The Ultimate List of Big Data Statistics for 2024 — Data Should Work, Not Overwhelm

Regardless of whether they were able to make profits, all organizations across the world agree that big data is critical for their digital transformation.

In fact, organizations of all sizes have invested anywhere from US$ 1 million to US$ 1 billion in data-related projects. And at least 91% of the organizations that have invested in data-related projects say they were able to derive some benefit from it. (Source: Accenture).

Scroll down to find the big data use cases, market, benefits realized by the businesses, existing challenges in using data, and more such big data statistics.

Big Data Statistics: Global Big Data Market Size

1. Global big data analytics market is projected to reach US$ 234.6 billion by 2026, growing at a CAGR of 10.2% between 2022 to 2026.

(PR Newswire)

PR Newswire estimates that the global big data market will be worth US$ 154.9 billion in 2022.

The Solutions segment — the adoption of big data analytics among large enterprises — is forecast to grow at a CAGR of 11.8% during the forecast period. It is estimated to reach US$ 170 billion by 2026.

Equally important to note, the US big data market is estimated to be worth US$ 55 billion in 2022. The United States dominates the big data market, accounting for 51% of all spending. For context, China’s big data industry is forecast to be around US$ 27.9 billion by 2026 (exhibiting a CAGR of 12.1%).

Given the proliferation of technology-backed cost-effective data structures in India, China, and Japan — the Asia Pacific will be the fastest-growing region.

Japan’s big data market is forecast to grow at a compound annual growth rate of 8.9%. Within Europe, Germany is the fastest-growing country, exhibiting a CAGR of 9.7%.

However, in contrast, Statista estimates highlight that the big data analytics market was valued at US$ 215 billion in 2021. And its forecast estimates the big data market worth will be US$ 274.3 billion in 2022. The big data market value was US$ 186 billion in 2018, which shot up by 62% in just four years.

2. 2.5 quintillion bytes of data is generated every day. In 2020, the amount of data was estimated to be around 44 zettabytes. By 2025, the number will reach 463 exabytes globally.

(IBM)

2.5 quintillion bytes of data means 2,500,000,000,000,000,000 bytes of data. That is to say — one quintillion bytes of data is equal to 10¹⁸ bytes of data.

However, the report comes from IBM’s “how much data is created every day” report of 2016. Today, 2.5 quintillion bytes estimation must be inaccurate. (There is no reliable source on how much data is created every day in 2022).

Nevertheless, here’s a quick overview of just how much data is created by internet users across different platforms:

• IDC’s 2020 report highlights on average an internet user creates about 1.7 megabytes of data every second.
• Datareportal’s 2022 Global Overview indicated that there were 4.95 billion internet users at the dawn of this year.
• More than half of the internet data, or online data, comes from mobile devices. In fact, mobile data accounts for 55% of all internet data.
• Datareportal estimates there were about 4.6 billion social media accounts worldwide at the dawn of 2022.
• Facebook’s big data statistics highlight that its users create about 4 petabytes of data each day (one million gigabytes).
• Facebook’s big data statistics highlight about 1.5 billion daily active users. However, Facebook data is only the tip of the iceberg.
• For instance, Forbes highlights that users watch 694,444 hours of video every minute on Netflix, 4,500,000 videos on YouTube, etc. And the list goes on.

Plus, there are about 99,000 queries made on Google every second, which holds about 92.49% of the search engine market (so the actual number of queries made by internet users per second exceeds that number).

Big Data Statistics: Usage by Industry, Organization Size, and Function

3. 78% of very large organizations (10000+ employees), 48% of large organizations (1000+ employees), and 43% mid-size organizations (100+ employees) use big data.

(Dresner Advisory)

Microstrategy’s big data growth statistics indicate that 90% of organizations think digital transformation is impossible without big data investments.

Nevertheless, only half of the organizations are currently using or experimenting with big data statistical analysis projects. Dresner’s big data trends state that only 50.1% of organizations (all sizes) have adopted big data as of 2021.

To break it down based on organization size, 78% of very large organizations use the technology, while the remaining 22% plan to use it soon.

However, it is critical to note that the adoption in very large organizations has declined by 4% compared to 2018. In 2018, some 82% of very large organizations were using big data.

Additionally, 54% of large and 50% of mid-size organizations are looking to adopt the technology in the near future.

4. 59% of small organizations are seeking employees with data literacy skills.

(IMA’s Technology Enablement)

Equally important to note, the Strait Times report on the data analytics market states that at least 70% of small organizations (1-100 employees) have not adopted big data analytics.

As much as 21% have no plans to adopt big data at all. The good news is that 59% of small organizations are seeking employees with data literacy skills.

Small businesses must understand that all types of organizations experience the same benefits (and difficulties) with big data.

Nevertheless, most small organizations haven’t adopted the technology — statistically speaking — because they do not have enough data to start. (IMA’s Technology Enablement reports).

Most SMEs say that “data collection isn’t a priority.” So, even if they are using the technology, they do not have large sets for the analysis. And maybe this is the reason Accenture reports that large organizations can derive more value from data compared to smaller organizations.

5. Telecommunication (87%), financial service (76%), and healthcare (60%) are the top three most active industries in big data usage.

(Dell)

Dell’s big data stats highlight that customer acquisition (93%), network optimization (85%), and customer retention (81%) are the top use cases for the telecom industry. Moreover, 13% of telecom companies are looking to invest in big data statistical analysis soon.

The healthcare industry is another leading industry with significant big data investments.

60% of healthcare organizations have already adopted the technology for personalized treatment (98%), admission predictions (92%), and management and optimization purposes (92%). The remaining 40% are looking to invest in the near future.

And there’s a good reason why almost all healthcare organizations globally are looking to invest in statistical analysis projects — there are numerous benefits of implementing the technology. McKinsey reports that the healthcare industry could save up to 12% – 17% (about US$ 300 billion to US$ 493 billion) of the annual cost using big data.

Also, less than 20% of the education industry is data-driven; however, at least 80% of educational organizations are looking to adopt the technology soon.

6. 76% of the global banking sector uses big data; at least 62% consider it “very critical” for their success.

(Easternperk)

Of the data-driven financial institutions, only 27% are in the production phase. 44% are still in the experimental phase, while 29% are in the proof of concept phase.

Customer analytics (44% cited it), predicting market trends (18%), risk and operational management (16%), and real or near-time data (9%) are the key drivers for implementing a data strategy among the financial institutions.

At least 82% of global banking sector executives say data will help them deliver a personalized experience to the users. In fact, Intelligence Automation reports data analytics can potentially increase the revenue of the financial sector by 18%.

Nevertheless, only 29% of the banking sector reviewed said they were able to derive value from the data sets.

At least 49% say they are still figuring out how to derive benefits from data. 47% say they are still understanding the processes that will enable them to receive maximum benefits from all the data available. (Some 72% of data is never utilized by the organizations).

Equally important to note is that the global BFSI big data industry worth will be US$ 37.55 billion in 2022.

PRNewsWire’s big data stats estimate the global big data revenue of the BFSI will be worth US$ 57.9 billion by 2026, exhibiting a CAGR (compound annual growth rate) of 9.9%. The US dominates the current BFSI big data segment, with over 41.3% market share.

Big Data Statistics: Technologies and Software

7. Business intelligence and analytics software applications market is forecast to reach US$ 18 billion by 2025, exhibiting a CAGR of 3.5%.

(Statista)

Business intelligence and analytics software are designed to retrieve, analyze, transform and report relevant data by processing large amounts of unstructured data.

As of 2021, Microsoft’s Power BI was the most popular business intelligence and analytics software globally, commanding 36% of the market share.

Tableau Desktop (20%), Qlik Sense (11%), and SAP Analytics Cloud (11%) were other leading players, collectively owning more than 40% of the BI market.

However, the global BI adoption rate is just 26%. Of the organizations that use BI technology, at least 67% of organizations have reported using multiple BI software. On average, an organization uses 3.6 BI software.

Some 28% are looking to reduce the number of BI solutions accessible, while 15% want to add even more BI tools.

On a side note, the business intelligence tools, along with other tools like CRM software, accounting applications, marketing automation tools, ERP tools, and more, are collectively known as enterprise software applications — its market size is forecast to reach US$ 243.03 billion in 2022.

8. Spark is the most opted big data infrastructure. Elasticsearch is the top chosen big data search mechanism. And Amazon S3 is the most popular data access method among organizations.

(Dresner Advisory)

• Kafka and Google Dataflow are the second and third most popular data infrastructure.
• Spark SQL and HDFS are the second and third most popular big data access method after Amazon S3 among organizations globally.
• Plus, Microservices and Kubernetes are organizations’ most opted data deployment technologies.

Big Data Statistics: Consumption of Big Data Analytics

9. 72% of organizations say big data analytics is “very important” or “quite important” to accomplish business goals.

(Statista)

In response to which technologies are important for meeting business goals, 73% of organizations responded that SaaS (Software as a Service) technologies are “very important” or “quite important” to accomplish business goals.

At the same time, almost 72% of organizations said big data technologies are helping their organization realize their business goals.

In comparison, other technologies like machine learning/artificial intelligence (52%), robotic process automation (46%), internet of things (47%), voice technology (28%), and blockchain technology (26%) are considered important by less than half of the organizations globally.

This proves that big data solutions have several use cases. In fact, McKinsey’s statistics state that there are over 160 use cases of big data among organizations globally.

For instance, a third of all marketers said big data solutions are very important for their advertising campaigns. And marketers use the technology for a reason: Aberdeen’s big data industry statistics highlight that data-driven culture can increase brand awareness by 2.7x.

10. Data warehouse optimization and forecasting are the two top use cases of big data among global organizations; 75% consider them “critical” or “very important” use cases.

(Dresner Advisory)

Year on year, data warehouse optimization has remained the top use case (since 2015).

The third most popular use case is customer and social analysis, with over 50% considering it “very important” or “critical.”

Additionally, data mining, advanced algorithms, and predictive analytics are other top use cases, with at least 78% considering it “critical,” “very important,” or at a minimum “important.”

McKinsey’s data growth statistics state that there are over 160 use cases of big data among organizations globally. However, 88% of the organizations are either in the experiential or proof of concept stage.

Among marketers, identifying the ways to increase sales (18%), understanding customer behavior (17%), and targeting the right customers for products and services (17%) are the most popular use cases of big data.

Big Data Statistics: The Benefits Realized by Applying Big Data Analytics

11. Organizations applying customer data analytics extensively are 3x more likely to generate above-average turnover growth than laggards (43% vs. 15%).

(McKinsey)

McKinsey’s big data analytics statistics highlight that companies that apply big data analytics extensively outperform laggards (the companies that do not use data analytics extensively) in many ways.

For instance, organizations using data analytics extensively are more likely to generate above-average profits than laggards (49% vs. 22%). Their sales are twice as much as the laggards (50% vs. 22%).

At the same time, the ROI is significantly higher for companies using data analytics extensively than laggards (45% vs. 20%).

12. 94% of organizations feel their big data implementation meets their needs. Plus, 92% are satisfied with the business outcomes.

(Accenture)

Plus, 96% of executives believe artificial intelligence, analytics, and all the data stored will become more important to their organizations in the next three years.

89% consider big data will revolutionize their businesses just like the internet in the 90s. Some 85% say big data will change their way of doing business. 79% say they will lose their competitive position or even go bankrupt if they do not utilize data.

Accenture data statistics report that 91% of companies will build or increase data science expertise within their organization by next year.

The reason most organizations are building and increasing data science expertise within their organization is that they believe big data has the potential to impact these business cases within the next five years:

• 63% of the organizations believe big data has the potential to improve their customer relationship.
• 58% say it will help them redefine their product development.
• Some 56% say it will change the way they conduct their operations.
• 48% say it makes their business more data-focused.
• 47% believe big data technologies will optimize their supply chain.
• 27% believe the data-driven culture will change the way they do business.

13. Successful companies (applying data extensively) are 23x more likely to acquire new customers than laggards.

(McKinsey)

Extensive use of customer data has benefits across the full customer lifecycle. McKinsey’s big data statistics report:

• Successful companies (applying data extensively) are 23x more likely to acquire new customers than laggards.
• The customer retention rates are 6.5x higher, and customer loyalty rates are 9x higher for companies using data analytics extensively compared to the laggards.
• Successful companies can sell to existing customers 7.4x more than the laggards.
• There is a significant increase in customer satisfaction rates of successful companies compared to the laggards (81% vs. 14%).
• Value delivered to customers is 15x more than the laggards (76% vs. 5%).
• They can also generate more revenue in terms of customer profitability (75% vs. 4%).

These key big data statistical analyses reveal a correlation between how extensively the organizations use the enormous data sets and their performance. Applying data analytics is not fruitful enough if the company is a non-intensive user.

Equally important to note, these big data statistics are based on a review of 400 large companies, meaning they have the time and resources. But it does not mean that small businesses do not possess the time and resources to use customer data extensively.

There are small business application tools that can meet any business need, even on a small budget. In this case, small business CRM tools offer actionable data rights and do not cost much.

14. Over 72% of organizations say big data initiatives are either profitable or costs have been paid.

(Capgemini)

Capgemini’s big data analytics statistics highlight that 27% of organizations globally say their big data initiatives are already generating profits. Almost half of them (45%) are at a break-even stage.

Be that as it may, at least 12% of organizations have incurred losses. Some 3% do not measure, whereas 12% said it was too early to say.

Nevertheless, 30% of organizations say they will accelerate the big data workloads and expand the operations to other departments in 2022.

At the same time:

• 16% say they will not make any changes to their existing big data projects.
• 11% plan to stay in the planning stage.
• 14% say they will build business cases while remaining in the proof of concept — evaluating benefits — stage.
• Some 13% of organizations want to modify big data usage to meet their business case.

Still, 12% say they will put the big data projects on hold for this year.

15. 37% of organizations say big data has improved decision-making capabilities across organizations. Improved collaboration (34%) and productivity (33%) are the other biggest benefits companies realize after using big data technology.

(Capgemini)

Capgemini’s key big data statistics highlight that regardless of whether the data investments have been profitable to the organizations, at least 90% say they have realized some benefit.

Here are other benefits that organizations have realized by applying big data technology:

16. Profitability from data operations was 8x higher for companies that believed in big data and analytics potential even before investing.

(Capgemini)

Big data volume statistics from Capgemini highlight that profitability is dependent on the high level of buy-in from the executives.

The profitability from big data operations was 8x higher for companies that believed in big data and analytics potential.

That is to say, the confident organizations who perceived data as an opportunity made significant investments in other areas, like changing the organization structure, hiring the right people to execute the strategy, etc.

In contrast, companies whose executives didn’t perceive big data analysis as having potential benefits were less profitable (6% vs. 49% saw big data analysis as an opportunity for growth).

The good thing is almost 76% of organizations say their executives perceived big data and analytics as beneficial “to a great extent” or “some extent” during investment.

Still, 19% of organizations say their executives sensed big data and analytics as beneficial to a “little extent” at the time of investment. Also, at least 3% considered predictive analytics to not be beneficial at all during the investment.

Another factor that directly affects profitability from big data usage is data governance. For instance, 75% of organizations that say they were profitable also agree that their company is good with standardizing and improving processes (other technologies).

Moreover, 75% say their organization is good at improving data quality and understands data governance. (Poor data quality and lack of data literacy are primary factors why most companies do not benefit from big data analysis).

Big Data Statistics: The Problems with Big Data and Data Literacy

17. Only 32% of executives say they were able to create “measurable value” from big data.

(Accenture)

Where 90% of the organizations that utilize big data say they have realized some benefit, only 6% of organizations surveyed by Accenture are indexed as “mature” regarding deriving value from data.

Another shocking survey from Forester reports that the organizations utilize not all data they collected. At least 60% – the organizations never utilize 73% of the unstructured data stored. (The report is based on 9000 high-value organizations globally).

Moreover, due to a lack of data literacy, most employees do not want to deal with data generated at all.

• Some 75% of organizations say they feel overwhelmed when working with data.
• 36% say they will find an alternate way — that requires no data handling — to complete the task.

Additionally, 14% of employees say they avoid task related to data altogether.

18. 75% of C-level executives and senior managers trust their gut instead of following data-driven insights. 41% of junior managers do the same.

(Accenture)

Qlik’s Data Literacy Index found that companies are underpinned because they do not know how to manage unstructured data nor have data literacy.

Additionally, most organizations, from C-level to entry-level, are not data-driven. Note that 48% of employees — all levels — often do not follow the data-driven insights but rather make decisions based on gut feeling.

In fact, only 37% say they do not defer with the insights generated by their analytical tools.

Another key big data statistic from Qlik points out that increasing the data literacy by 3-5% will lead to higher enterprise value.

For context, the average valuation of the organizations sampled in the study is US$ 10.7 billion, so the increase in enterprise value estimated is US$ 320 – US$ 534 million.

Big Data Statistics: Challenges Faced While Actualizing Big Data

19. 46% of BI users say the software is not flexible enough; 31% say the software lacks key features.

(BI-Survey)

Looking at the business intelligence software concerns, 46% of users say the software is not flexible enough.

31% of users say the BI software lacks key features. At least 28% said the software is difficult to use, whereas 22% said the query time is too slow.

In response, at least 36% of BI vendors blame the poor data quality to be the reason why companies are not able to derive measurable value from the software. However, only 6% of companies believe they have poor data quality.

20. 57% of organizations hired consultants, 45% used contract employees, whereas 34% used vendor technology resources to implement big data within their organization.

(Accenture)

At least 95% of the companies surveyed by Accenture say they utilized one or more external help at the time of implementing big data.

However, the good thing is most organizations are addressing the challenges faced with big data.

• 54% have initiated internal technical training.
• 50% are running vendor-based workshops.
• 49% are conducting independent research.
• 33% are taking external technical training.

Moreover, organizations of all sizes plan to increase data science expertise by next year. Deloitte’s key big data statistics highlight that the US tech industry is looking for more analytical and data science jobs and fewer engineering talent.

Consultancy’s report on data statistics highlight that only 21% of the organizations have a COD (Chief Data Officer) in place. Back in 2015, only 6% of organizations worldwide had a Chief Data Officer.

Business unit or division head (23%), CEO (9%), CFO (18%), CIO (15%), and multiple executives (20%) are overseers in most organizations.

21. 51% of organizations say security is their main challenge with implementing big data operations.

(Accenture)

Lack of budget (47%), lack of talent to implement (41%), and lack of talent to manage data sets and analytics regularly are other challenges to identified by organizations.

Plus, integration with existing systems (35%) and being unfit for the technology (27%) are other challenges to implementing the technology, as stated by the organizations globally.

Moreover, most organizations do not have the technology to manage unstructured data as well as data creation. Deloitte’s big data stats state that 32% of organizations have no centralized data creation and predictive analytics approach.

At the same time, 23% of companies do not have the right technology and infrastructure for data mining and capturing enormous data sets.

22. Organizations are spending more on generating data (32% have invested more than US$ 1 million) than on analysis of internal data (only 26% have invested more than US$ 1 million).

(PWC)

PWC’s big data volume statistics indicate that organizations spend more on collecting data, cloud computing, and retrieving data than machine learning and using the data collected. Machine learning statistics highlight that 91.5% of leading businesses have ongoing investments in ML and AI.

In fact, Deloitte’s data statistics indicate that 49% are still using basic tools with limited analytics capacity.

Moreover, most companies (31%) report that their senior executives do not discuss data management, which is available in silos, and partly usable. Some 4% say they still have inferior quality of data that makes analysis difficult.

Nevertheless, 31% have replicated data and identified the key domains. Plus, 34% have integrated all the data and have created central databases.

23. 79% of US organizations say they are data-driven; however, 39% of domain experts do not know what “data-driven” means, and 6% of data experts feel the same.

(SigmaComputing)

And although 94% of data experts know what data-driven means, 45% are not confident enough about satisfying the requirements of domain experts.

However, more than 1 in 3 blame the domain experts saying they do not understand what can and cannot be accomplished with data.

• Data analysis takes time: at least 25% of domain experts have given up on getting an answer because data analysis took too long. (Some 55% of data experts say they usually need up to 1-4 weeks to get a request.)
• Lack of confidence among domain experts: 34% of domain experts admit they are not confident in their ability to understand data questions. and 30% admit they feel embarrassed when they ask the data teams. Equally important to note, 29% of data experts prevent exploring all that data, fearing “messing it up.”
• Data experts feel underappreciated: 79% of data teams say they spend almost half of their time with ad hoc requests. 55% say they receive up to four additional requests on every fulfilled request.

Nevertheless, the good news is — as SigmaComputing’s big data trends indicate — domain experts (64%) and data teams (79%) want to work more closely.

The Future is Not Near…It’s Now: Why These Big Data Statistics Are Important?

It is clear that across all industries, being data-driven is the trend. And while some organizations are still struggling with big data, almost all utilizing the technology have benefited on some level.

Moreover, today, all successful companies have one thing in common: they collect, analyze, and act on data.

One key takeaway from these big data statistics is that organizations that do not focus on financial profits through technology are the ones that benefit the most.

The mature organization understands that profits don’t come from adopting the technology itself; instead, it comes from improving the processes and turning in the profits via other channels.

So, instead of focusing on financial profits, businesses should focus on improving the processes and letting the other channels increase profits.

Big data or not — it isn’t even a question. As underlined earlier, 94% of organizations feel their big data implementation meets their needs. Plus, 92% are satisfied with the business outcomes. (Accenture).

However, managing and integrating the unstructured data, understanding that data analysis takes time, improving data literacy, adopting the right technology, as well as believing in the potential of data should be the priority of organizations.

Source

1. Accenture
2. PR Newswire
3. Statista
4. IBM
5. DataReportal
6. Facebook
7. Microstrategy
8. Strait Times
9. Dresner Advisory
10. IMA’s Technology Enablement
11. Dell
12. Easternperk
13. PRNewswire
14. Intelligence Automation reports
15. Statista
16. Dresner Advisory
17. Statista
18. Aberdeen
19. Dresner Advisory
20. McKinsey
21. McKinsey
22. Accenture
23. McKinsey
24. Capgemini
25. Capgemini
26. Capgemini
27. Accenture
28. Accenture
29. Qlik
30. BI-Survey
31. Deloitte
32. Accenture
33. Consultancy.co.uk
34. Accenture
35. Deloitte
36. PWC
37. Deloitte
38. SigmaComputing
39. Accenture

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