certain topics, we also offer this knowledge externally through courses in our Academy (only available in Dutch for now).


Read more about our sector

And do check our other data science intiatives at The Analytics Lab >


How we help

Better user experience on pharma community CPhI with data-based enhancements

naar de case

CarNext uses data science for optimizing Google Ads performance

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