Your cat’s birthday is in a few days and you are looking online for a toy to get her. You search online for a bit, and then you log into Facebook. Suddenly, every ad you encounter is about feline entertainment.
Coincidence?
Not in the slightest.
You were targeted and we have the big data stats to prove it clearly.
(I wonder what you will see on your wall after spending some time here.)
As machine learning advances, more and more information about your interests and searches gets collected and analyzed. To accommodate the growing volume of data, hosting services need to constantly grow and adapt as well.
But why is big data so important?
Key Big Data Statistics for 2026
A note on what follows. Big data statistics are unusually prone to mixing incompatible measures: data-volume forecasts, analytics spending, software revenue, cloud adoption and company case studies get stacked together as though they describe one market. They do not. This page leads with measured adoption rates, says who was counted, and keeps the older case studies clearly dated.
- 39.85% of EU enterprises performed data analytics in 2025, either with their own employees or through an external provider.
- 33.02% used their own employees for it. The rate was 78.84% among large enterprises and 27.86% among small ones.
- 26.20% analysed transaction records, 17.62% customer data and 11.44% social media data, using their own employees.
- 53% of EU businesses bought cloud services in 2025, rising to 85% of large businesses and 52% of SMEs.
- In a 2026 survey of 600 senior executives, 95% said data challenges had already slowed their AI progress.
One scope note that applies throughout: Eurostat covers enterprises with at least 10 employees in specified economic sectors, so these are not censuses of every business in the EU, and they are European rather than global figures.
What Enterprises Actually Analyse
The interesting question is not how much data exists, which nobody measures consistently, but what companies do with it.
Among EU enterprises in 2025, the most commonly analysed source was their own transaction records, at 26.20%, followed by customer data at 17.62% and social media data at 11.44%. These are the shares using their own employees, and the categories overlap, so they should not be added together.
That ordering is worth noticing. The data companies analyse most is the data they already had: sales records, order histories, payment information. The sources that attract the most attention, social media chief among them, are analysed by barely one enterprise in nine.
Cloud is the infrastructure underneath it
In 2025, 53% of EU businesses purchased cloud services, 85% of large businesses and 52% of SMEs. Buying cloud services is not the same thing as doing analytics, and this page no longer treats it as a proxy for one. It is the infrastructure layer most analytics now runs on, which makes it useful supporting context rather than an adoption rate.
For the fuller picture of that infrastructure, see our cloud computing statistics and cloud adoption statistics.
The Gap Between Large and Small Companies
The single most striking number in the current data is the size gap. In 2025, 78.84% of large EU enterprises performed data analytics with their own employees. Among small enterprises the figure was 27.86%.
That is a difference of more than 50 percentage points on the same measure in the same year, and it is a better description of where analytics actually sits than any headline about the data economy. Analytics capability remains concentrated in organisations large enough to employ people to do it.
The external-provider route narrows the gap a little: 13.85% of enterprises used one, which is how the combined figure reaches 39.85%. Those categories can overlap, so the two should not be summed.
Data problems are now an AI problem
Veeam surveyed 600 senior executives across North America, Europe and Asia-Pacific between March 16 and April 6, 2026, covering financial services, healthcare, manufacturing, retail and technology. In that survey, 95% said data challenges had already slowed their AI progress.
Two caveats belong with that number. It is vendor-sponsored research among C-level respondents at larger organisations, not a representative sample of all businesses, and 95% of 600 executives is not 95% of companies. With those stated, it captures something the adoption percentages do not: the constraint on analytics has moved from collecting data to being able to trust and use what has already been collected.
Big Industries Using Big Data
Big data is useful across the board but certain industries benefit from it much more than others.
Healthcare
- Consulting firm McKinsey estimated that big data analytics adoption can save up to 17% of healthcare costs. In 2013 that amounted to $493 billion dollars in reductions!
Banking
- Modern customers look for a highly personalized experience. In fact, 84% of executives, surveyed by Oracle, agreed to this. 81% of them believe the solution lies in IT cloud development.
- Adoption of big data in the field will bring up to an 18% increase in revenue. For a $1 billion company, this would come up to $180 million a year!
- The American Express Company has already jumped on the BDA train. By analyzing over a hundred variables, they can now accurately predict 24% of the accounts that will close within 4 months.
Media
- With more than 70 million active users, Miniclip is one of the largest gaming websites. To retain customers and increase revenue, the company uses big data. Analyzing the collected information helps determine which games will be more successful.
- Statistics prove Miniclip’s migration to Amazon Web Services (AWS), a cloud platform specialized in collecting and processing big data, was a very smart move. The new game deployment now takes 4 hours, where it used to take 4-5 weeks!
- By moving to AWS, Miniclip saved $100,000 for new load balancers.
- The website now has availability in the five 9s. Latency was cut in half – from 4.5 seconds to 2 seconds. Time to market was decreased by a staggering 97%!
- The entertainment giant Netflix is another one of the companies using big data. The analysis of the massive amounts of data collected from their 100 million subscribers, has allowed them to predict each customer’s interest.
- Big data influences 80% of all movies and shows watched on Netflix.
- Back in 2009, the company offered a million dollars for whoever comes up with the best prediction algorithm. This move (and the winning algorithm) have been saving Netflix $1 billion a year from customer retention!
Retail
- Naturally, the amazing stats about big data didn’t go unnoticed by Amazon. The vast amounts of data were why they created AWS – their own cloud computing platform.
- Amazon creates an individual “360-degree view” profile of each customer. They group you with others with similar interests to recommend products you’ll like.
- Before 2016, the company hardly had any profit. After the introduction of AWS, Amazon’s income skyrocketed. In 2017 they earned $3 billion, and in 2018 – $10.1 billion.
- Starbucks wouldn’t have been the coffeehouse chain we know, had they ignored the statistics about big data analytics! Their business has been constantly growing thanks to their smart information gathering.
- The Starbucks mobile app has more than 17 million users, the reward program – 13 million. One-third of the purchases are made online. Using the information customers shared there, they learn more about purchasing habits.
- The strategy is working well – Starbucks was projected to have 37,000 stores worldwide by 2021. Unfortunately, it fell short and currently has 33,295 in 2022.
- Personalization and engagement are working their magic. In 2017, 18% of customers accounted for 36% of the sales!
Energy and Utilities
- The worth of big data got its fair share of attention from the energy industry as well. General Electric vastly increased its efficiency by using information from sensors on turbines and engines.
- The company estimates big data can boost US productivity by 1.5% YoY. Those numbers stack up nicely in the long run!
(Sources: DisruptorDaily, IDC, TexasAMA, CIO, DestinationCRM, Medium, Eastern Peak, Oracle, Amazon, Miniclip, InsideBigData, DataFloq, CNBC, Forbes, TechHQ)
Industries That Are Moving Fast Towards Big Data
Big data reaches far.
Medicine
- Physicians can monitor their patients closer than ever before. Data collected from wearable trackers provide valuable insight – something, that would be impossible with the usual brief visits.
- Big data allows hospitals to create statistics about the effectiveness of different treatments and drugs. This not only improves healthcare but can also greatly reduce costs.
- Data can lead to significant improvements in ER treatment. After a hospital used the information they collected, the length of stay was reduced by 40%, and the effectiveness improved by 50%.
- Local public health can also benefit from big data. It helps city inspectors to prioritize high-risk establishments and catch violations before they become a hazard.
Construction
- Construction companies are now able to better estimate their price quotes. By analyzing big data and using the industry stats in every country, they can track material-based expenses.
- Knowing how long a project will take is also much easier when companies can compare it to similar work in the past.
- After switching to the BDA interface, 98% of sales representatives reported a huge improvement in time, needed to calculate costs.
Transportation
- Public transport in London uses big data to provide commuters with personalized details and information about delays.
- Trains’ condition is monitored by a variety of sensors. One hundred trains can create up to 200 billion data points yearly. This improves safety in previously unthinkable ways.
(Sources: Towards Data Science, big data – Made Simple, ScienceDirect, Bernard Marr)
Popular Big Data Access Methods
Where can you find the biggest data?
Amazon Web Services (AWS) S3
- AWS S3 is Amazon’s storage service. It is designed for 99.999999999% data durability, the figure usually called eleven nines. Durability is the probability of not losing stored objects, which is a different thing from uptime or availability.
- Its simple interface and reliable service make AWS S3 one of the most liked big data tools.
- Millions of companies around the globe use AWS S3. Some of the more popular ones include:
- NASA – particularly images received from the Curiosity rover.
- Netflix – the company transferred to AWS S3 in 2015.
- Nokia – they went for this platform to improve scalability.
- Samsung – the Printing Apps Center was launched on the platform.
- Slack – they’ve been using AWS S3 since 2009.
- Adobe – LiveCycle Forms and Connect are two products that run on AWS.
- Airbnb – their entire database is on the platform.
Spark SQL
- Spark SQL can read data from both semi-structured and structured data. It also includes columnar storage, code generation, and cost-based optimizer.
- It can connect to Spark programs and external tools like Tableau.
- Spark SQL simplifies working with structured datasets – it provides DataFrame abstraction in Java, Scala, and Python.
- Some of the companies using this program to manage big data are:
- UC Berkeley AMPLab
- Alibaba Taobao
- Autodesk
- eBay Inc.
- IBM Almaden
- NASA JPL – Deep Space Network
- Shopify
- TripAdvisor
- Yahoo!
Hive
- Apache Hive simplifies reading, writing, and managing large datasets in distributed storage.
- This big data tool is used mostly in the United States, in companies working with Computer Software. They commonly have over $1 billion in revenue and between 50 and 200 employees. Some examples are:
- Facebook Inc
- Hortonworks Inc
- Qubole
- Castle Global, Inc.
- Groupon, Inc.
HDFS
- The primary data storage of Hadoop applications is the HDFS (Hadoop Distributed File System).
- HDFS was originally created as a part of the Apache Nutch web search engine project.
- It’s highly fault-tolerant – a big difference from other distributed file systems.
- HDFS can run on low-cost hardware.
- These advantages have convinced many companies to integrate it into their systems. These include:
- Talentburst
- Unity Technologies, Inc.
- Intel
- Indeed, Inc.
- Microsoft
(Sources: Zoomdata, CNBC, Amazon, TechRepublic, Network World, Enlyft, Apache, DZone, Apache, Enlyft, Apache)
Most-Adopted Big Data Analytics
Big data is only as useful as your ability to read it, and the tooling is where that gets decided. The adoption figures earlier on this page describe how many enterprises analyse data at all; these are the tools they reach for when they do.
So which tools do companies employ to analyse data?
Apache Spark MLib
- MLib began as a part of Apache Spark. This is why it’s updated with each new Spark release.
- The algorithms MLib uses are very high-quality – the results are more accurate than the one-pass approximations on MapReduce.
- MLib runs fast, thanks to Spark’s strong>iterative computation. For comparison, it’s 100 times speedier than MapReduce!
- Users are encouraged to help the project grow. They can suggest patches directly to Apache.
TensorFlow
- TensorFlow is one of the most-adopted big data analytics in enterprises today.
- Not only does it have an extensive choice of libraries and tools, it’s also fully open source.
- It makes model building easy, thanks to its intuitive high-level APIs.
- Users are able to train and deploy machine learning models in the browser, cloud and even on-device.
(Sources: Forbes, Apache, TensorFlow)
Big Data Tools
To harvest big data you need a giant harvester.
Apache Hadoop
- Hadoop is the software product that always gets mentioned when the topic of BDA arises. It doesn’t require much hardware-wise and can run both on-prem and in the cloud.
- Hadoop is famous for its huge-scale data processing. It’s an open-source framework and can provide storage for any type of data.
- Some of the better-known features are:
- HDFS
- MapReduce
- YARN
- Hadoop Libraries
Apache Cassandra
- Apache Cassandra is well-known for being a very scalable and resilient database. It’s also relatively easy to learn and configure.
- It’s being used by huge companies like Facebook, Netflix, Twitter, and Cisco.
- Cassandra can handle heavy workloads thanks to its architecture.
- The stats point to it being is one of the most reliable big data software.
- Apache Cassandra also offers capabilities that no other NoSQL or relational database can. These include:
- Exceptional linear scalability
- High fault tolerance
- Simplicity of operations
- Built-in high-availability
MongoDB
- MongoDB is an open-source NoSQL database. It’s compatible with a variety of programming languages.
- This tool is best for working with semi or unstructured data sets or ones that frequently change.
- MongoDB is also great for data storage from CMS, product catalogs, or mobile apps.
- Some of MongoDB’s capabilities are:
- Storage of any type of data
- Cloud-native deployment
- Flexibility of configuration
- Database partitioning
Neo4j
- Neo4j is an open-source graph database.
- The tool performs well even under a heavy workload of data and graph requests.
- Neo4j’s most prominent features are:
- Flexibility
- High-availability and scalability
- Support of ACID transactions
- Cipher graph query language
- Integrations with other DB
(Sources: Analytics Training, Towards Data Science, TechTarget, Whizlabs, IT Svit)
Big Data Use Cases
Let’s see how big data revolutionizes industries already.
Data Warehouse Optimization
- Many corporations use data warehouses to handle their BI needs. The cheapest and easiest way to manage that information is to utilize open source big data solutions like Hadoop.
- This ensures faster operation speed and lowers costs.
- The whole “big data vs business intelligence” competition has an obvious winner – traditional BI tools don’t scale when the users and data increase.
- Customers now look for insights that only ML can provide. This calls for analytical tools that can work with all types of data.
- Data warehouse optimization aims to facilitate a built-in scalable query mechanism that allows running individual workloads.
Price Optimization
- BDA can provide companies with valuable insight into which prices have achieved the best results. It’s hard to maximize income without losing customers.
- Utilizing big data software also allows for dynamic pricing. Companies can now build models predicting how much a customer will be willing to pay, as circumstances change.
- BDA usage is very common, especially among B2B companies.
Recommendation Engines
- This is one of the most popular uses of big data analytics.
- BDA of historical data is why platforms like Amazon and Netflix always seem to know what you’ll like.
- Most users now expect a recommendation engine when they’re shopping. Therefore, organizations that don’t utilize the data they’ve collected may lose their customers to competitors.
Preventive Maintenance and Support
- The industrial sector can also benefit from predictive analytics. Companies in the energy, agriculture, manufacturing, and transportation have already come to this conclusion.
- A variety of sensors constantly collect data from expensive equipment. They form the Industrial Internet of Things – IIoT.
- Analyzing the collected data can help detect malfunctions before they cause an accident. This saves companies a lot of expenses.
(Sources: Datamation, EDUCBA, HPE)
Benefits of Big Data and Big Data Analytics
In case you are doubting it still, big data has incalculable benefits. Just kidding. Proper big data analytics can calculate anything.
Reduced Cost
- Big data software can help companies improve their processes and customer service. This increased effectiveness can have a big impact on reducing cost.
- Surveys by Syncsort and NewVantage showed that BDA has helped 59.4% of respondents to decrease expenses.
- 66.7% of companies stated that they began using big data for that purpose.
- Almost 55% of respondents are aiming to instead increase their revenue and growth with BDA.
Increased Productivity
- The high speed at which BDA tools operate allows businesses to make quick decisions.
- Syncsort study indicates that 59.9% of companies use software like Hadoop to increase their productivity.
- The big data statistics show that BDA increases both employees’ personal productivity and the effectiveness of operations in larger structures within companies.
New Product Development
- BDA allows companies to keep up with trends and create successful products.
- According to a NewVantage survey, 11.6% of executives are investing in big data with the goal of finding means of innovation.
- The insights BDA offers can help a company pull ahead of its competitors.
Better Decision-Making
- Big data allows organizations to better understand the constantly changing market conditions. Analyzing what people are purchasing helps companies plan ahead and produce what their customers want.
- 36.2% of enterprises interviewed for a NewVantage study stated that better decision-making is why they’re investing in BDA.
- 59% of companies confirmed they experienced success in this area, thanks to BDA.
Fraud Detection
- The financial industry is understandably very interested in big data and analytics when it comes to fraud detection.
- Financial institutions use algorithms based on machine learning, so they excel at finding patterns and anomalies. This allows for a fast reaction in case of fraud.
(Sources: NewGenApps, Datamation, Syncsort, Syncsort, NewVantage Partners)
Conclusion
Now that you’ve been amazed by all these big data stats, you can continue your cat toys research. Go ahead and teach that AI exactly what entertainment your feline companion prefers. That way you can get some awesome suggestions!
