Friday, September 22, 2023

Data Science Trends and Predictions 2023

By Nikhil Sonawane - November 01, 2022 3 Mins Read


Data science is one of the most crucial skill sets to have in today’s digital-first landscape. Industry veterans consider this multidisciplinary data management approach to identify, gather and evaluate patterns through efficient data analytics tech stack and domain expertise, as one of the most effective approaches to processing modern data.

CDOs are exploring opportunities to improve their data mining approaches, predictions, Machine Learning (ML), and other data analytics operations to meet the evolving needs of the enterprise. In fact, many leaders consider data science as a revolutionary approach to transform every aspect of global businesses.

Following are some data science trends and predictions that all CDOs and DataOps teams should look out for in 2023:

Augmented Analytics

One of the most significant trends observed in data science today is augmented analytics, and there is a surge in the adoption of this approach in many enterprises. Enterprises can embrace this trend to leverage Artificial Intelligence and ML to revolutionize how data is gathered, managed, and processed. Data analytics tools that have augmented analytics integrated are getting popular in the market as they enable the DataOps teams to automate their tasks and get actionable insights by implementing an algorithm that offers conversational analytics. Augmented analytics is an effective approach to scale data science platforms and integrate advanced analytics tools into their data tech stack.

Also Read: Ways Data Engineers Can Reimagine Data Management Process with Modern Data Tech Stack

Data-as-a-Service (DaaS)

DaaS is another data science trend to look out for in 2023 because it enables the DataOps teams to streamline and strengthen their data management processes.

A report published by Statista predicts that the global market size will reach a revenue of approximately USD 10.7 billion by 2027. The market size of this Data-as-a-Service is growing because modern enterprises need to access the data streams in real-time and ensure a seamless collaboration throughout the organization. Organizations that have limited access to data science resources and tech stack can subscribe to a DaaS service to streamline DataOps.


A report by Gartner predicts that the business value added by Blockchain will reach approximately more than USD 176 billion and exceed USD 3.1 trillion by 2030. As Blockchain is a shared, secured, and un-editable database or ledger, CDOs are exploring opportunities to enhance their data management approaches to get valuable, actionable insights using Blockchain tools for analytics.

Machine-Learning-as-a-Service (MLaaS)

MLaaS is another trend that data scientists can leverage to outsource their ML processes to a third-party vendor. It is one of the most efficient ways to implement ML for organizations that do have the finances, budget, and technical expertise. The Machine-Learning-as-a-Service model offers enterprises low-cost services enabling the majority of them to get more accurate insights from the data gathered. Hence MLaaS is one of the emerging trends of data science that can be witnessed in 2023. Depending on the needs of the organization, CDOs can select from a wide range of services that they can subscribe to.

Also Read: Smart Contracts in Blockchain

Big data analytics automation

Automation is one of the driving factors that transform the way data is governed in the world today. Industry veterans are exploring possibilities to automate Big Data analytics to gather and evaluate unstructured data throughout their organizations. Moreover, they are also exploring opportunities to scale Analytics Process Automation (APA) by providing actionable insights and predictive capabilities. Enterprises that leverage Big data analytics automation enables them to deliver better results and reduce operating costs.

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Nikhil Sonawane

Nikhil Sonawane is a Tech Journalist with OnDot Media. He has 4+ years of technical expertise in drafting content strategies for Blockchain, Supply Chain Management, Artificial Intelligence, and IoT. His Commitment to ongoing learning and improvement helps him to deliver thought-provoking insights and analysis on complex technologies and tools that are revolutionizing modern enterprises. He brings his eye for editorial detail and keen sense of language skills to every article he writes. If he is not working, he will be found on treks, walking in forests, or swimming in the ocean.

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