Microsoft Power BI Desktop (the “BI” stands for “Business Intelligence”) connects the world of spreadsheets and programming – it has an interface not too distant from Excel, but also enables you to design complex data loading processes and enhanced built-in transformation features with snippets of real code.
Last year, online sales on Black Friday hit $9.03 billion For context, just 3 years earlier, the revenue generated from eCommerce reached only $1.3 billion. As a result of the rising digitalization, the need to transform warehouses into smart logistic centres is growing accordingly. And funnily enough, although some warehouses have already embraced the miracles of modern technology, many others still use… paper counts.
Did you know that in 2021, numerous manufacturing machines are still operated by Windows 95’s ancient precursor? Since MS-DOS premiered in 1981 (Ronald Reagan just started serving his first term), you may naturally think – how is that even possible? The short answer is – legacy systems are not easy to replace. And interestingly, it’s rarely due to the cost factor.
53 years ago, a computer lip-read the conversation between two astronauts and, not long after, decided to kill them. If you’re a cinema fan, you probably recognize where this story is from. However, today this futuristic vision isn’t Hollywood anymore – with Natural Language Processing, or NLP for short, it’s becoming reality (luckily, without the killing part).
Launching something new – especially when it’s innovative – naturally bears a risk of failure. Although the reasons for an offering’s lack of success are often complex, there are overarching themes that link the failure of new products. Luckily, there's a way to avoid all of these traps. In this article, we'll show you why prototyping is so important and why you should never start developing a big software solution without the right preparations.
The beginning of the last decade saw the rise of Data Science. Multiple companies – including some leading brands – started employing Data Scientists. Businesses spent millions on developing this field at their organizations… and often contributed towards the biggest ML Ops fails in history. Luckily, the current data science landscape is completely different. In this blog entry, we're going to guide you through a fairly easy implementation of tools that will empower your Data Scientists to deliver value effectively.