Big data is a term that refers to the large and complex data sets that organizations must manage and analyze in order to make better decisions.

With the rise of the internet, social media, and the Internet of Things (IoT), big data is becoming increasingly important for businesses of all sizes and industries.

In this blog post, we will explore statistics on big data trends, adoption, market analysis, demographics, and more.

Let’s get started.

Key Big Data Statistics 2023 – MY Choice

  • The big data analytics market is set to reach $103 billion by 2023.
  • Poor data quality costs the US economy up to $3.1 trillion yearly.
  • In 2020, every person generated 1.7 megabytes in just a second.
  • Internet users generate about 2.5 quintillion bytes of data each day.
  • 95% of businesses cite the need to manage unstructured data as a problem for their business.
  • 97.2% of organizations are investing in big data and AI.
  • Using big data, Netflix saves $1 billion per year on customer retention.
  • Predictions estimate the world will generate 181 zettabytes of data by 2025.

Big Data Statistics

Worldwide revenues for BDA in 2019$189.1 billionIDC
Increase in BDA use from 2018 to 201912%IDC
Worldwide revenues for BDA by 2022$274.3 billionIDC
CAGR of BDA, 2018-202213.2%IDC
Big data market size by 2023$77 billionEntrepreneur, 2019
Value of big data analytics market by 2023$103 billionTechJury, 2021
Growth rate of big data market in 201920%TechJury, 2021
  1. Global big data analytics market is projected to reach US$ 234.6 billion by 2026, growing at a CAGR of 10.2% between 2022 to 2026.
  1. 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.

Big Data Statistics: Usage by Industry, Organization Size, and Function 2023

  1. 78% of very large organizations (10000+ employees), 48% of large organizations (1000+ employees), and 43% mid-size organizations (100+ employees) use big data.
  2. 59% of small organizations are seeking employees with data literacy skills.
  3. Telecommunication (87%), financial service (76%), and healthcare (60%) are the top three most active industries in big data usage.
  1. 76% of the global banking sector uses big data; at least 62% consider it “very critical” for their success.

Big Data Statistics: Technologies and Software 2023

  1. Business intelligence and analytics software applications market is forecast to reach US$ 18 billion by 2025, exhibiting a CAGR of 3.5%. 
  1. 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.

Big Data Statistics: Consumption of Big Data Analytics 2023

  1. 72% of organizations say big data analytics is “very important” or “quite important” to accomplish business goals.
  1. 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.

Big Data Statistics: The Benefits Realized by Applying Big Data Analytics 2023

  1. Organizations applying customer data analytics extensively are 3x more likely to generate above-average turnover growth than laggards (43% vs. 15%).
  2. 94% of organizations feel their big data implementation meets their needs. Plus, 92% are satisfied with the business outcomes.
  3. Successful companies (applying data extensively) are 23x more likely to acquire new customers than laggards.
  1. Over 72% of organizations say big data initiatives are either profitable or costs have been paid.
  1. 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.
  1. Profitability from data operations was 8x higher for companies that believed in big data and analytics potential even before investing.

Big Data Statistics: The Problems with Big Data and Data Literacy 2023

  1. Only 32% of executives say they were able to create “measurable value” from big data.
  2. 75% of C-level executives and senior managers trust their gut instead of following data-driven insights. 41% of junior managers do the same.

Big Data Statistics: Challenges Faced While Actualizing Big Data 2023

  1. 46% of BI users say the software is not flexible enough; 31% say the software lacks key features.
  2. 57% of organizations hired consultants, 45% used contract employees, whereas 34% used vendor technology resources to implement big data within their organization.
  3. 51% of organizations say security is their main challenge with implementing big data operations.
  4. 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).
  5. 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.

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.

Summary of Big Data Stats


  1. The volume of data being generated is increasing at an exponential rate, with an estimated 2.5 quintillion bytes of data being created every day.
  2. The use of big data analytics is becoming increasingly important for businesses, with 88% of organizations reporting that they use it for competitive advantage.
  3. The big data market is expected to grow at a CAGR of 11.9% from 2020 to 2025.
  4. Big data technologies such as Hadoop and Spark are becoming increasingly popular for data processing and analysis.


  1. The retail industry has the highest adoption rate of big data at 81%, followed by the healthcare industry at 78%.
  2. Small and medium-sized businesses are less likely to adopt big data than larger companies, with only 45% of SMBs currently using it.
  3. The most common reasons for adopting big data include improving customer insights (66%), optimizing operations (62%), and developing new products and services (60%).

Market Analysis

  1. The big data software market is dominated by major players such as IBM, Microsoft, and SAP.
  2. The cloud-based big data software segment is expected to witness the highest growth during the forecast period.
  3. The Asia-Pacific region is expected to grow at the highest CAGR during the forecast period, driven by the increasing adoption of big data in countries such as China and India.


  1. Big data is most commonly used by companies in the retail (81%), healthcare (78%), and manufacturing (76%) industries.
  2. The average age of companies using big data is 20 years, with the majority being established companies.
  3. The majority of companies using big data have annual revenues of over $50 million.
  4. Big Data Analytics Benefits Statistics
  5. BARC, reported that organizations are reaping the benefits of Big Data— 69% chance of better strategic decisions, 54% chance of enhanced operational process control, 52% for a better understanding of consumers as well as 47% for effective cost reduction. 
  6. The organizations that are reaping the benefits of Big Data reported an average 8% increase in revenues while there is a 10% reduction in costs. 
  7. While the majority of high rolling companies can see the benefits of big data when it comes to cost savings, only 4.8% of respondents consider saving their budget to be a major factor that drives investments. 
  8. According to Sigma Computing, the benefits of big data are clear to many companies, yet around 63% of employees say they can’t get insights from their solutions in the right timeframe. 
  9. A study by BARC indicates multiple benefits for using a big data initiative, including better strategic decision making (69%), improved control of operational processes (54%), and improved understandings of customers (52%). 
  10. Big Data Analytics Usage Statistics
  11. At the time, researchers dealing in the field believed that 22% was the maximum potential a company could draw from big data usage. 
  12. Big Data Analytics Market Statistics
  13. Fast forward to 2019, and the Big Data banking analytics market had hit $29.87 billion, which was set to grow at a CAGR of 12.97% between 2020. 
  14. According to statistics about Big Data in healthcare, the global Big Data healthcare analytics market was worth over $14.7 billion in 2018. 
  15. According to Wikibon, the big data analytics market is expected to reach $49 billion with a compounded annual growth rate of 11%. 
  16. The data market’s growth will be 7% from 2025 to 2027. 
  17. While exploring global data market growth forecast from Statista, we discovered that big data had the highest growth rate in 2012 (61%) and 2013 (60%). 
  18. While going through big data growth statistics, 2018 saw big data market growth of 20%, and in 2019, the big data market grew by 17%. 
  19. In 2020, the big data market grew by 14%. 
  20. According to Big Data statistics, the Big Data market is currently worth $138.9 billion. 
  21. The Big Data and business analytics revenue report from Statista showed the forecast of the Big Data market that it will grow to US$274.3 billion by 2023 with a five year CAGR of 13.2%. 
  22. Statista, forecasted that the Big Data market segment will grow up to US$103 billion by 2027 with a share of 45% from the software segment. 
  23. Big data statistics from 2018 reveal the size of the global big data and analytics market, which is forecast to grow at a compound annual growth rate of 13.2% to a staggering $274.3 billion by 2023. 
  24. While it’s estimated that the overall cash value of the market will reach $103 billion in the next three years, this figure could double by 2027. 
  25. According to big data growth statistics, the market in 2019 was expected to grow by 20% compared to 2018. 
  26. The largest share of this revenue comes from spending on services, which accounted for around 39% of the market in 2019. 
  27. Apart from banking, other industries that hold a major share of the market when it comes to big data analytics revenue are discrete manufacturing (11.7%), process manufacturing (8.7%), professional services (7.9%), and governments (7.1%). 
  28. In 2019, it’s estimated that the market was worth around $189 billion. 
  29. Percentage of 1519 year olds not in education, by labour market status “ 
  30. Big Data Analytics Software Statistics
  31. Statista, forecasted that the Big Data market segment will grow up to US$103 billion by 2027 with a share of 45% from the software segment. 
  32. In 2021, there is 24% in services, 16% in hardware, and 24% in software while there will be 33% in services, 24% in hardware as well as a whopping 46% in software use in 2027. 
  33. Big Data Analytics Adoption Statistics
  34. While the Banking sector generates the largest amount of data, it may come as a surprise that financial service companies have the lowest rate of A&BI adoption, coming in at 29%. 
  35. 73.4% of companies still report business adoption of Big Data and AI initiatives as a challenge. 
  36. Big Data Analytics Latest Statistics
  37. However, 90 percent of the data in the global datasphereis replicated data, while only 10 percent is unique data. 
  38. 95% of businesses cite the need to manage unstructured data as a problem for their business. 
  39. 97.2% of organizations are investing in big data and AI. 
  40. What’s more, 15% of all new Google searches have never been typed before!. 
  41. According to global statistics on Big Data technologies, on average, poor data quality costs businesses worldwide anywhere between $9.7 million and $14.2 million yearly. 
  42. 45% of businesses worldwide are running at least one of their Big Data workloads in the cloud. 
  43. Although the cloud houses 67% of enterprise infrastructure , only a small percentage of businesses are currently utilizing it for Big Data operations. 
  44. 80 90% of the data we generate today is unstructured. 
  45. As of 2013, a whopping 64% of the global financial sector had already incorporated Big Data as a part of their infrastructure. 
  46. By the end of 2019, it was already worth $22.6 billion and is expected to grow at a CAGR of around 20%. 
  47. According to big data stats, cyber scams have gone up 400% at the start of the pandemic. 
  48. Storage for this data will grow at a Compound Annual Growth Rate of 19.2% during the forecast period. 
  49. That’s a big change considering that users only stored 2% of the data in 2020. 
  50. Data interactions went up by 5000% between 2010 and 2020. 
  51. Big data stats show that the creation, capturing, copying, and consumption of data went up by a whopping 5000% between 2010 and 2020. 
  52. In 2012, only 0.5% of all data was analyzed. 
  53. According to IDC’s Digital Universe Study from 2012, only 0.5% of data is analyzed, while the percentage of tagged data is a bit higher at 3%. 
  54. According to big data statistics from IDC, in 2012 only 22% of all the data had the potential for analysis. 
  55. The same source said that by 2020, the percentage of useful data, i.e., the information that has the potential for analysis, would jump to 37%. 
  56. With the estimated amount of data we had in 2020 , we have to ask ourselves what’s our part in creating all that data. 
  57. According to the latest Digital report, internet users spent 6 hours and 42 minutes on the internet which clearly illustrates rapid big data growth. 
  58. Lastly, the same source discovered that out of the total time digital users spend online, 33% is reserved for social media. 
  59. Apart from social media, 16% of the time users spend online goes to online TV and streaming , and another 16% to music streaming. 
  60. Online press takes a 13% share of total online time, whereas the remaining 22% of the time is reserved for other online activities. 
  61. 62.5% of participants said their organization appointed a Chief Data Officer , which indicates a fivefold increase since 2012 (12%). 
  62. Additionally, a record number of organizations participating in the study have invested in big data and artificial intelligence initiatives at 97.2%. 
  63. The highest percentage of organizations (60.3%). 
  64. Nearly one third of participants (27%). 
  65. Lastly, only 12.7% of participants said their companies invested more than $500 million. 
  66. That will decrease even further, only growing by 7% by 2027. 
  67. According to RJMetrics, in 2015, there were between 11,400 and 19,400 data scientists worldwide. 
  68. As at the end of 2019, worldwide spending on Big Data was already worth $180 billion , and it was projected to grow at a CAGR of 13.2% between 2020 and 2023. 
  69. By 2024, it will increase by a Compound Annual Growth Rate of 26%. 
  70. Data science jobs will increase by around 28% by 2026. 
  71. The 2023 technology predictions show that jobs in the data science field will increase by nearly 30% by 2026. 
  72. Data is growing at a CAGR of 10.6%. 
  73. It also reported that the installed base of storage capacity will increase at a compound annual growth rate of 19.2% from 2020 to 2025. 
  74. Forbes, predicted that more than 150 zettabytes or 150 trillion gigabytes of real time data will need analysis by 2025. 
  75. Forbes found that over 95% of companies require some help to manage the multiple sets of unstructured data while 40% of companies claimed that they need to deal with Big Data more frequently. 
  76. StrategyMRC, predicted that the Hadoop and Big Data Market will experience substantial growth from US$17.1 billion in 2017 to US$99.31 billion in 2023 with a 28.5% CAGR. 
  77. According to Wikibon, the Big Data and analytics, and application database solutions are expected to grow from US$6.4 billion in 2017 to US$12 billion by 2027 with a 6% CAGR, within a span of ten years. 
  78. The result showed that 39% were not sure about the data driven culture in organizations, 46% admitted that the lack of domain expertise creates a challenge for delivering relevant data models. 
  79. According to ReedSmith, the outbreak of the coronavirus pandemic has increased the rate of Big Data breaches and cyberattacks like scams, phishing, and ransomware to above 400%. 
  80. By 2025, it’s estimated that the global datasphere will grow to 175 zettabytes. 
  81. By 2023, the big data industry will be worth an estimated $77 billion, which is roughly 70% of Bill Gates’ net worth. 
  82. By analyzing their 100 million subscribers, Netflix was able to influence 80% of content viewed by subscribers due to accurate data insights. 
  83. The amount of data generated each second in the financial industry will grow 700% in 2021. 
  84. Unstructured and semi structured data now make up an estimated 80% of data collected by enterprises. 
  85. 81.7% of companies have a mix of legacy and modern cloud technologies — highlighting the rapid transition to the cloud continues year over year. 
  86. Yet, 63% of employees report they cannot gather insights in their required timeframe. 
  87. Most companies only analyze 12% of the data they have. 
  88. You got it, that means 88% of data goes unanalyzed. 
  89. Almost half (48%). 
  90. 51% of business domain experts say there are no reporting bottlenecks, while only 6% of data and BI experts come to the same conclusion. 
  91. Only 26% of companies say they have achieved a data driven culture, leaving the other 73% of companies in the dust. 
  92. Argentina, comes in first with a 20.8% compound annual growth rate. 
  93. The desire to arm employees with insights has 62% of companies claiming self service business intelligence is essential in 2021. 
  94. According to statistics based on a survey conducted by Sigma, about 71% of business experts have a desire to improve their data literacy skills. 
  95. A 2011 McKinsey report predicted shortage of talent necessary for organizations to take advantage of big data. 
  96. Rather than Gigabytes and Terabytes, nowadays, the data produced are estimated by zettabytes, and are growing 40% every day. 
  97. The percentages of models selected among the 24 models by each of the three criteria are summarized in Table 1. 
  98. Emerson and Kane suggested that a data set would be considered large if it exceeds 20% of RAM on a given machine and massive if it exceeds 50%, in which case, even the simplest calculation would consume all the remaining RAM. 
  99. Under the current settings,RRE has a clear advantage in fitting with only 8% of the time used by the other two approaches. 
  100. By 2024, this figure is expected to effectively double at a CAGR of 13.3%. 
  101. In fact, according to statistics on big data, such companies are already thriving, reporting an average growth of more than 30% annually. 
  102. However, if we go further back, we’ll see that in 2012 and 2013 the growth rates were as high 61% and 60% respectively. 
  103. Although a slowdown is expected in the next few years, big data statistics suggest a 7% increase between 2025 and 2027. 
  104. Deduced from data volume growth, experts estimate that retail will take the biggest share between 2018 and 2023, at a CAGR of 13.5%, with banking landing coming in second at a CAGR of 13.2%. 
  105. In 2019, the revenue stemming from big data was estimated to be $9.6 billion. 
  106. At a 23.5% compound annual growth rate, this figure is expected to hit $22.49 billion in the period between 2019 and 2023. 
  107. Big data statistics from 2017 determined that 90% of online data in the world had been generated just two years earlier. 
  108. The top two issues are followed closely by customer/social analysis and predictive maintenance at 70%. 
  109. Big data statistics show that 97.2% of major worldwide organizations are focusing investments into big data and AI. 
  110. A study conducted over 60 Fortune 500 corporate giants concluded that as many as 62% of companies appoint a chief data officer , whose job is to run data analytics and statistics. 
  111. As for investments, around 60% answered that their big data analytics budget is under $50 million. 
  112. As for those companies that invest more, 27% confirmed their budget stretches between $50 and $500 million, with another 12.7% of those going beyond $500 million when it comes to big data statistical analysis. 
  113. In addition, 75% of the executives who took part in the survey agreed that fear of disruption from competitors also serves as a great driving force when it comes to motivation for big data investments. 
  114. A 2018 survey showed that 30% of global organizations were planning to implement uses of statistics in big data management sometime in 2019. 
  115. Another 12% said they had planned to do so in 2018. 
  116. According to statistics for big data based on a 700 participant survey, as few as 3% of the respondents reported they were capable of answering their customers’ requirements by using data analytics and statistics. 
  117. On the other hand, 21% said they make little use of their analysis. 
  118. The companies that took part in the 2012 survey pinpointed reasons why they were only capable of analyzing around 12% of the available data. 
  119. This study based on 330 participants also concluded that as many as 31% of respondents consider the issue to be “not very important.”. 
  120. However, 42% agree that big data security analytics will become an issue of great importance in the future. 
  121. The figure represents a 35% increase compared to 2015 when the last estimate regarding data science job openings was made. 
  122. Well, for one thing, this data helps the company direct preferences via a big data recommendation system that influences 80% of the content you are offered. 
  123. By using Big Data to compile lists of preferred students, the University has reduced its reliance on services such as the College Board and ACT by 40%. 
  124. has estimated that the industry is set to continue growing at a combined annual growth rate of 29.7%. 
  125. It’s estimated that around 2.5 quintillion bytes of data are generated per day. 
  126. According to a 2013 article on, “Moneyball has played a role in 15 of 30 teams getting into at least one postseason series—not a Wild Card Game, but a postseason series—the last three years. 
  127. Budget Officeestimated that extending theBush tax cutsof 2001 and 2003 for the 2011–2020 time period would add approximately $3.3 trillion to the national debt. 
  128. Watson, Kevin; Halperin, Israel; AguileraCastells, Joan; Iacono, Antonio Dello .”Table 3 Descriptive , inferential (95% CI). 
  129. 97.2% of organizations say they’re now investing in AI and big data. 
  130. Around 95% of companies says their inability to understand and manage unstructured data is holding them back. 
  131. Only around 26% of companies say they’ve achieved a data driven culture, despite this massive amount of data creation. 
  132. A 2020 report by MicroStrategy found that around 94% of companies believe that data and analytics will be essential to the growth of their company, and their digital transformation. 
  133. Sigma went on to reveal that around 39% of business domain experts don’t know what it means to be data driven. 
  134. And 76% of data experts say half of their time is spent making ad hoc reports for teams. 
  135. Forrester reports suggest that between 60 and 73% of all data is never used for analytical purposes. 
  136. Only around 3% of the companies studied in total weren’t having challenges accessing big data. 
  137. A Dresner Advisory services report found that 36% of companies see big data as crucial in their organizations. 
  138. Around 29% said that big data was very important, 20% it was important while 12% rated it as “somewhat important”. 
  139. This is a significant increase in data interest since 2018, when Dresner insights found only 60% of companies using data. 
  140. An IDC worldwide spending report on Big Data found that banking 14% and discrete manufacturing 12% were the two biggest channels for big data investment, followed by process manufacturing, professional services, and the government. 
  141. The industries mentioned above made up around 50% of global revenue generation for big data investments in 2018. 
  142. The revenues for big data analytics are expected to reach around $88 billion in 2018, with a growth of 13.2% on the horizon. 
  143. The company estimated that around $3.1 trillion is lost on poor data analytics each year. 
  144. These strategies were marked important by 80% of respondents. 
  145. Additionally, customer and social analysis, fraud detection, and predictive analysis were marked as crucial by more than 70% of the respondents. 
  146. This gives healthcare big data solutions a growth rate of around 19.1% CAGR. 
  147. Data driven companies are 23 times more likely to acquire customers. 
  148. McKinsey reports demonstrate that data driven organizations with insights into customers are 23 times more likely to collect new clients. 
  149. They’re also 6 times more likely to maintain the customers they gain. 
  150. Data driven companies are also 19 times more likely to achieve and maintain a profitable status, compared to companies which don’t use data. 
  151. Around 47% of customers also said that they had reduced process costs thanks to data analytics. 
  152. Accenture’s study into the success rates of big data found that almost 79% of people believe that companies who fail to embrace big data could risk bankruptcy. 
  153. The study also found that around 51% of companies strongly agree that big data will revolutionize the way they do business. 
  154. Additionally, 39% strongly agreed that big data will dramatically change their operations in the future. 
  155. Fortunate 1000 companies could earn more than $65 million in additional income if they only increased their data visibility by 10%. 
  156. According to senior analyst Richard Joyce, currently only around 0.5% of all data is properly analyzed. 
  157. McKinsey’s studies on Big Data as the next frontier for innovation and competition found that businesses embracing big data analytics could increase operating margins by up to 60%. 
  158. An additional McKinsey study found that exploiting data analytics in retail organizations could be enough to boost profits by more than 60%. 
  159. According to Org, there’s so much data available online today, that it would take around 181 million years to download everything. 
  160. New Vantage Partners revealed in 2019 that around 97.2% of companies are investing in both big data, and artificial intelligence. 
  161. Right now, around 71% of business experts already say they’d like to improve their data literacy skills, and the number of data focused jobs is growing all the time. 
  162. Big Data Processing And Distribution Systems Usage Statistics
  163. In a fully productive application, the processor usage periodically varies between 5% and 35% with a mean of 10%, which is by far an acceptable continuous computing load. 
  164. Big Data Processing And Distribution Systems Software Statistics
  165. In 2010, this industry was worth more than $100 billion and was growing at almost 10 percent a year about twice as fast as the software business as a whole. Developed economies increasingly use data. 
  166. Big Data Processing And Distribution Systems Latest Statistics
  167. Systems up until 2008 were 100% structured relational data. 
  168. This led to the framework of cognitive big data, which characterizes big data applications according to[193]. 
  169. According to the extensive data sources in smart grid as shown in Fig.1, the formats and dimensions of data are diverse in structure. 
  170. The potential risk problem and health condition can be predicted with the help of this PHM method. 
  171. Reference utilized a two stage clustering algorithm to classify customers according to their load curves. 
  172. According to the survey published by Northeast Group, LLC, the loss caused by electricity theft reached more than $89.3 billion in the world every year. 
  173. Dobre and Xhafa report that every day the world produces around 2.5 quintillion bytes of data , with 90% of these data generated in the world being unstructured. 
  174. As reported by Akerkar and Zicari , the broad challenges of BD can be grouped into three main categories, based on the data life cycle data, process and management challenges •. 
  175. According to Labrinidis and Jagadish , BDA refers to methods used to examine and attain intellect from the large datasets. 
  176. According to Delbufalo , there are four stages of database searching process. 
  177. Phase II.1 –A number of keywords were entered into the Scopus database following conditions 2, 3 and 4 in Section3.1. 
  178. This process resulted in 2360 publications, of which 433 were left as relevant after filtering according to the barring conditions. 
  179. In analysing the different articles reviewed in this SLR, the authors identified 7Vs – seven characteristics of data [volume → C = 90 (39.64% of 227 articles), variety → C = 59 (25.9%), veracity → C = 44 (19.4%). 
  180. C = 30 (13.2%). 
  181. → C = 18 (7.9%), visualization → C = 6 (2.6%). 
  182. → C = 4 (1.8%). 
  183. → C = 25 (11%) and data interpretation → C = 15 (6.6%). 
  184. According toLabrinidis and Jagadish developing and maintaining this extraction method is a continuous challenge. 
  185. → C = 4 (1.8%), and data ownership → C = 3 (1.3%). 
  186. According toKhan, Uddin, and Gupta the challenge here is to ensure not to cross the fine line between collecting and using BD and guaranteeing user privacy rights. 
  187. Using the keywords as stated in Section 1.2, initial search resulted in 2360 articles from 1996 until 2015 based on the number of subject areas including material sciences, energy, neuroscience, chemistry, etc. 
  188. As presented in Fig. 8, the largest number of publications were recorded for year 2015 (with C = 114, 50.2%), followed by year 2014 (with C = 63, 27.7%) and year 2013 (with C = 43, 18.9%). 
  189. This is followed by USA (C = 145, 18.35%), and then there is Australia (C = 51, 6.45%), UK (C = 49, 6.20%), and Korea (C = 37, 4.68%). 
  190. Whereas, from Belgium (with C = 1, 0.12%) to Italy (with C = 17, 2.15%). 
  191. 10 demonstrates that the vast majority of the publications are research papers (C = 159, 70.04%), followed by general review (with C = 27, 11.89%) and technical and conceptual papers (with C = 15, 6.60% and C = 9, 3.96%, respectively). 
  192. The findings suggest that although a total of 11 different types of research methods were recorded from our data analysis, the majority of studies were analytical in nature (C = 103, 45.37%). 
  193. This was then followed by articles that are either conceptual/descriptive or theoretical in nature (C = 64, 28.19%), and design research (C = 12, 5.28%). 
  194. With regard to the analytical methods (with C = 103, 45.37%). 
  195. The other categories with their associated counts and percentages are presented in Fig. 11. 
  196. The existing trend that data can be produced and stored more massively and cheaply is likely to maintain or even accelerate in the future . 
  197. There are 500, 500 covariance parameters to be estimated. 
  198. The classical model selection theory, according to [73], suggests to choose a parameter vector β that minimizes negative penalized quasi. 
  199. Earn a Degree Breakthrough pricing on 100% online degrees designed to fit into your life. 
  200. Breakthrough pricing on 100% online degrees designed to fit into your life. 
  201. According to Glassdoor , data engineers earn an average annual salary of $102,864, and data scientists earn an average annual salary of $113,309. 
  202. An Aberdeen survey saw organizations who implemented a Data Lake outperforming similar companies by 9% in organic revenue growth. 
  203. ESG research found 39% of respondents considering cloud as their primary deployment for analytics, 41% for data warehouses, and 43% for Spark. 
  204. ESG research found 43% of respondents considering cloud as their primary deployment for Spark. 
  205. You can lower your bill by committing to a set term, and saving up to 75% using Amazon EC2 Reserved Instances, or running your clusters on spare AWS compute capacity and saving up to 90% using EC2 Spot. 
  206. Hence, the currently available PMUs and other lowvoltage measurement devices only provide aggregated information, mostly according to the specifications of the standard for electromagnetic compatibility IEC 610004. 
  207. T is the evaluation period length, which is ten times the single waveform length for the evaluation according to the standard EN 61000430 for 50. 
  208. This is a restriction compared to the class A voltage range definition in the standard EN 610004 30, which requires 10% to 150% of the supply voltage. 
  209. Therefore, EDR complies with class S as the input range allows 10% to 121% of the supply voltage. 
  210. The voltage and current channel measurements show an error of ±0.05% in the processing unit, but due to using of Rogowski coils as the sensors, currents are captured with a higher uncertainty of 1%. 
  211. Using the DFT with a window length of 8,192, we could prove that the EDR is capable of determining harmonics up to the 30th with a maximum deviation of 5% and up to the 50th with a maximum deviation of 15%. 
  212. When increasing the sampling rate from 12.8 to 25 kHz experimentally, we could even reach a 5% maximum deviation up to the 50th harmonics, which would comply with the class. 
  213. The error rate for the total dataset was 3.16%. 

In conclusion, businesses that want to stay competitive in today’s fast-paced environment need to be aware of the latest big data trends and demographics.

By staying informed and utilizing the right tools and technologies, businesses can optimize their big data strategies and achieve their goals.

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