Top data science trends to watch out for in 2023

Reliability, scalability, and insights are the name of the game for 2023, and form the major part of data science trends

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There are decades where nothing happens, and there are weeks when decades happen. We live in a world where AI and data science are shaping and complementing the future of humanity across nearly every industry. In the past few years, AI has evolved from a science-fiction fantasy to a critical part of our lives.


The challenge is not to just survive change but to thrive. Enterprises are willing to go beyond the basics and relook at their data science investments to drive sustainable business value. Boardrooms and newsrooms have devoted significant attention to data science in the last two years.The rapid adoption and focus on data science has led to accelerated change and expansive growth in top areas including AI as a Service, AutoML and TinyML, data regulation, data governance and a continued boom in cloud migration.

The global enterprise focus and expectations have shifted radically in the last few years as data science is increasingly augmenting human potential to reimagine business fundamentals and drive paramount value. We predict the focus in 2023 will be on building trust, scalability, technology proliferation, personalization, and finding the right talent and skills. Explore how these themes will impact and interact with enterprises’ strategic priorities in the coming years.

Theme 1: Building trust and scalability


Reliability, scalability, and insights are the name of the game for 2023. This theme centers around scalability, which, in turn, allows for better decision-making and better results.

  1. Augmented Intelligence: To date, AI and ML have chiefly been used in predicting outcomes and as standalone applications. In the next year, machine learning and natural language processing will both be used to better automate processes and process data while also deriving insights from them as a task that would otherwise be handled by humans, thus increasing workflow efficiencies. Augmented intelligence can transform data analytics with intelligent automation and actionable insights.

2.     Ethical and Explainable Intelligence: As AI/ML becomes omnipresent in every facet of life, from healthcare to governance, the need to white box them also becomes more crucial. Likewise, it will become increasingly important to explain ML outputs and what specific data was used for what purposes. Ethics and fairness in AI/ML will assist in explaining or removing inherent biases to prevent inequitable decisions, making this trend important for 2023 and many years to come.


3.     AI for Sustainability: As the world faces the massive challenges of climate change and reducing carbon footprint, AI can serve as a superhero, helping to build more efficient and sustainable products, optimize energy efficiency, and identify pressing problems. AI supports sustainability across industries, companies, and countries. In 2022, we saw the onset of AI as a driver for sustainability—2023 will take this critical trend to the next level.

Theme 2: Technology proliferation and personalization

Enterprises achieve the goal of hyper-personalization through immersive technologies, enhanced connectivity, and advanced data science models. We will see more experimentation, more consolidation, and more conversational AI.

  1. Quantum ML: Experimentation of quantum computing to build more powerful ML models will grow in 2023. With big players like Microsoft and Amazon enabling quantum computing resources via the cloud, this may soon become a reality.

2.     Consolidation of MLOPs: In 2022, enterprise adoption of MLOPs—which deliver scale, speed, and production diagnostics to improve existing models—took shape in a big way. In the coming year, companies are expected to increase their ML budgets fourfold, with a substantial part of it dedicated to MLOps to support enhanced real-time collaboration between teams. While downstream integrations will continue to be a challenge, additional processes and frameworks will be put in place at the initial stage of development to address this issue.

  1. Conversational AI:  Our society is becoming increasingly dependent on systems that provide instant gratification and contextual recommendations. Therefore, there is an imminent need to make our AI more engaging and personalized. Currently, most systems can handle basic conversations using simple scripts and act as a guided resolution agenda. However, with the adoption of GPT-3 frameworks, we will see a new generation of AI that can handle more complex conversations. It will be possible for AI to understand the user's intent and respond accordingly. Furthermore, they will remember previous interactions and provide a more personalized service. With the advancement of conversational AI, chatbots will become an integral part of our lives.

Theme 3: Finding the right talent and skills

Finding the right talent will continue to be a challenge, so companies must go beyond conventional methods in identifying and securing the best and the brightest.

  1. Talent Crunch: The gap between supply and demand regarding data science talent will continue to widen in 2023. Companies must spend a wealth of time, money, and resources to find the best available data scientists. They should focus on organizing Hackathons, bootcamps, and meetups to target new-age skillsets in AI and data science. Identifying niche skill sets through conventional hiring channels could be challenging. For example, full-stack data science skill sets will now include business domain, machine learning, software engineering, ML engineering, and infrastructure engineering to build end-to-end assets.
  1. Citizen Data Scientists: The one-two punch of the data scientist talent crunch and the increase in no-code/low-code machine learning platforms will strengthen and grow the citizen data scientist community to deliver self-service ML as a business user. Citizen data scientists can bolster organizational value, solve a host of business-specific issues, and deliver meaningful prescriptive analytics.

Scalability, personalization, and talent will make headlines throughout 2023. Fortunately for prognosticators, data science continues to explode and evolve, creating new efficiencies, adoption, and trends that will complement growth and innovation across industries for years to come. Companies and individuals have much to look forward to in 2023 and beyond.

The article has been written by By Zohra Ladha - Senior Director, Data Science, Tredence