The 5 Biggest Data Science Trends In 2022 – Forbes

The emergence of data science as a field of study and practical application over the last century has led to the development of technologies such as deep learning, natural language processing, and computer vision. Broadly speaking, it has enabled the emergence of machine learning (ML) as a way of working towards what we refer to as artificial intelligence (AI), a field of technology that’s rapidly transforming the way we work and live.

The 5 Biggest Data Science Trends In 2022


Data science encompasses the theoretical and practical application of ideas, including Big Data, predictive analytics, and artificial intelligence. If data is the oil of the information age and ML is the engine, then data science is the digital domain’s equivalent of the laws of physics that cause combustion to occur and pistons to move.

A key point to remember is that as the importance of understanding how to work with data grows, the science behind it is becoming more accessible. Ten years ago, it was considered a niche crossover subject straddling statistics, mathematics and computing, taught at a handful of universities. Today, its importance to the world of business and commerce is well established, and there are many routes, including online courses and on-the-job training, that can equip us to apply these principles. This has led to the much-discussed “democratization” of data science, which we will undoubtedly see impact many of the trends mentioned below, in 2022 and beyond.  

Small Data and TinyML

The rapid growth in the amount of digital data that we are generating, collecting, and analyzing is often referred to as Big Data. It isn’t just the data that’s big, though – the ML algorithms we use to process it can be quite big, too. GPT-3, the largest and most complicated system capable of modeling human language, is made up of around 175 billion parameters.

This is fine if you’re working on cloud-based systems with unlimited bandwidth, but that doesn’t by any means cover all of the use cases where ML is capable of adding value. This is why the concept of “small data” has emerged as a paradigm to facilitate fast, cognitive analysis of the most vital data in situations where time, bandwidth, or energy expenditure are of the essence. It’s closely linked to the concept of edge computing. Self-driving cars, for example, cannot rely on being able to send and receive data from a centralized cloud server when trying to avoid a traffic collision in an emergency situation. TinyML refers to machine learning algorithms designed to take up as little space as possible so they can run on low-powered hardware, close to where the action is. In 2022 we will see it appearing in an increasing number of embedded systems – everything from wearables to home appliances, cars, industrial equipment, and agricultural machinery, making them all smarter and more useful.

Data-driven Customer Experience

This is about how businesses take our data and use it to provide us with increasingly worthwhile, valuable, or enjoyable experiences. This could mean cutting down friction and hassle in e-commerce, more user-friendly interfaces and front-ends in the software we use, or spending less time on hold and being transferred between different departments when we make a customer service contact.

Our interactions with businesses are becoming increasingly digital – from AI chatbots to Amazon’s cashier-less convenience stores – meaning that often every aspect of our engagement can be measured …….


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