Predictive maintenance algorithms currently in use are typically based on machine learning capabilities, so they improve over time (i.e., they get better at accurately identifying potential failure situations). Plus, the entire process can be automated. So not only can predictive analytics determine the best time to carry out maintenance on the machine but it can also schedule the best time for that maintenance to occur based on a variety of factors, including current production demand. Predictive maintenance delivers substantial improvements in productivity in addition to enhancing standards of customer service, increasing profits, and more.

The acquisition of good talent is among the greatest challenges faced by HR managers today. As a result, AI hiring technologies are emerging rapidly as a focus area for HR. Most startups are now using AI in addressing the long-standing challenges of candidate identification, engagement, and onboarding to improve the efficiency in the hiring process and reach the goals of ROI. For years, AI has been used by recruiters and agents though it wasn't as sophisticated as it is currently. For instance, it is now fairly popular to search hundreds of CVs with an AI-enabled Applicant Tracking System (ATS) to check for specific phrases and keywords. Therefore, AI is useful to recruiters to reduce the time spent going through applications.

To determine if a problem can be solved using data science, you must be able to phrase it either as a statistical modelling problem, a hypothesis test, a supervised learning problem, or an unsupervised learning problem. A statistical modelling problem is one in which you are trying to figure out the relationship between two variables and if one is important for the other. Hypothesis tests are employed when you want to conduct a comparison between two groups. In essence, you are looking to discover whether the two groups differ, and how they differ. A good example of this is A/B testing.

Why do most AI projects fail? Something which many people do not know, is that up to 90% AI projects lead to failure. And this is not only for AI, this is for IT projects in general. This might sound weird coming from a company specialising in data science and AI. But that's the reality, and this is why the Tesseract Academy was created: to ensure that all organisations can enjoy the benefits of data science and AI,,without the risk of implementation.

In this article, I'm interviewing a veteran data scientist, Dr Stylianos (Stelios) Kampakis, about his career to date and how he helps decision makers across a range of businesses understand how data science can benefit them. While data science is a field showing immense growth at present, it's somewhat nebulous in its description. I think there's a lot of uncertainty as to exactly what it is and how to apply it. Fortunately, Stelios is an expert data scientist with a mission to educate the public about the power of data science and AI. He is a member of the Royal Statistical Society, honorary research fellow at the UCL Centre for Blockchain Technologies and CEO of The Tesseract Academy.