Blog Post

Data Science Trends and Advances in 2022

Today, change is a constant. Businesses are evolving and constantly adapting, embracing cutting-edge technologies to maximize efficiency and return on investment. In today’s world, buzzwords such as data analytics, big data, artificial intelligence, and data science can be intoxicating. Businesses aspire to use data-driven models to simplify their operations and make informed decisions.

In the wake of COVID, stakeholders have had to adjust to change faster, as the pandemic disrupted industries around the world. And as a consequence of this, investments in data analytics and data science are on the increase. Almost every organisation is now taking a closer look at its data. This ‘awakening’ has led many organisations to learn how to implement and make sense of the data they aggregate, providing online classes and on-the-job training for their in-house employees. These efforts have resulted in the much-debated “democratisation” of data science, which will influence business ‘high tides’ in 2022.

Let’s look at what we think the top data science trends and advances will be in 2022, to help you understand how big data and data analytics are becoming an integral element of many business operations, regardless of industry.

Data Science Trends and Advances in 2022 1

The rise of Small Data and Tiny ML

Big Data is a term that has been circulating in industries the last few years, referring to the increase in digital data we are all producing, collecting, and analysing. It’s not only the data that’s big, but the machine learning algorithms we employ to handle it are quite ‘heavy’ too. The conversation around ‘what’s next? has already begun – since issues around skills, budgets, and resources for managing this big data are also at their peak.

If you’re dealing with cloud-based systems with infinite bandwidth, this won’t matter to you, but it doesn’t cover all the scenarios. And so “small data” has surfaced as a paradigm for facilitating quick, cognitive processing of the most critical data in circumstances when time, bandwidth, or energy expenditure are critical. It has a lot to do with the notion of edge computing. For example, imagine a device that can monitor crops and highlight an issue when it detects features such as soil moisture, specific gases (for example, apples emit ethane when ripe), or harsh atmospheric conditions (e.g. high winds, low temperature, or high humidity). It could facilitate massive boosts to crop growth and overall yield.

To utilise this kind of data, TinyML can be integrated, meaning machine learning algorithms that take up the least amount of space feasible to execute on low-power, on-field hardware. From wearables to household appliances, automotive and industrial equipment, TinyML will emerge in a growing number of embedded systems, making them smarter, more useful and more reliable.


Human-Centered Data Science 


tiny ML

As we dive deeper into technological dependence, business success is reciprocally dependent on human emotions and experiences.

Today it is all about how businesses can utilise the customer data they collect in order to provide valuable and enjoyable experiences. Ranging from an improved customer journey in e-commerce, a user-friendly interface, and navigation through complex software to executing an efficient and goal-oriented customer service.

For the benefit of businesses, our interactions are increasingly occurring in the digital space, meaning that often every aspect of our engagement can be measured and analysed for insights into how processes can be improved. This on its own has led to greater personalisation of the goods and services being offered to us by the businesses we deal with. A global trend of diversification through personalisation is on the march, and it is here to stay as we enter 2022.


AutoML and the democratisation of data science


democratisation of data science

Meaning “automated machine learning”, AutoML is an intriguing development that is pushing the “democratisation” of data science. A considerable percentage of a data scientist’s work is frequently spent on data cleansing and preparation – repetitive, monotonous tasks that demand data skills. At its most basic, AutoML covers the automation of these processes but is also increasingly about developing models, algorithms, and neural networks. The goal is that anybody with a problem to solve or an idea to test will be able to apply machine learning using simple, user-friendly interfaces that hide its inner workings, allowing users to focus on their solution.

Further proof that the ‘democratisation’ process is real is the growing popularity of Python, which is now ranked as the 3rd most popular programming language. How did we come to this conclusion? Python has a huge number of free data science libraries such as Pandas and machine learning libraries like Scikit-learn, which make data science more accessible and provide a friendly learning curve for beginners in the field.

Digital collaboration

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AI, the internet of things (IoT), cloud computing and ultrafast networks like 5G are the foundations of digital transformation, and they all share one thing – data. Combining these technologies unleashes transformational potential. Artificial intelligence enables IoT devices to function intelligently, communicating with one another, ushering in a wave of automation. 5G and other ultra-fast networks will enable new types of data transfer with AI algorithms playing a key role – from routing traffic to ensure optimal transfer speeds, to automating environmental controls in cloud data centres. In 2022, an increasing amount of intriguing data science work will take place where these transformational technologies meet, ensuring they complement each other.


Generative AI and synthetic data


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We all remember the deepfake Tiktok videos featuring ‘Tom Cruise’. They were created using generative AI to derive new content, using existing data. This trend is spreading to other industries to help train ML algorithms in the use of synthetic data, which will be immensely beneficial to society. As an example, generative AI can train image recognition systems to spot signs of very rare and infrequently photographed diseases in medical images.

Fortunately, regarding the emerging wave of privacy concerns, synthetic data plays a huge role in minimising collateral. Because it is artificially manufactured data, it tackles the issues around using images of real people or events, to train algorithms.

On the other hand, these advancements open a plethora of opportunities for phishers, hackers, scammers, and extortionists, as we head into 2022. Business cybersecurity still lags on comprehending the limitless possibilities of deepfakes and generative AI. After all, digital transformation is a progress that can be overwhelmingly positive and negatively disruptive at the same time.


Data science, like any other science, is constantly evolving. The sector is about to undergo an enormous transformation in everything from data governance to deepfake technologies. Keeping an eye on these trends can help you stay one step ahead in today’s competitive environment.