Data Science: the latest market trends

A quick reminder before starting this article, Data Science or data science is a interdisciplinary field that uses mathematics, development, the business approach and the different knowledge of a field of activity to solve complex data problems.

Its emergence as a field of study and practical application in recent years has allowed the development of various technologies, in particular machine learning (Machine Learning), which we can consider as a stepping stone to what we call Artificial intelligence (AI), a technological field that is rapidly transforming the way we work and live.

The vast amount of data that these technologies collect and store can bring crucial benefits to organizations and societies around the world, but only if we know how to interpret them. In this article, we will review the latest trends in Data Science, which are booming thanks to the strong digital development of the last two years, especially due to COVID-19.

Four trends in Data Science

The importance that Data Science is also gaining in the world of business and commerce means that the science behind this field is becoming more and more accessible. This has resulted in the widespread democratization of data science and that, without a doubt, we will see among the learning trends in the years to come. But within the field in question, there are four main trends:

TinyML with the Small Data

While the development of Machine Learning focuses instead on solutions that require strong technological resources, a new sub-field of machine learning is emerging: the TinyML. Its objective: integrate Machine Learning into systems with very limited resources. This idea has grown in recent years and is motivated by the fact that some problems encountered do not necessarily need a data center or a dedicated platform to solve them. In short, it is a methodology that seeks to improve solutions and push the limits for which more powerful computing is really necessary.

So we are beginning to see it emerge in more and more embedded systems: from wearables and IoT (wearable technology) to household appliances, cars or industrial equipment, making them all smarter and more useful for consumers.

AutoML or machine learning

As we already mentioned in a previous article, a large part of a Data Scientist's time is spent cleaning and preparing data, tasks that are time-consuming and often repetitive. AutoML (Auto Machine Learning) allows the automation of these tasks. Its short-term goal is for anyone with a problem to solve or an idea to test, to be able to apply Machine Learning automatically, to save time and focus on planning solutions. This trend is one of the drivers of the democratization of Data Science that we mentioned at the beginning of the article and that we will see in the years to come.

Data-based customer experience

Data Science is becoming more and more important in the fields of marketing and commerce. Interactions between consumers and businesses are increasingly digitised, resulting in an increased ability of the latter to measure and analyze the behavior of their customers. For all of these reasons, we should see in the coming years how businesses will use user data to provide customers with more personalized, rewarding, and enjoyable experiences.

Hybrid forms of automation

Automation is a constant in articles listing the next digital trends, in particular thanks to the exponential progression of technologies such as Robotic Automation Process (RPA), which offer very interesting results thanks to an in-depth analysis of complex Big Data data. But this automation can be optimized by using hybrid models with human participation, because it offers businesses the ability to process structured and unstructured data and to integrate human abstraction at critical decision points. By identifying key candidates for hybrid automation solutions and using a best practice implementation approach, businesses can multiply the effectiveness of their projects and remain competitive as automation technology evolves.

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