“Data is the new oil,” a British mathematician stated in 2006. However, with the latest trends emerging nowadays, data seems to be more valuable than oil. Data science and AI are evolving more rapidly than any other domain. According to Fortune Business Insights, the global data insight industry was valued at USD 133.2 billion in 2024 and USD 776.86 billion by 2032, exhibiting a CAGR of 24.7%.
We will discuss the latest data science and AI trends in the industry, which you should be aware of. Here is the list of topics we will cover.
Data Science Technology Growth
Data science is the field of study wherein we analyse data generated from different sources to infer conclusions so that businesses can make better data-oriented decisions. It is a broader domain that includes subsets such as predictive analytics, AI-powered insights, real-time data processing, big data analytics, and edge computing.
Data science is a rapidly emerging technology used in almost all fields, from healthcare, finance, marketing, and manufacturing to the entertainment industry. LinkedIn states that there are 6,50,000 active job openings for data scientists, 1,43,000 for AI professionals, and 5,61,000 for data analytics professionals worldwide. Data science professionals are paid between INR 7LPA and INR 28LPA, with an average of INR 15.5LPA. The demand for data scientists is exponentially increasing with time.
Considering all these facts and figures, one wonders: What is the potential of data science in the coming decades? Data science, along with other cutting-edge technologies such as quantum computing and cloud computing, has revolutionised the way data is analysed and processed.
Top 10 Data Science Trends
Here are the top 10 data science trends that you should be aware of in 2025:
Generative AI
Generative AI is a technology that is making headlines. With advancement in the industry, we are close to a future where we will be talking to robots and they will be responding by understanding the context of the question. Currently, we have Gen AIs such as ChatGPT, DALL-E, Gemini, etc., that do the same. Next the industry is constantly working on conversational AI, hyper-personalization and intelligent automation.
Machine Learning
Machine Learning continues to be the cornerstone of innovation in data science. Technologies like AutoML, which eases the development and deployment of Machine learning models, fraud detection, predictive modelling, and real-time ML models for real-time decision-making, are a few trends in the domain. Also, developments have increased the focus on making ML models transparent and easily accessible.
Edge Computing
Edge computing is a new technology that enables computations near the data source, reducing latency and providing real-time analytics. This minimises the need to send the data over the cloud, hence keeping the user’s data privacy intact and optimising resource usage, which leads to less energy consumption.
Augmented Analytics
Augmented analytics is a data analysis method that involves artificial intelligence and machine learning. It enables humans to interact with data at a contextual level, allowing businesses to interact with data without relying on data professionals. It bridges the gap between complex data analysis and business users, democratising analytics for all.
Ethical AI
With the rise of Artificial Intelligence algorithms, it is vital to use them wisely. Over the past few years, some concerns have been raised about the responsible development and deployment of AI and ML technologies. Like the popular proverb from the Marvel film, “With great power comes great responsibility.” Upholding data governance, ethics, and privacy is more important than ever.
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Natural Language Processing
Natural Language Processing (NLP) is the base of GenAI. It is the way in which a machine understands, processes, and communicates with human language. NLP is widely used in creating transformers and large language models (LLMs) that can understand and respond to texts, images, and videos. Nowadays, multimodal NLP and multilingual NLP are taking the headlines to create advanced and enhanced virtual assistants and chatbots.
Quantum Computing
Quantum computing is a new technology currently being integrated with data science. This will accelerate data processing and increase the efficiency of the model. Encryptions based on quantum computing are said to be one of the best encryptions, ensuring data security.
Hyper Automation
Hyperautomation uses AI and Machine learning algorithms to automate, streamline and optimise complex workflows. This enables intelligent automation, reduces human intervention, and increases operational efficiency.
Internet of Things
IoT, or the Internet of Things, is widely used in industries such as healthcare, pharmaceuticals, and manufacturing. These IoTs act as real-time monitoring devices that reduce latency and improve real-time decision-making. Nowadays, IoTs are used in agriculture to measure moisture content in the soil, and appropriate actions can be taken to achieve better results.
Big Data Analytics
Big Data Analytics is one of the major data science technologies. Imagine you have a PC that has 10TB of storage. The daily data generated by people is 403 million TBs. Two questions arise. Where can huge data be stored, and how can it be processed? Big Data Analytics is the answer to all of it. It uses new-age concepts such as data lakes, data mesh, and delta lakes to store the data efficiently and distribute computing to process the data efficiently.
Future Trends of Data Science
TinyML
TinyML brings machine learning to small, low-power devices where resource constraints exist. It makes machine learning available and accessible to a broad range of devices across industries. It uses low energy, which aligns with green computing initiatives.
AutoML
AutoML allows users to build ML models without the intervention of data professionals. It can be termed a low-code/no-code platform that bridges the gap between business professionals and data. Data collection is required in AutoML. AutoML automatically performs all the necessary calculations and processing and provides the output. No preprocessing, training, or evaluation of the model is involved.
Edge Intelligence
Edge intelligence is designed by combining edge computing and artificial intelligence, hence the name. In Edge computing, a layer is designed that is closer to the data source, where all the computations take place. Applying artificial intelligence (AI) to the edge layer is known as edge intelligence. Applications of edge intelligence can be widely seen in sensors, manufacturing, controllers and connected vehicles.
Responsible AI
Since the introduction of ChatGPT in 2022, AI has become an essential part of decision-making. Responsible AI focuses on building transparent and unbiased AI solutions that can be used for the betterment of society.
Cloud Migration
Cloud migration has redefined the way data is stored and analysed. It is the process of migrating an on-premise infrastructure to a cloud platform such as AWS, Azure, or GCP. The primary reasons for this are scalability, flexibility, and cost. Most companies are migrating to the cloud, which is why it is such a trend.
Check out our video on Emerging Trends in Data Science
Here is a list of emerging technologies and tools in data science that you should be aware of to be in the data science domain:
- AI-Powered BI Platforms: ThoughtSpot, Tableau AI, Power BI with AI integrations.
- AutoML Platforms: Google AutoML, Auto-SKLearn, Amazon Lex, Azure AutoML, H2O.ai
- MLOps Frameworks: MLflow, Kubeflow, Azure Machine Learning and Amazon SageMaker
- Big Data and Distributed Computing: Apache Spark and Hadoop
- Data Integration and ETL Tools: Apache Airflow, Fivetran, Azure Data Factory, and AWS Glue.
- Cloud-Native Data Platforms: Snowflake and Databricks
Challenges in Adopting Data Science Trends
There are many challenges in adopting these data science trends. The domain is still evolving, and here are a few challenges listed:
- Data quality and availability: The poor quality of data deeply affects data-driven decisions. The better the data, the more accurate the decisions will be. Also, access to sensitive data further complicates the process.
- Rapid technology evolution: Technology is ever-evolving. A few years back, we were introduced to ChatGPT, which can generate answers based on context. Nowadays, AI can create images and videos, understand context, replace pictures, develop voice, and more. There is a lot to catch up on at a time, so fixating on a single technology or framework seems complicated.
- Skill gap: With evolving technology, the talent pool needs to keep up with the latest developments. Rapid advancements in technology and frameworks have outpaced the availability of skilled professionals.
- High implementation cost: Considering the implementation cost and ROI, it is challenging for small to medium-sized companies to decide whether to implement these new technologies.
- Ethical and regulatory changes: Since the introduction of these technologies, multiple governments have expressed concerns about data privacy and data management. Numerous policies, such as GDPR, CCPA, etc., have been implemented. Therefore, abiding by such policies in a range of scenarios is a task.
Benefits of Staying Updated with Data Science Trends
Keeping up with the emerging trends in any domain is very important and gives you a fair amount of advantages in the industry. Here are a few benefits listed below:
- Early adoption of the latest technologies gives fair advantages and helps an organisation to outperform its competitors.
- Adopting new technologies ensures professionals are relevant in this rapidly evolving industry.
- New technologies open the door to more opportunities and innovation.
- Using advanced analytics and technologies ensures informed decision-making.
Conclusion
Overall, these are some of the latest trends in data science. The future of data seems bright and ever-evolving. I hope you enjoyed reading this article and are now aware of the upcoming trends in the field of data science.
If you want to keep up with the latest trends you can consider enrolling in Intellipaat’s Executive Professional Certification Program in Generative AI & Machine Learning in collaboration with IIT Indore. You will get exposure to all the latest technologies and trends with hands-on knowledge from industry experts and IIT faculty.