A few years ago, I combined my day job as a data engineer, helping customers engineer data analytics, with my love for  football and set out to beat the bookies. It’s an exciting game to play, and I have learned a lot about the merits of different predictive models that

Moving IT to the cloud is one of the main objectives  for many companies. But once achieved, they find managing data in a hybrid environment a challenge. This is when metadata management becomes more important than ever. Cloud vs. on-premises? It’s one of those topics that you hear broached at

In 2018, the GDPR changed how tech companies handle data privacy. In 2019, it’s influencing the public’s perception of internet privacy and changing how tech companies treat violations—and one another. Last month, I wrote about the state of internet privacy in the context of the GDPR and other regulations that

Here is why Data Scientists need to think like a behavioural economist or psychologist when they communicate or story tell their insights. This helps companies to take concrete and bias-free decisions to acquire customers, retain employees and deal with managers within the organization. Picture this scenario: There are two investment

Throughout my ongoing meetings and conferences about the GDPR and how it relates to cutting-edge technologies, I encounter many ‘add-on’ privacy solutions such as ‘secure your Office 365’, ‘breach detection’ and ‘fix your network.’ Such options, which aim to patch privacy vulnerabilities as they arise, also have the secondary effect

Vehicle-to-Cloud – yes, it is a thing!  And it is making automotive insurance providers and telecoms thrive together to support telematics “Self-driving cars are the natural extension of active safety and obviously something we should do,” this is a popular quote by Elon Musk. But wait, even more popularly discussed

Artificial Intelligence is dominating innovation in companies across the globe- doesn’t matter if they are giant conglomerates or young startups. According to a report by Research and Markets titled Artificial Intelligence Market by Technology, and Industry Vertical – Global Opportunity Analysis and Industry Forecast, 2018-2025, the global Artificial Intelligence market

For some time, observability in IT operations has been associated with three data types that monitoring systems must ingest in order to be at least somewhat effective: logs, metrics, and traces. This limit to the type of data consumed is far from efficient when it comes to the true needs

“Mix and match” might work for products on sale at the supermarket, but it’s not necessarily the best strategy for an IT department. In this case, the “mixing” involves adopting cloud solutions along with in-house IT systems, with the IT team managing the relationship between the different systems. The issue

How do you merge the perspectives of the data scientists, entrepreneurs, investors, student and CTO’s under one roof when it comes to data-driven decisions? How do you bring together the likes of Google and IBM with academics and universities to find answers to the most relevant questions in the world