The 5 Biggest Data Science Trends In 2022

THE 5 BIGGEST
DATA SCIENCE TRENDS
IN 2022

© 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
THE 5 BIGGEST
DATA SCIENCE TRENDS IN 2022
Data has become one of today's most important
business assets, and data science enables us to turn this
data into value. In the field, we see fast evolutions and
new advances, especially in artificial intelligence and
machine learning. Here, we look at the five biggest data
science trends for 2022.

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.
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 & 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.

SMALL DATA & TINYML
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
and analyzed for insights into how processes can be smoothed out or made more enjoyable. This has
also led to a drive to create greater levels of personalization in goods and services being offered to us
by businesses. The pandemic sparked a wave of investment and innovation in online retail technology,
for example, as businesses looked to replace the hands-on, tactile experiences of bricks ‘n’ mortar
shopping trips. Finding new methods and strategies for leveraging this customer data into better
customer service and new customer experiences will be a focus for many people working in the field of
data science during 2022.

DEEPFAKES, GENERATIVE AI, & SYNTHETIC DATA
This year many of us were tricked into believing Tom Cruise had started
posting on TikTok when scarily realistic “deepfake” videos went viral. The
technology behind this is known as generative AI, as it aims to generate or
create something – in this case, Tom Cruise regaling us with tales of
meeting Mikhail Gorbachev – that doesn’t exist in reality. Generative AI has
quickly become embedded in the arts, and entertainment industry, where
we have seen Martin Scorsese de-age Robert DeNiro in The Irishman and
(spoiler alert) a young Mark Hamill appear in The Mandalorian.

DEEPFAKES, GENERATIVE AI, & SYNTHETIC DATA
In 2022 I expect we will see it bursting into many other industries and use cases.
For example, it’s considered to have huge potential when it comes to creating
synthetic data for the training of other machine learning algorithms. Synthetic
faces of people who have never existed can be created to train facial recognition
algorithms while avoiding the privacy concerns involved with using real people’s
faces. It can be created to train image recognition systems to spot signs of very
rare and infrequently photographed cancers in medical images. It can also be
used to create language-to-image capabilities, allowing, for example, an
architect to produce concept images of a building simply by describing how it
will look in words.

CONVERGENCE
AI, the internet of things (IoT), cloud computing, and superfast networks like 5G are the cornerstones
of digital transformation, and data is the fuel they all burn to create results. All of these technologies
exist separately, but combined; they enable each other to do much more. Artificial intelligence
enables IoT devices to act smart, interacting with each other with as little need for human interference
as possible – driving a wave of automation and the creation of smart homes and smart factories, all
the way up to smart cities. 5G and other ultra-fast networks don't just allow data to be transmitted at
higher speeds; they will enable new types of data transfer to become commonplace (just as superfast
broadband and 3G made mobile video streaming an everyday reality) and AI algorithms created by
data scientists play a key role in this, from routing traffic to ensure optimal transfer speeds to
automating environmental controls in cloud data centers. In 2022, an increasing amount of exciting
data science work will take place at the intersection of these transformative technologies, ensuring
they augment each other and play nicely together.

AUTOML
Short for "automated machine learning," AutoML is an exciting trend
that's driving the "democratization" of data science mentioned in the
introduction to this piece. Developers of autoML solutions aim to create
tools and platforms that can be used by anyone to create their own ML
apps. In particular, it’s aimed at subject matter experts whose
specialized expertise and insights make them ideally placed to develop
solutions to the most pressing problems in their particular fields but
who often lack the coding knowledge needed to apply AI to those
problems.

AUTOML
Quite often, a large portion of a data scientist's time will be taken up with
data cleansing and preparation – tasks that require data skills and are often
repetitive and mundane. AutoML at its most basic involves automating
those tasks, but it increasingly also means building models and creating
algorithms and neural networks. The aim is that very soon, anyone with a
problem they need to solve, or an idea they want to test, will be able to
apply machine learning through simple, user-friendly interfaces that keep
the inner workings of ML out of sight, leaving them free to concentrate on
their solutions. 2022 is likely to see us take a big step closer to this being an
everyday reality.

For more on data science, AI, and tech trends, sign up for my
newsletter or check out the new edition of my book ‘Data Strategy:
How To Profit From A World Of Big Data, Analytics And Artificial
Intelligence.

© 2021 Bernard Marr , Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker,
futurist, and a strategic business & technology advisor to governments and
companies. He helps organisations improve their business performance, use data
more intelligently, and understand the implications of new technologies such as
artificial intelligence, big data, blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is
a frequent contributor to the World Economic Forum and writes a regular column for
Forbes. Every day Bernard actively engages his 1.5 million social media followers
and shares content that reaches millions of readers.

hello@bernardmarr.com
www.bernardmarr.com

The 5 Biggest Data Science Trends In 2022

More Related Content

What's hot (20)

Similar to The 5 Biggest Data Science Trends In 2022 (20)

More from Bernard Marr (20)

Recently uploaded (20)

The 5 Biggest Data Science Trends In 2022