Topic Name: DATA Analytics- Review of literature
Submitted by: Purti Sortee (FY MMS JDBIMS)
Abstract:
Big-data is currently a buzzword in both academia and industry, with the term being used to describe a broad domain of concepts, ranging from extracting data from outside sources, storing and managing it, to processing such data with analytical techniques and tools. This thesis work thus aims to provide a review of current big-data analytics concepts in an attempt to highlight big-data analytics’ importance to decision making. Many companies are using big-data analytics to analyse the massive quantities of data they have, with the results influencing their decision making. Many studies have shown the benefits of using big-data in various sectors, and in this thesis work, various big-data analytical techniques and tools are discussed to allow analysis of the application of big-data analytics in several different domains.
1. Introduction:
A big-data revolution is under way in health care. Start with vastly increased supply of information. Over the last decade, pharmaceutical companies have been aggregating years of research and development data into medical databases, while payors and providers have digitized their patient records. Meanwhile, the US federal government and other public stakeholders have been opening their vast stores of health-care knowledge, including data from clinical trials and information on patients covered under public insurance programs. In parallel, recent technical advances have made it easier to collect and analyze information from multiple sources—a major benefit in health care, since data for a single patient may come from various payors, hospitals, laboratories, and physician offices. Talk of a “big-data revolution” has become ubiquitous, suggesting the prominence and prevalence of imaginaries associated with this “next big thing” in the decades-old ethos that celebrates the wonders of information technology. As above quotation illustrates, the big-data revolution is supposed to be borne of apparently novel capacities to merge once discrete data sets, thereby creating vast stores of knowledge.
2. Objectives:
To extract information from data, the process of data and identify meaning in data so that information gained may be utilized to make better decisions.
To understand applications of Big Data analysis.
3. IoT-Enabled Big-data Analytics Architecture:
Babar, M., Mohammad et al (2021) proposes generic scheme to process data in parallel, distributed mechanism to overcome processing issues and intends explicit apprehension of multimedia communication to make efficient data-processing and decision-making. IoT with big-data is crux for multimedia data-computation. A pre-processing module along with proposed architecture is layered framework with parallel and distributed architecture using big multimedia data analytics to speed up processing mechanism of big-data. Specific datasets are utilized to realize proposed architecture to optimize data processing. Research shows how big-data analytics of multimedia data has released sphere of possibilities, opportunities in all industries.
4. Big-Data Management: A Driver for Digital Transformation?
Kostakis, P et al (2021) helps in better understanding how big-data management is used in existing literature by conducting quantitative text analysis with a qualitative literature review, considering that its meaning and usefulness vary across business sectors. Quantitative and qualitative text analysis led to identification of four main components of big-data management:
1. Data life-cycle processes
2. Technology
3. Information security
4. Business and human power.
In the era of big-data, businesses must learn true value, benefits of data. They must change strategies in order to elicit whole range of information and knowledge from processing big-data, so that to ensure viability and to gain competitive advantage under uncertain conditions. The paper’s contribution is as follows:
A. Expanding research on big-data management
B. Help in business strategy development, and on how to use big-data management practices to facilitate/ achieve digital transformation.
5. Applications:
i. An Intelligent Aviation Information Management System:
Aarthy, C. C., Narayanan et al (2021) investigates the relationship between big-data and growth of smart aviation industry. Paper offers suggestions, countermeasures for preparation and implementation of national aviation big-data platform, information system and development of global aviation big-data collaboration process and aviation big-data technology. Primary purpose of big-data in aviation management is to record and reflect status of aeronautical operations and operational efficiency. They work together resulting in aviation industry’s safety. Study concludes that aviation big-data plays critical role in growth of smart aviation industry and will significantly improve aircraft safety and efficiency.
ii. Big-data Analytics in Education:
Shidaganti, G. et al (2021) presented a framework that’s meant to carry out comprehensive operation that leads towards an effective analytical operation over educational data. The proposed system has emphasized over data transformation, data quality incorporation, and predictive analytics in educational data. Scripted in MATLAB, the study highlights that it is capable of better analytical operation in contrast to text mining approach, machine learning approach, and Hadoop, which are most used techniques in data analytics over educational domain. Our future work will towards optimizing performance more by exploring more approaches towards its implication on real-time applications.
iii. Controlling COVID-19 Pandemic:
Alsunaidi, S et al (2021) highlights contributions of several studies in COVID-19-based big-data analysis domain, Paper presents several applications used to manage and control pandemic providing taxonomy structure which classified potential applications of COVID-19 as: diagnosis, estimate or predict risk score, healthcare decision-making, and pharmaceutical. Paper suggests valuable future directions to be considered for further research, applications and introduces several data analysis tools and explained main features of each tool. It shows important insights on number of challenges that might hinder use of data analytics tools for COVID-19 and highlights number of future directions that should be considered in further research and applications.
iv. Analysing firm performance:
Gul, R. et al (2021) investigates impact of investment in data analytics on financial performance of banks in Pakistan. This study makes three key contributions:
1. Examining impact of data analytics on productivity and profitability through econometric methods.
2. Existing research highlighted that digital data itself might not create value for companies
3. It provides insight into an emerging economy, Pakistan, and shows how digitalization affects firm performance in financial sector.
The impact of DA investment on performance measures including ROA, ROE, net interest income is negative. Authors offers an insight into actual outcome of investment in DA which is highly relevant to policymakers, regulators to understand its significance and develop policies. Study offers the magnitude of productivity estimates, which will facilitate the decision makers, incumbents to conduct cost-benefit analysis before investing in DA.
v. Reshaping healthcare services
Schlicher et al (2021) discusses successful implementation of data analytics in AI enabled Mission Control at one of the largest healthcare service providers in the state of Washington. Many healthcare service providers are working on various data and analytics-enabled solutions to increase outcome of their health services. This study demonstrates tangible evidence from quantitative and qualitative analysis for ROI for large projects. Co-ordinating patient care with real-time data and being able to take speedy decisions bring so much value to the healthcare services.
6. Growth of Digitization and its Impact on Big-data Analytics:
Garg, A. et al (2021) Discusses importance of Data analytics in government/private sectors to analyse, mine required information. It helps Manager/ firms to take right decision on time. The economy, market, living standards are improving across the globe. An individual’s development could prove to be helpful in country development. The Government of India is taking many initiatives for deriving benefits of digitisation like The Prime Minister of India initiated National Digital Health Mission for health record of every person. The need of the hour is that everyone needs to adopt digitisation to make system fast, error-free, transparent. Increasing data poses new challenges, which needs to be promptly resolved for creating reliable systems.
7. Big-data and AI-based solutions.
Bakker, L et al (2021) provides recommendations on use of economic evaluations to support development decisions of analytics for big-data and artificial intelligence -based solutions. Many types of analytics can be developed within a specific clinical setting / disease or using a particular dataset. The framework presented in this study stimulates efficiency of development by selecting those applications worth further investment after assessing the feasibility of development and identifying critical barriers. For these applications, early economic evaluations can assist decision-making of analytics developers by estimating for instance requirements of effectiveness and the headroom for pricing, validation, and implementation
8. Protection of privacy and personal data in the big-data environment:
Denker, A. (2021) addresses here is how far smart cities go in collecting private data from inevitable public interactions. Author sought an answer to question of ownership and controlling the data produced and processed in such large quantities, which reflects ongoing concerns and uncertainties regarding private data protection. The Paper looks at issue of privacy and data protection, focusing on two problem areas.
1. The smart city’s dependence on smartphones – big-data – the cloud coalescence.
2. Cloud privatization and “big-data” ownership and reuse.
The insecurity and susceptibility of smart city systems is primarily due to absence of credibility of this coalescence. There is an obvious problem with the social leg of lack of alignment in the interpreting privacy standards and private data security. As a result, privacy in smart cities remains an enigma.
CONCLUSION:
The amount of big-data is already massive, but it is expected to grow exponentially as new technologies such as the more pervasive IoT devices, drones and wearables will jump into the fray. The availability of Big-data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. The convergence of these trends means that we have the capabilities required to analyse astonishing data sets quickly and cost-effectively for the first time in history.
These capabilities are neither theoretical nor trivial. 90% of big-data in the world today has been generated in last two years, and the recent advancements in deep learning are playing a key role in helping businesses decrypt this precious goldmine of information. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue, and profitability.
The Age of Big-data is here, and these are truly revolutionary times if both business and technology professionals continue to work together and deliver on the promise. Big-data and business analytics solutions are now a mainstream technology, and together with AI and automation, they represent the foundation upon which the digital transformation process is built.
References:
Aarthy, C. C., Narayanan, M. K. B., Kumar, G. R., Jayasundaram, J., Saikrishna, S., & Kumar, C. R. (2021). Big-data analytics and an intelligent aviation information management system. Turkish Journal of Computer and Mathematics Education, 12(11), 4328-4340. Retrieved from https://www.proquest.com/scholarly-journals/big-data-analytics-intelligent-aviation/docview/2639728830/se-2
Alsunaidi, S. J., Almuhaideb, A. M., Ibrahim, N. M., Shaikh, F. S., Alqudaihi, K. S., Alhaidari, F. A., . . . Alshahrani, M. S. (2021). Applications of big-data analytics to control COVID-19 pandemic. Sensors, 21(7), 2282. doi:http://dx.doi.org/10.3390/s21072282
Babar, M., Mohammad, D. A., Tariq, M. U., Ullah, F., Khan, A., Uddin, M. I., & Almasoud, A. S. (2021). IoT-enabled big-data analytics architecture for multimedia data communications. Wireless Communications & Mobile Computing (Online), 2021 doi:http://dx.doi.org/10.1155/2021/5283309
Bakker, L, Aarts, J., Carin Uyl-de Groot, & Redekop, K. (2021). How can we discover the most valuable types of big-data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. BMC Medical Informatics and Decision Making, 21, 1-12. doi:http://dx.doi.org/10.1186/s12911-021-01682-9
Denker, A. (2021). Protection Of Privacy and Personal Data in The Big-data Environment of Smart Cities. Gottingen: Copernicus GmbH. doi:http://dx.doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-181-2021
Garg, A., Popli, R., & Sarao, B. S. (2021). Growth of digitization and its impact on big-data analytics. IOP Conference Series. Materials Science and Engineering, 1022(1) doi:http://dx.doi.org/10.1088/1757-899X/1022/1/012083
Gul, R., & Ellahi, N. (2021). The nexus between data analytics and firm performance. Cogent Business & Management, 8(1) doi:http://dx.doi.org/10.1080/23311975.2021.1923360
Kostakis, P., & Kargas, A. (2021). Big-data management: A driver for digital transformation? Information, 12(10), 411. doi:http://dx.doi.org/10.3390/info12100411
Shidaganti, G., & Prakash, S. (2021). A comprehensive framework for big-data analytics in education. International Journal of Advanced Computer Science and Applications, 12(9) doi:http://dx.doi.org/10.14569/IJACSA.2021.0120926
Schlicher, Jessica,M.D., M.B.A., Metsker, Matthew T, PA-C, MPAS, MHA,F.H.M., C.M.P.E., Shah, H., M.S., & Demirkan, H., PhD. (2021). FROM NASA TO HEALTHCARE: REAL-TIME DATA ANALYTICS (MISSION CONTROL) IS RESHAPING HEALTHCARE SERVICES. Perspectives in Health Information Management, 1-9. Retrieved from https://www.proquest.com/scholarly-journals/nasa-healthcare-real-time-data-analytics-mission/docview/2603245865/se-2