watermark picture

Data Science & Data engineering

While traditional BI is suitable for describing the past, data science is all about predicting the future and prescribing corresponding actions. It is the art of turning your data into advantages. This all is made possible by the domain of data engineering. Data engineers will support data analysts and - scientists by building systems that are capable of extracting and storing large amounts of data from a multitude of sources.

Data scientist & Data engineer, only hypes?

Or the team that you need?

Fancy tools and technologies alone won’t give your business a competitive advantage. The essential element to create advantages from your data are people. People that are not only technically skilled to implement these algorithms and systems, but also understand the real challenges and needs of your business. This will be the only way to turn those challenges into opportunities and generate real impact.

As there are endless possibilities to implement data science and data engineering into your business, it is impossible to list them all. However, below we will give you some real life examples:

Shift from data to impact today

What's your data challenge?

Turn your data into an asset

Your data is an opportunity to get a competitive edge, to mitigate risks and to grow your business in a sustainable way by making the right decisions. But too often data remains unavailable, unknown or not combined and opportunities are missed. Are you seeing the untapped potential? Are you looking for the next step to take?

What’s your data challenge?

Don’t forget to also start treating your data as an asset! Check out our view on Data Governance

datashift ceo nico huybrechts

Looking for an experienced Data Science or Engineering partner?

Curious how a Modern Data Platform can accelerate your growth in data impact?

To truly unlock your data, it takes a clear strategy, a solid technological foundation and the right people. And that's what we provide for our clients.

Where are you today when it comes to your data? Reach out and discuss your challenge.
We're here to help.

At an incredible pace, Datashift geared us up with the required architecture, knowledge and tools to be able to steer our business with insights. They easily switched in priorities when the business needed different focus and clearly understood the importance of a hands-on approach at the start, evolving into a strategic and future proof data roadmap later on. The dynamic and experienced 'Datashifters' easily blended into our team and were vital for our desired commercial success.

Bart Van Den Langenbergh,
Head of Marketing & Sales, Streamz

quote icon

"Data science is not about fancy algorithms. It is about data-driven problem solving in order to create business value."

quote author
Eline Vanwalleghem
Data Science Consultant

Get inspired by these Data Science and Engineering use cases

Read our paper on the Modern Data Platform here

Data Science and Engineering articles you might be interested in

What an event-driven architecture brings to the table to solve your data ingestion challenges

Before you can generate insights from your data, you need to move those data from an operational to an analytical environment - a process commonly referred to as data ingestion. An event-driven architecture provides an elegant way to achieve a process marked by timeliness, performance, and cost-effectiveness.

Read More

How a data hub helps you step up customer engagement

A modern data platform makes integrating new services and empowering new use cases easier than ever. A data hub that provides your salespeople and service engineers with actionable customer data straight from your data platform is an excellent example of what you can achieve with modest effort.

Read More

Who knows who: from Hollywood to Flanders

In this sequel on graph analysis we try to find connections between different actors and movies they played in. Some of the links are not obvious at first glance, e.g., what is the connection between Zendaya and Audrey Hepburn? Will Smith and Chris Rock? Xander De Rycke and Steven Spielberg? Andy Peelman and Steven Seagal? The method to find out those links is the Dijskstra shortest path algorithm.

Read More

Movie star popularity based on graph analysis

The internet exists out of an endless pool of raw data, not all data is worth the effort to analyse. But our colleague Jens saw that the Internet movie database (IMDb) was a diamond in the rough waiting to be mined. So, he did what every curious Data Scientist would do, he rolled up his sleeves and got to work. He used graph analysis to investigate the popularity of actors, actresses, and directors.

Read More

Why a lightweight cloud analytics lab goes hand in hand with your BI environment

As your traditional BI environment bumps up against its limits, a lightweight cloud analytics lab can serve as a complement and as a first step in building a future-proof modern data platform. Check out three reasons to level up your current data architecture.

Read More

AI is on the rise. So are AI safety and ethical AI.

As AI has a growing impact on our society, new initiatives and organizations focusing on AI safety and ethical AI continue to spring up.

Read More

Four big data blogs you don’t want to miss

Are you overwhelmed by the amount of information about big data on the Internet? Then, check out our four favorite big data blogs and some of their top articles.

Read More

How to build a churn prediction model that actually works

The truth is, predicting churn is easy. The hardest part is making it actionable. With this approach you’ll retain only your valuable customers that are about to churn, with a personalized retention action at the right time.

Read More

The 5 ingredients of a successful data strategy

Many organizations struggle with ongoing data issues and are missing the opportunity to use data as a means to profoundly impact their business. What's usually missing is a comprehensive data strategy. Let's have a look at the 5 ingredients a successful long-term data strategy can't do without.

Read More

AI for everyone? It’s no alchemy run by wizards!

We should use AI to make our organization smarter! Chances are you’ve recently heard this statement and see organizations acting to it. Leading companies are using AI across departments to increase productivity. Customer care organizations are using chatbots and speech recognition in their customer contacts. Marketing departments are predicting churn and segmenting their customer base.

Venturing into the field of AI may seem daunting at first. While AI has been hyped immensely in the last decade, it is a deeply technical field. Gartner even made a prediction that 80% of AI will remain alchemy run by wizards whose talents won’t scale in the organization. Your organization probably doesn’t have the scale and technological know-how of Google, Apple or Facebook. This might lead you to conclude that developing AI models isn’t for you. You are right, partially.

Read More