Big Data Analytics
Dr. Tamaro J. Green
Big Data Analytics is a field of computer science that combines both computing, statistical, data storage, and data processing tools for the analysis of big data. Big data analytics can also combine computing industries such as health care, retail, government, manufacturing, and media. The big data analytics market is expected to grow from US$37.34 Billion in 2018 to US$105.8 Billion in 2027.1 Big data analytics developed as tools and techniques became available for the analysis of big data. As big data storage capabilities increased hardware and software that could process and analyze big data has developed.2 3 Big data analytics emerged as a field where emerging data analysis and data processing tools have developed to address limitations in traditional data management systems.4 Some of the emerging technologies for big data analytics include parallel processing and distributed computing.5
Big data analytics combines with artificial intelligence and machine learning to improve wireless networks and network security.6 Data scientists can automate network diagnostics on big data analytics platforms to identify root causes and anomolies of network problems.6 Machine learning improves big data analytics platforms with natural language processing that enables classification, categorization, extraction, and annotation of text.7 Artificial intelligence and machine learning can facilitate the analysis of big data that is retrieved from sensors and devices in the Internet of Things.8 Reliable and responsible artificial intelligence can improve the capabilities of big data analytics platforms.9
Big Data Analytics
Big data analytics has facilitated software services such as security monitoring, log analysis, and cyber-security threat detection.10 Big data analytics has also proven resourceful for large scale analysis in health care and financial systems.11 In health care big data analytics provide tools for analyzing large datasets of electronic health records.12 For financial systems, big data analytics has been the foundation of e-commerce and financial trading systems.13
The history of big data analytics is associated with the development of big data and the fields of data science and machine learning.14 As big data grew from storage capacities of database management systems beyond traditional servers, big data analytics grew from the business intelligence and reporting systems of traditional database management systems.15 Cloud computing and software-as-a-service systems also enhanced processing resources for large scale analytics.16 Software-as-a-service platforms allow for big data analytics on servers or cloud systems.17 The analysis of big data in specialized fields has also advanced the development of big data analysis.18
Healthcare analytics analyzes large sets of medical field data.19 Examples of healthcare analytics systems include the analysis of electronic health records, genomic data, or disease surveillance data.20 Healthcare analytics systems can combine machine learning and artificial intelligence to automate analysis.21 Artificial intelligence and big data analytics have been combined for the tasks of infectious disease surveillance and for developing precision health care analytics systems.22 23 Big data analytics can also increase the capabilities of image analysis in radiology.24 Big data analytics has also played a role in climate modeling, bioinformatics, and smart health care systems.25
Big data analytics supports audits and accounting for financial systems.26 Financial institutions incorporate artificial intelligence and machine learning to analyze large sets of transaction data.27 Financial institutions also implement big data analytics to measure the performance of firms.28 Big data analytics offer text mining tools to analyze large data sets of corporate documents.29 Big data analytics platforms supply text mining tools such as sentiment analysis to develop models that combine data from social media sources to evaluate systemic risk of financial systems.30 Big data analytics platforms combine natural language processing and machine learning to assist financial regulators govern financial systems.31 Big data analytics platforms are also capable of identifying patterns in large financial transaction datasets for fraud detection.32 Accountants can automate routine tasks and offer more time with their clients with big data analytics.33
Financial technology companies build off big data analysis platforms to provide wealth management services.34 Financial technology, or fintech, companies can offer customers personalized services with the advantage of big data analysis platforms.35 Big data analytics platforms run artificial intelligence algorithms to provide risk management solutions.36 Some of the services that big data analytics provides fintech companies are the ability to deliver digital experiences for customers, offer new products quickly, and provide investment decision support.37 Big data analytics and artificial intelligence also facilitate new technologies such as algorithmic trading, cryptocurrency markets, block chain, peer to peer lending, and crowdfunding.38
Big data analytics has driven changes in analysis of labor markets and the digitization of government services.39 Econometrics and census data can be collected, analyzed, and reported with greater speed and efficiency due to the technologies and methodologies of big data analytics.40 Information discovery from social media and other spaces of interaction can also assist governments and labor organizations.41 Big data analytics platforms have the potential of reducing the workload of paralegals, lawyers, and judges.42 Big data analytics may also provide policy makers tools for measuring accountability for human rights violations.43 Big data analytics may support the development of intelligent transportation systems and smart cities.44 45 Data scientists may also provide decision makers with information to design supply chain logistics, plan procurement, or optimize inventory with big data analytics platforms.46
In education, big data analytics has a role in both instruction and administration for educational institutions.47 Big data analytics serves as a tool for educators to improve the quality of education and provide resources for intervention.48 Learning analytics and education data science describe the role of big data analytics in education systems.49 Big data analytics platforms can serve education administrators in the evaluation, formulation, analysis, and implementation of education policy.50 Educators can operate predictive analytics on big data analytics platforms to identify patterns in students and develop evidence based cycles of improvements.51 Big data analytics platforms are tools to launch innovative learning environments with a divergence of channels and resources.52 Educational data mining and social learning analytics allow big data analytics platforms to support innovative learning environment in more than tracking progress but to augment communication and collaboration.53 Big data analytics text mining facilitates the instruction of foreign languages and presents resources for remote language learning.54 Big data analytics supplies tools for self-regulated assessments in and data mining model analysis of massive online open courses.55
There are three types of big data analytic systems, descriptive, predictive, and prescriptive.56 Descriptive big data analytics allow statistical methods to apply to large data-sets.57 Data scientists utilize predictive big data analytics to forecast performance or value of large data sets.58 Prescriptive big data analytics produces models and simulations from large data sets for scenario planning and decision making.59
Data visualization dashboards present charts, graphs, and other display tools for big data analytics.60 Interactive data visualization dashboards provide user interface controls to manipulate data that is presented in the visualization tools.61 The design of big data dashboards supports the ability of viewers to identify critical results of big data analytics.62 Big data analytics dashboards also allow for decision makers to take actionable steps through the analysis of big data.63 Data scientists can develop pipelines to feed data visualization tools data from heterogeneous sources.64
Big data analytics has also served social science and media with data journalism.65 Open data and digital journalism have contributed to large data sets available for big data analytics in journalism.66 Big data analytics provides an intermediary resource for digital transformations in data journalism.67
The ability to analyze large data sets has afforded natural language processing engines the tools to perform text analysis on corpuses of manuscripts and social media.68 Machine learning capabilities and research into the social constructs of language contributed to big data analytics in natural language processing.69 Big data analytics allows for categorization of large data sets of unstructured data for natural language processing engines.70
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