2022 Data Science Trends

2022 Data Science Trends

Shanghai light dispaly tunnel. Long exposure. Tripod used.

Part 1

Yet another year draws to a close. As the world still grapples with the aftershocks of a pandemic, technology, and innovation gradually begin to gain momentum yet again. This global crisis has proven truly and well that technology can turn into a life-saver when life comes to a standstill. In the evolving landscape of a “new normal”, the emergence of data science as a field of study and application is opening up new avenues of research as well as employment.

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Barely a decade ago, it was considered a niche expertise straddling statistics, mathematics, and computing – an area in which just a handful of research scientists were interested. Today, its pivotal role in business and commerce is well acknowledged.

In keeping with the rapidly changing scenario, successful data professionals today understand the importance of moving beyond the traditional skills of analysing large data, data volumes, and programming. Data science has enabled machine learning (ML) which, in turn, has contributed to the development of artificial intelligence (AI) – a domain that is rapidly transforming everything around us. This is the right time for us to look ahead into the emerging trends for the coming year.

Moving towards Small Data

We have heard a lot about Big Data, and how it can transform analytics. It involves the humongous volume of digital data that we generate, collect, and analyse – and in keeping with the data volume, the ML algorithms used to process it keep on getting more-and-more complex. While that is producing astounding analytics, there are use cases where processing a large amount of data over unlimited network bandwidth just might not help. A real-life application like a self-driving car will need a more analytical response based on a limited amount of data concerning its immediate surroundings – and it will need it real quick. Big Data crunching on a centralized Cloud server when trying to avoid a traffic collision will not work. This is where “small data” – a close relation to Edge computing – helps by facilitating fast, cognitive analysis of the most vital data when response time is crucial.  And this requires TinyML – an ML algorithm designed to occupy minimum space and run on low-powered hardware, close to where the action is. This is going to be one big focus area for embedded systems – gadgets, appliances, automobiles, or machinery.

Synthetic training data through generative AI

Generative AI is leveraged to generate or create new data based on prior training through sample data. This is being widely used in creative outputs like static images, videos, audio, artworks – and specialised technologies like de-aging used in movies. However, the new use of generative AI would be to create synthetic data. This is simulated artificial data that can be used to other machine learning algorithms – like image recognition systems, for example. Synthetic faces of people who do not exist in reality would be used to train facial recognition algorithms. The advantage is that it will entirely do away with privacy concerns involving the use of real-life data. Other major areas of use could be for diagnostic AI through generated medical images, architectural concept designing AI, and emergency security systems.

Augmented Customer Interfaces to gain more attention

Our business interactions are becoming increasingly digital – from AI chatbots to cashier-less payment or human-less delivery. Future customer servicing might involve no human staff at all. And that means every aspect of our digital engagement data are being collected, measured and analysed for insights into how processes can be smoothed out or made more enjoyable. As companies like Facebook, Microsoft and Amazon race to create a metaverse for the workplace or retail space, even what we think of Zoom meetings or E-commerce interfaces of today will be replaced by new augmented customer interfaces. These will take various forms, from AR on mobile to new methods of potential communication such as a Brain-Computer Interface (BCI), and will be a focus for data science professionals over the next couple of years.

Augmented Data Management will be pressed into service

To leverage the most out of data generated through augmented customer interfaces, Augmented Data Management will be much in demand. Gartner also identifies integrated human-data interaction to be a pervasive trend for a future AI-human hybrid workforce. Augmented data management using ML and AI to optimize and improve operationswill enable active metadata to simplify and consolidate architectures and also increase automation in redundant data management tasks. This will eventually reduce task loads and facilitate AI-human architectures.

Emergence of AI-As-a-Service platforms

As data science enters adulthood and machine learning gains traction, we shall witness more B-2-B and AI-as-Service platforms and services emerging. As a trend, this is going to democratise AI capabilities, allowing small entrepreneurs with limited resources to reap the benefits of AI-powered business tools. We already have platforms like Shopify, Square, Lightspeed and other similar pay-and-use services targeted at small businesses. Even technology giants are eagerly eyeing this market. Google, Microsoft, Amazon, and Baidu are already well-capable to produce AI-As-a-Service at scale for their customers. Indeed, experts think this is going to be one of the biggest growth curves in data science in the immediate future.

Convergence – Things come together!

Currently, we have individually brilliant frontier technologies that push forward the boundaries of data science. We have augmented and virtual reality (AR/VR) AI, the Internet of Things (IoT) and Cloud computing. We also have ever-expanding connectivity capabilities as 5G and other ultra-fast networks not only provide higher data transmission speeds but are capable of transferring entirely new types of data.  And that is where innovators are currently working on. With so many powerful technologies existing separately, what if they could be combined to create a symbiotic metaverse where each technology enables the other to go beyond? Much of the future progresses in data science will focus on such a convergence at the intersection of these transformative technologies.

Look out for more data science trends in our upcoming episode. 

(To be continued)

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