Data Science & Data engineering

Successful marketing in today's digital era hinges on a profound understanding of customer needs and behaviours, coupled with effective actions based on that knowledge. The vast amount of data available in the digital landscape offers an unprecedented opportunity to gain deep insights into various aspects of marketing. This blog post explores the potential of data science and AI in enhancing marketing practices to achieve optimal results.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.