30 Eye-Opening Big Data Statistics for 2020: Patterns Are Everywhere
Updated: March 29,2022
We are constantly producing data – even our kitchen appliances are hooked up to the internet, sharing and storing mountains of data.
The amount of information being collected around the globe is literally too hefty to process. That’s where the notion of big data comes into play.
Simply put, big data consists of huge sets of raw data that are too complex for traditional data-processing software. Powerful computers and advanced software mine these data sets to reveal unsuspected patterns and trends. Big data is taking the world by storm and we’re here to present you the most fascinating Big Data statistics.
There are a lot of misconceptions about Big Data, and the biggest one is that it’s simple. At first glance, Big Data appears to be just another processing issue. Surely all data trends would eventually be revealed by conventional processing on a bigger computer. But that’s not how it works.
Advanced software is able to find correlations, patterns, causal relationships, and obscure links among data that conventional programs would never uncover. Given sufficiently large data sets, AI programs discover environmental risk factors for diseases, patterns in stock prices and traffic flow, predictive factors for currency exchange rates, and other valuable information.
The truth is, big data is not just big, but complex. The best way to understand big data analytics is through the concept of four V's:
- Volume: Big data is just big.
- Variety: Big data is highly varied and diverse.
- Velocity: Big data is growing at exponential speed.
- Veracity: The accuracy of big data can vary greatly.
Big data analysis deals with all four dimensions.
While it started out as an interesting but obscure field of study in computer science, today big data technology is being embraced by all kinds of companies due to its incredible potential for monetization.
In order to better understand the upcoming big data business trends, check out the following big data statistics to understand more about the future of big data.
Big Data General Statistics
1. By 2020, each person on earth will generate an average of about 1.7 MB of data per second.
(International Data Corp.)
Daily smartphone and computer usage means that the volume of data is expanding rapidly. The average user shares dozens of media links daily, and all of that has to be stored somewhere.
2. Worldwide, people are already generating 2.5 quintillion bytes of data each day.
There are plenty of ways for businesses to use this data to generate more profits. In recent years, marketers have begun developing data analytics tools that help them understand the market better. Simply put, data companies can always hear a melody in all the noise you’re producing online.
3. Advanced data analytics show that machine-generated data will grow to encompass more than 40% of internet data in 2020.
(International Data Corp.)
Machine-generated data is data produced by a computer without human input. Since apps and programs are becoming more complex, there will be an increasing need for advanced big data processing, even on a smartphone level.
4. Stored data will grow to 44 ZB by 2020.
Originally, data analytics companies proposed a much lower number of just 40 ZB. However, the last couple of years have seen the growth of IoT technologies. IoT is already growing in volume, with more and more home appliances connected to cloud computers. In fact, IoT statistics for 2022 state how IoT generated data will be widely used to cut costs in almost all industries.
5. Nearly 90% of all data has been created in the last two years.
Analytics show that data grows at a nearly exponential rate. The number comes as no surprise, especially if you take into account the growth of machine-generated data.
6. Data growth statistics show that more than two-thirds of data today is generated by individuals, not companies.
The average day of an internet user is unimaginable without social media. Millions of people are creating a huge mass of online data simply by sharing or commenting on news, posts, and articles. Needless to say, big data companies and advertising agencies have recognized the business potential of user-generated data.
7. Website data analysis shows that more than 570 new websites are created every day.
There’s content being generated every second of our lives - even now as you are reading this. Businesses and individuals are moving their operations online, and as a result, machine-generated data is rising almost exponentially.
Big Data Business
8. A recent big data analysis report from Dresner Advisory Services concludes that 53% of companies are adopting big data analytics.
It’s becoming impossible to ignore big data and its business impacts. Big data is proving to be quite profitable. Millions of users are generating new data points each day, and plenty of data analysis tools have been developed to make sense of all the information. Data analytics is invaluable when analyzing market trends and policies.
9. Unstructured data is a problem for 95% of businesses.
The vast majority of companies today don’t have the necessary expertise to deal with big data. Most of the time, big data solutions are outsourced to other companies. Experts say big data specialist will soon become one of the most sought-after professions.
10. Over 150 trillion gigabytes (150 zettabytes) will need analysis by 2025.
Companies are getting desperate for experts who possess data analysis skills. Big data is a lucrative business, regardless of what industry you’re in. Marketers can use a data analysis tool to get a better understanding of what customers want. Financial advisors use it to predict market fluctuations. Simply put, applications of big data are countless.
11. Statista’s big data statistics claim how, by 2023, the big data industry will be worth an estimated $77 billion.
With cloud storage on the rise and an increase in machine-generated data, the amount of data to analyze is also growing. Naturally, we can expect an increase in demand for business data analytics experts.
12. In the last year, the number of IT professionals using big data descriptive and predictive statistics grew from 40% to 60%.
Since there’s an increasing demand for big data analysis, more IT experts are shifting to big data. Practically every industry nowadays requires some form of big data analysis, and big data science education is becoming increasingly profitable.
13. The data analytics software market increased by 14% from 2018 to 2019.
According to big data trends, the most common usage for data analysis software in an average company is in sales, marketing, research and development, and workplace management. As companies grow bigger, they need precise big data analytics tools for all the data piling up.
14. Companies using big data experienced a profit increase of 8% in 2015.
Big data adoption is essential for the survival of any business. And there are many ways entrepreneurs can use big data and analytics. They can use it to predict products that might be popular, to cut operational risks, and even to influence customers’ buying behavior. And of course the essence of big data is that there are likely to be other applications that we are not aware of yet.
15. Data warehouse optimization is considered a critical or very important issue by 70% of companies.
More than two-thirds of companies identified data warehouse optimization as one of the most important big data applications. Just over half said they also consider customer/social analysis and predictive maintenance crucial for their big data platforms.
16. The most important big data software today is Spark, with more than 30% of companies using it to sort out big data.
A little more than 30% of respondents have said that they use Spark for big data sorting. Next in popularity come Yarn and MapReduce, with more than 20% of companies listing them as their favorite big data platform.
17. More than 70% of companies say Spark SQL is critical for analyzing big data projects.
Spark is the prime choice for companies that deal with big data. Nearly 30% of respondents say that Hive and HDFS are more suited for big data statistical analysis. The rest of the respondents mostly rely on Amazon S3.
18. Spark Machine Learning Library (MLIB) adoption is projected to grow 60% in the next 12 months.
The projections are even bolder when extrapolated over a longer time period. Respondents to a research study published by Forbes say MLib will completely dominate the big data machine learning statistics over the next 24 months.
19. According to Forbes statistics on big data, 79% of executives agree that companies will perish unless they embrace big data.
Moreover, 83% of company representatives say that they have decided to pursue big data projects in order to seize the competitive edge. The global market is changing rapidly, and naturally, all those who resist change will find themselves out of business.
20. Big cata and analytics market revenues for software and services are projected to increase from $42 billion in 2018 to $103 billion in 2027.
The big data industry is already attaining a compound annual growth rate of about 10.5%. We can only expect this number to grow in the next decade as the big data industry invades nearly all industry niches.
21. Entrepreneurs find big data technologies most valuable in cutting expenses (49.2%) and creating new avenues for innovation and disruption (44.3%).
One of the main objectives of a company is to decrease expenses while maximizing income. Big data modeling has proved to be essential in finding hidden costs and inefficient business ventures. According to Forbes, 69.4% of companies have started using big data to create a data-driven culture, with 27.9% already reporting positive results.
22. The value of Apache Hadoop is expected to grow from $17.1 billion in 2017 to $99.31 billion in 2022.
Hadoop has already attained a 28.5% compound annual growth rate. This MapReduce-based big data mining program is quietly conquering the world of big data, and it has the numbers to show it. The fast growth is projected to happen between 2021 and 2022 when the market is expected to jump $30 billion in value.
23. It would take an average user approximately three million years to download all the data that is currently on the internet.
(Institute of Physics)
A typical user browses the internet with an average download speed of 44 megabits per second. According to big data volume statistics, it would take that user approximately three million years to complete the download of today’s internet, not counting the data uploaded in the meantime.
24. Big data will entirely depend on automated analytics systems by 2020.
Even now, big data analytics statistics are pretty much unimaginable without software such as Hadoop and Spark. However, big data benefits businesses in many ways, including product-placement planning, cutting expenses, and more.
25. IBM reports on big data statistics say that only 23% of companies surveyed have an enterprise-wide big data strategy.
Most companies are pretty unprepared when it comes to the application of big data analytics for business. Fewer than one-quarter of businesses are using big data analytics, which can prove to be detrimental in the future.
Big Data Jobs Statistics
26. 59% of all data science and analytics job demand is in finance and insurance, professional services, and IT.
The growth of data analytics has brought a huge demand for data science jobs. Currently, the highest demand for data science experts is in finance and insurance (19%), followed by professional services (18%), and IT services (17%). The question remains whether or not other industries will pick up the pace and start recognizing the benefits of big data.
27. According to Forbes’ big data statistics, the number of data science jobs is projected to grow by 364,000 by the end of 2020.
The overall demand for data science experts is projected to grow approximately 39% and will reach 2,720,000 jobs. Similarly, there will be a 15% increase in job openings for data science specialists. However, if you were thinking of applying right away, note that 81% of companies require specialists with at least three to five years of experience. However, not all data science jobs are the same, and different experts deal with different types of data analytics.
28. The demand for data scientists and advanced analysts will grow by 28% by the end of 2020.
Data scientists are already hard to come by, as it often takes much longer to find qualified candidates compared to other jobs - even when employers are willing to pay premium salaries. An average data scientist’s salary is $8,736 above median bachelor's and graduate-level salaries.
29. According to Forbes’s statistics for big data jobs, 39% of data science jobs require a Ph.D. or master’s degree.
Obtaining a data science degree can be quite hard, namely because there are few structured data science study programs. An average data scientist usually comes from a science, math, statistics, or IT background. Further, data science is usually tied to graduate and postgraduate study programs.
30. The most difficult position to fill is the analytics manager position in professional services. It takes 53 days, on average, to fill that job.
With an average salary of $96,845, data scientists who fill that position are far from earning the highest salary. That honor goes to analytics managers working in the finance niche, earning $113,754. The lowest-paid data and statistics scientists are functional analysts who work in the professional services niche. They have an average salary of $69,135.
Frequently Asked Questions
There are a lot of misconceptions regarding the relation of statistics and big data. Big data is a scientific field that deals with data sets that are too large or too complex for traditional data processing software, and therefore, cannot be analyzed using traditional statistical methods. Big data and statistics shouldn’t be mixed up, as they are very different fields. Statistics is a subfield of math that has been around for a hundred years now and is more akin to data analysis.
Among all big data topics, the sheer quantity of big data is the most debated one. For some data specialists, big data is any type of data that is distributed across multiple systems. Other experts say that data should be considered big if it can’t be processed with simple data analytics tools. Either way, the field of big data is still developing, and its definition may need some time to crystalize.
The growth of big data is happening at an exponential rate, and the most recent big data news suggests that there’ll be approximately 44 ZB of data in 2020. As a result, there will be an increase in the big data growth rate, especially machine-generated data. Moreover, machine-generated data will grow to encompass 40% of all information on the internet.
The best examples of big data sources are social media, IoT, cloud computing, the internet, and databases. All five of these create big data the same way – through machine-generated data that piles up due to communication among various computers in the network.
Andreas Kaplan has defined the four most important characteristics of big data as the Four V’s:
In essence, big data is data that comes in huge sizes (volume), and it is generated faster than we can analyze it using traditional means (velocity). Big data is not homogenous (variety), and it consists of information of different value (veracity).
If we combined all the storage space from the four biggest online storage and service companies like Google, Amazon, and Microsoft, and looked at big data in terms of numbers, we would get a figure of 1.2 million terabytes. However, this figure is growing each second, and there are speculations that the volume of data will grow to 44 ZB by the end of 2020.
One exabyte is one quintillion bytes. That is one billion gigabytes. That means that one exabyte can store around 50 million Blu-ray movies or 250 billion MP3 songs.
According to a 2008 Google big data analysis, Google’s search engine was handling around 20 petabytes per day. However, a decade is a lot of time in terms of computing power development. If we apply Moore’s law, we can get an approximate figure of 160 petabytes per day.
According to Statista, the global data market will be worth $77 billion by the end of 2023. Companies that use big data analytics are already gaining momentum on their competition. Furthermore, since there are so many uses of big data, there will be soaring demand for data scientists and analytics specialists.
The internet is growing at a rate of 11 new users per second, or a million users each day. Currently, around 57% of people on earth use the internet.
Probably every aspect of a modern company can be improved using big data, and the importance of big data in business is unquestionable. Big data can be used in pretty much any industry. For example, healthcare big data statistics can help doctors and epidemiologists predict future epidemic patterns. Finance businesses can use big data to find the most lucrative investment. Big data stats can help both small businesses and large corporations manage their time and resources more effectively. In the end, Big Data statistics and analytics can help everyone make long-term wise decisions.
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With a degree in humanities and a knack for the history of tech, Jovan was always interested in how technology shapes both us as human beings and our social landscapes. When he isn't binging on news and trying to predict the latest tech fads, you may find him trapped within the covers of a generic 80s cyberpunk thriller.