Top 20 Big Data Trends and Predictions to watch in 2026

With more than 2.5 quintillion bytes of data being generated daily. It is more than safe to assume that Big Data is gearing up for changing the way we think!  Big data, in association with Artificial Intelligence, Machine Learning, and other technologies, is fueling what we call the Fourth Industrial Revolution. According to Research Nester, the big data and business analytics market size is evaluated at more than $309.68 billion in 2025 and is projected to reach $940.44 billion by 2035. In 2026, the industry size of big data and business analytics is evaluated at $343.4 billion.

The big data market is booming, driven by digital transformation, AI/ML, and the need for data-driven insights, expected to surge from hundreds of billions in 2024 to over a trillion dollars by 2030-2035, with strong growth in cloud solutions, real-time analytics, and adoption across sectors like BFSI, Healthcare, and Retail.

Here, data professionals share their big data and database/infrastructure predictions for 2026:

1. In 2026, unstructured data will emerge as the backbone of AI innovation, redefining how enterprises harness intelligence across their organizations.  As AI continues to advance, the availability of high-quality structured data is reaching its limits, creating what many analysts call a “data ceiling.” However, with an estimated 80–90% of enterprise data being unstructured, from documents and emails to images, videos, and design files, the potential to unlock its value has never been greater.  In 2026, having a comprehensive strategy for enterprise unstructured data is no longer considered “being a step ahead”, but vital for AI success – Nick Burling, chief product officer, Nasuni

2. Enterprise AI infrastructure needs will change how data is stored, managed, used, and accessed by applications: Agentic AI and AI-driven workloads require reliable data storage, information streamed in real time, events organized by context , and the reuse of data for new models. Therefore, the infrastructure to support them will be expected to include powerful compute (CPUs, GPUs, TPUs), high-performance networking, scalable storage, and robust security and governance measures. That’s the reason why enterprise AI data infrastructure market is headed to a $7 trillion valuation in 2030 – Tiago Azevedo, CIO at OutSystems

3. The enterprise data stack will become “agent-ready” by default: B

y the end of 2026, connectivity, governance, and context provisioning for AI agents will be built into every serious data platform. SQL and open protocols like MCP will sit side by side, allowing both humans and machines to query, act, and collaborate safely within the same governed data plane.—Redpanda’s CTO Tyler Akidau

4. Data operationalization sparks “digital twins” for data:

For 20 years, data was something you looked at in a quarterly report. Now, it’s being operationalized as a core asset of products themselves or may also be the product itself. However, the underlying data environment is a complex web of hidden dependencies that can be easily disrupted in many organizations. As they prioritize IT resilience, more companies in 2026 will invest in creating real-time “digital twins” of their data ecosystems—not to monitor the data itself, but to manage the complex “skeleton” of pipes that holds it all together – Ram Chakravarti, CTO, BMC Software

5. A developer exodus is on the horizon:

Some open source projects, of which PostgreSQL is one, are heavily dependent on “grey beard” developers, who are highly experienced in what they do. Younger developers on projects like these are far scarcer, often because the languages and processes used by the projects are not the modern ones they’re used too. In PostgreSQL’s case, a cultural shift could be made to use modern tools like GitHub Pull Requests rather than patches that are shared via email, but it can be hard to persuade the long-time developers to change their ways —Dave Page, vice president of engineering, pgEdge

6. A unified data estate becomes the strategic battleground:

The era of focusing solely on GPU availability is coming to an end. The real competitive advantage lies in creating unified, global data estates that can power inference and generative AI at scale. In 2026, infrastructure players who can eliminate silos across sites, storage systems, and clouds will become the most strategic players in AI adoption.—Molly Presley, SVP for global marketing, Hammerspace

7. The new generation of cyber pros will finally fix data sprawl:

In 2026, the biggest cyber threat will not come from a new malware strain or a state-sponsored attack. It will come from the growing sprawl of data. Sensitive information now flows through thousands of APIs, SaaS platforms, and partner ecosystems, often without clear ownership or enforceable controls. In 2026, their creativity and pragmatism could be what finally restores control to an increasingly borderless world—Steve Cobb, CISO, SecurityScorecard

8. Return of the service mesh:

Service meshes will make a strong comeback. Early excitement with the technology gave way to disillusionment because sidecar-based architectures were difficult to manage. The introduction of ambient mode has simplified adoption by moving proxies to the node level. This reduction in complexity will encourage renewed adoption; in 2026 Istio ambient mode will likely become the most widely used service mesh technology—Ratan Tipirneni, CEO at Tigera

9. The Rise of predictive analytics

Mark Darbyshire, Chief Technologist at SAP UKI: “With the dawn of the ‘zettabyte era’, and the world churning out more than a trillion gigabytes of data, 2022 will see businesses looking to predictive analytics to uncover trends and patterns and gain unprecedented insight into customers, businesses and markets.

“This will allow them to go beyond reaction, to anticipating and shaping better business outcomes. Insurance company Aviva, for example, is using predictive analytics to target the right customers, from their 31 million-strong customer base, with the right offers at the right time.”

10. AI and Cognitive Machines

Chetan Dube, CEO, IPsoft: “I believe a tectonic shift in the relationship between man and machine is imminent. As the intelligence of cognitive systems matures it will carry humans to higher planes of creative thinking.

“These cognitive machines are going to redefine the business landscape. In fact, I wouldn’t be surprised if within the next 10 years, you will walk down the corridor past a co-worker and not know if they are human or machine.

The rise of artificial intelligence is empowering businesses and industries across the globe and empowering people with unprecedented capabilities. While the general perception is of “massive overlays and mass firings“, the experts say that humans are still going to be “crucial“.

11. Big Data Fabric

Big Data Fabric is an emerging concept of a platform that can accelerate and refine business insights. As per Forrester, the platform will automate the ingestion, discovery, curation, integration, and preparation of data from data silos.

Hence, the business organizations will have a set of data services to deliver capabilities across all the business verticals and a choice of business endpoints, in a consistent manner. Further, the platform will standardize data management practices and practicalities across hybrid multi-cloud environments.

Offering unparalleled analytical services, the big data fabric will empower the business networks with enhanced security across the cloud networks, on-premise systems, and edge devices.

12. Climate Change Research

Climate change research comes under the umbrella term “X Analytics” that is coined by Gartner. “X” stands for a data variable for a wide range of structured and unstructured content, such as video analytics, text analytics, and audio analytics.

Leaders in the data analytics segment utilize X analytics to solve the toughest challenges to humanity, such as disease prevention, wildlife protection, and climate change.

Big Data, in unison with other technologies, such as artificial intelligence can comb through millions of research papers, news sources, clinical trials, and academic content pages to help climate researchers. The researchers can find new ways to contain the massive climate change, create containment plans for severe outcomes in red zones and identify the most vulnerable population pools via graph analytics, etc.

Also Read:

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13. Data Lake

Data lakes are a new type of architecture that is changing the way companies store and analyze data. Historically, organizations would store their data in a relational database. The problem with this type of storage is that it is too structured to store all types of data such as images, audio files, video files and more. Data lakes allow organizations to keep all types of data in one place.

Gareth Martin, Analytics Portfolio Lead EMEA, HPE: “Organisations are assessing the success of their Big Data programs and realise that without a specific focus on the value from analytic solutions, they don’t tend to break even as quickly as planned, or at all.

“Rather than build a data lake, companies are moving toward building analytic labs to focus on value-based solutioning.”

14. Overcoming the limits of legacy systems

Bob Wiederhold, Couchbase CEO: “In 2016, we’ll see more enterprises re-platform their data management systems using NoSQL to overcome the limits of their 30-year old legacy relational systems.

“Mobile applications must be fast, responsive and reliable to meet end users demanding expectations. To meet these demands, mobile developers are no longer building applications for the best case performance scenario, they are building apps for the worst case scenario – guaranteeing an ideal mobile experience regardless if connectivity is ideal or spotty.

15. Salaries to skyrocket

Mike Maciag, COO, Altiscale: “Salaries for both data scientists and Hadoop admins will skyrocket in 2016 as growth in Hadoop demand exceeds the growthof the talent pool.

“In order to bypass the need to hire more data scientists and Hadoop admins from a highly competitive field, organizations will choose fully managed cloud services with built-in operational support.

“This frees up existing data science teams to focus their talents on analysis instead of spending valuable time wrangling complex Hadoop clusters.”

16. Skill shortages to persist

Dan Graham, GM of Enterprise Systems at Teradata: “Shortages of analytics experts will persist, expanding from data scientists to power users, architects, and data management experts.

“Loss of subject matter experts and their knowledge coupled with scarce replacements will force corporations to apply knowledge management techniques to analytics staff.

“Business Intelligence tools will compete by including collaborative features for the capture, reuse, vetting, and tagging of tribal knowledge to fill gaps.”

17. The age of the algorithm

Prof.Dr. Michael Feindt, Founder, Blue Yonder: “Everyone has big data now, but raw data on its own provides no value. “Only by applying algorithms will people find transformative value from their data. Algorithms help organisations put their data to work, providing predictive analytics and automated decisions. Data visualization tools are useful in analyzing large sets of data using graphs and dashboards thereby creating reports helpful for taking business decisions.

“Algorithms create action; without action you achieve very little. With the Internet of Things taking off; smart phones; driverless cars; connected devices, comes more and more data. This data requires algorithms to make sense of it; to create operational efficiencies; to predict outcomes and make decisions based on this; to differentiate your brand; to stay ahead of the competition.

“Also progress in medicine could be much faster if data were exploited better.”

18. Recognizing customers

Andy Lawson, MD and SVP at Salesforce UK: “2016 will be the year of 1-to-1 customer engagement. With the number of connected devices soon to reach 75 billion, businesses need to recognise that behind each of these devices is a customer, and the opportunity to connect with them has never been greater.

“Businesses will start to use the huge amounts of data generated by connected devices to discover insights that can be used to engage with customers in an entirely new way. They will be able to track, respond and even anticipate their customer’s needs, creating a personalised 1-to-1 customer experience. I believe this new approach to business intelligence will enable organisations to transform their relationships with customers.”

Also See:
The Importance of Big Data Analytics in Business
Best Practices for Big Data Protection
Using Big Data For Traditional Marketing Tactics
How to use big data to build your e-commerce brand
What You Need to Know About Working in Big Data

19. Big data becomes omnipresent through apps

Michael Benedict, Chief Product Officer, Progress: “This first wave of Big Data focused on the infrastructure stack-storage, scale and integration. It’s the next wave of technology that I’m most excited about, because it will make Big Data mainstream and consumable by everyone.

“Companies will stop thinking about Big Data as a big data warehouse to be managed and scaled. Instead, they’ll think about the marketing analytics application that automatically provides the next best piece of content to users and drives higher conversion levels. True Big Data value will emerge from this next wave of applications and services. These are the ISVs to watch.”

20.More data sources (IoT, smart devices)

There are many different ways that we can now collect data including sensors, social media platforms, and even smart devices.  With every technology introduction to the mass market, we should expect more data to be generated thus increasing the challenges of managing big data from new sources. However once done properly, these data might help organizations better serve their clients and improve their business model.

21. Advanced big data tools

In order to handle big data properly and get the most out of it, organizations need advanced big data tools that are investing in cognitive technologies such as Artificial Intelligence and Machine Learning in order to facilitate big data management and help them get more insights.

(Updated on 23 Dec 2025)