2025 Forecast: Top 5 Big Data Trends to Watch

1. The rise of AI and machine learning in data management

Artificial intelligence and machine learning are playing an increasingly central role in big data analytics and business intelligence. As organizations grapple to process and manage the amount of data they receive daily, AI and ML can process and analyze vast amounts of data at a rate and accuracy level that people simply can’t match. 

Across industries, businesses can harness AI and ML to automate big data processing, cleansing, structuring, and analysis. AI solutions can automate up to 64% of all data collection and up to 70% of all data processing tasks. 

AI and ML have become so prolific in the world of big data that more than 60% of IT leaders state they intend to increase their spending on AI and ML solutions for better data management to drive faster, more accurate insights. 

2. Data lakes and lakehouses optimizing storage

Traditionally, businesses have stored their data in data warehouses that act as typical databases that house structured data. They’re often used for quick, efficient reporting and insights. However, a data warehouse often lacks the architecture to house unstructured or semi-structured data, creating silos that can impact the insights generated. 

Data lakes have surged in popularity as a solution to this. Data lakes allow businesses to store and house raw, unstructured, and semi-structured data with no size limits or system origin limits. They can store all kinds of data including images, audio files, video files, and more. 

Based on a 2022 survey, 21% of organizations stated that they intended to increase their data lake spending by 10% or more in the next three years. 

More recently, data lakehouses have emerged as a hybrid data management solution consisting of both lake and warehouse capabilities. Data lakehouses offer the flexibility of data lakes as well as the structure and functional capabilities of data warehouses. However, the technology is still very new and has operational limitations. 

3. Greater adoption of cloud computing

With big data coming into organizations from an array of sources, storage capacity has become a serious issue. On-premises data storage systems generally don’t have the storage capacity to handle the terabytes and petabytes of incoming data, so greater adoption of cloud-based storage is anticipated to become one of the most significant big data trends of 2024. 

Aside from its simpler storage infrastructure and scalability, cloud computing provides a host of benefits, including reduced storage costs, greater operational efficiency, and decreased reliance on external data security services. Cloud computing provides the infrastructure that makes cloud-based storage systems like data lakes possible, along with cloud-based tools that power Data as a Service (DaaS). 

4. More sources of big data

Big data is being generated by multiple sources today, including smart devices, IoT technology, social media platforms, generative AI, drones, and sensors. For every new technology introduced to the market, there exists a new source of big data. 

While this presents new data collection opportunities for organizations it can also make data management more complicated depending on the type of data being collected and its source. However, once they’ve managed to solve this challenge, this new data can result in improved efficiency, better services, and higher revenue for businesses. 

5. Greater data governance and regulation 

As data privacy and user control become more important to consumers, analysts predict that businesses will pay closer attention to emerging data governance and regulation.

Privacy concerns, a lack of transparency around data collection and usage, and bias will likely be key drivers in the development of more stringent big data governance.  

Only 10% of users worldwide had their data protected by privacy laws in 2010. Experts estimate this number will jump to 75% by 2024. The collection and use of big data by private and government enterprises are regulated according to region, with the GDPR in place in Europe, PIPL in China, POPIA in South Africa, and PIPEDA in Canada. 

However, there’s a noticeable lack of federal regulation on data protection in the United States. Although there are a few acts in place, none of them are comprehensive enough to match the big data governance laws existing in other countries. On top of this, many U.S. states are beginning to pass their own data privacy laws. Businesses will undoubtedly be closely monitoring the emergence of new governance regulations as well as user sentiment regarding how and why their data is being used as they’ll both impact data trends in 2024.