Introduction
A vast amount of information is available to everyone on hand for making decisions in specified matters. Big data analyzes large and complex data when companies are facing difficulties in handling traditional techniques. With increase of such kind of data, taking information that is meaningful or valuable becomes difficult because data is changed every time according to requirements. So, Big data analytics are used for providing information insights to companies or individuals. It is an application of big data of advanced techniques of analytics.
After using analytics of big data, organizations develop their business by minimizing costs and enhancing customer centric services (Elgendy & Elragal, 2014). To help companies, analytics of big data uses various tools and methods of analytics that are applicable to big data. It organizes data from huge amounts and complex data in comprehend formats to find out risks, opportunities, and help companies in making decisions for development. This report aims to describe about benefits, applications, challenges and recommendations to effectively implement applications of analytics of big data.
Key Benefits of Big Data Analytics Technology
Big data analytics is the method of describing enhanced techniques of analysis that help process huge and complex data sets into meaningful data. Effective utilization of analytics of big data is helpful for firms by decrypting data that is hidden. Its providing comprehended data which is very useful for decision makers. For example, organization of Amazon takes insights related to priorities and feedback from customers by using effective analytics of big data. Its utilizes those insights and enhances production by producing sales that are three times higher than before sales(Ramadan, Shuqqo, Qtaishat, Asmar, & Salah, 2020). Big data analytics (BDA) provides good solution for companies because it has customized applications and proprietorship of information. Capabilities of BDA include maturity, development and founding stages. Benefits of BDA consist of making strategic decisions, efficiency of operation, enhancing experience of customers, competitive advantage and speeding up innovation.
Companies get competitive benefits using BDA to maintain trends in market, effectively giving responses regarding needs of customers. Its adapting to transform environments of business. Through analysis of data, we find ways to streamline operations, eliminate hindrances, and minimize waste of resources to improve operational efficiency. BDA understands and responds to customers’ preferences and behaviors to provide more tailored experiences, increasing customer loyalty and joy. It speeds up innovation by giving useful insights related to patterns of market and allows companies to know potentials and abilities for producing new services.
Various Application Areas of Big Data Analytics
Management of multimedia data is one of the applications of analytics of big data. Multimedia information consists of audio, text, images and sources of access to multimedia. People share huge amounts of content in unstructured formats, such as social networks and websites, through multimedia. For example, on Instagram, nearly 20 billion images are uploaded, and YouTubers share a lot of videos (Pouyanfar, Iyengar, Yang, chen, & shyu, 2018). So, all that data is considered as big data. To maintain organizations in good way, they must use analytics of big data which gives valuable comprehension of information. For computing data parallelly, visualizing, manipulating, understanding and managing data, BDA uses different tools such as big data analytics of IBM, multimedia and Oracle, Microsoft Azure etc.
Another application of analytics of big data is giving support in process of decision making. Decision makers need to get insights from complex data that is changing rapidly based on value, variety, high volume, veracity and velocity. For this, analytics of big data uses methodology of science that is framework of BDAD. The full form of BDAD is Big Data Analytics and Decisions and it helps in mapping analytics, architecture and tools of big data. Its ultimate goal is to support and enhance making decisions in companies by combining analytics of nig data into process of making decisions (Elgendy & Elragal, 2016).
Retail Sector Experiments
Consequently, retail sector experiments to check whether BDAD frameworks are helpful with help of analysis of visualization, regression, cluster, sentiment, correlation, association and algorithms of decision trees and text mining. Result shows that BDAD frameworks are very useful when analytics of big data are integrated with process of making decisions.
Healthcare is the other application of analytics of big data. BDA provides various technologies for management of healthcare to give precious information related to patient’s health and treatment. Healthcare system is very complex for having multiple stakeholders such as doctors, hospitals, decision makers, patients and companies of pharmaceutical (Batko & Slezak, 2022). Analytics of big data is helpful for knowing details of patients, management, clinical data, making decisions related to patient diagnoses and preventing spreading of diseases by realizing potential information and improving healthcare system efficiency.
Key Challenges in Big Data Analytics
Big data analytics face challenges if they use AI because sometimes such AI technologies have uncertainty like noisy, unstructured and incomplete data. Those uncertainties affect the entire process of analytics including organizing, analyzing and collecting big data. For suppose, it deals with imprecise and not clear information, techniques of machine learning and data mining facechallenges in producing optical results. Another challenge is reducing capacity of computational storage and capacity of storage. Large data that is unmanageable creates swift challenges for finding environments of company including storage, queries and strategies. When sources and types of data are increasing, practical challenges arise because they don’t contain proper details of velocity which helps in changing data into valuable data (Hariri, Fredericks, & Bowers, 2019).
Analytics of big data confront various challenges when four vs in big data are not exhaustive, and some obstacle occurs in specific areas including data cleaning, quality, high dimensionality, validation, data reduction and feature engineering, representation of data, data sampling, and data integration. In addition to this challenges arise in risks in security, costs, data accuracy, regulations of data privacy and complexity. Costs of maintaining and laying out analytics of big data is expensive. So, it is challenging for startups and small companies. New technologies and businesses are created on daily basis and also big data enhances its methods. So, companies face problems in selecting technology that is best for their company without creating new problems.
Recommendations to Successfully Implement Big Data Analytics Applications
Big data Analytics plays a vital role in companies gaining precious insights from huge and complex data. It includes the process of data analysis, collection, visualization and processing.Analytics of big data is all about data evaluation. Changing needs of business, determined by advanced technologies and useful in making decisions. Data processing is mainly focused on manual tasks which are automated such as management of inventory and processing of payroll. It uses advanced methods such as predictive analysis, machine learning and data mining to make insights from bulk amounts of data. Further, it also adopts technologies of IoT and cloud computing for cost effectively storing, analyzing and collecting data.
Analytics of big data are used in various industries such as manufacturing, healthcare and retail to enhance experiences of users, drive innovation and effectively improve operations. Big data analytics uses multiple tools to process, analyze and manage bulk datasets. Tools like Hadoop are helpful for processing vast datasets over computer clusters. Hadoop is a framework that utilizes simple models of programming widely used for processing and storing complex data. Companies must acquire best practices that offer them to effectively act, analyze and collect data. Additionally, give necessary programs of training to employees to know and command on their roles in data analysis (Ochuba, Amoo, Okafor, Akinrinola, & Usman, 2024). Furthermore, encourage staff to utilize data for informing their thoughts and take initiatives in data driven ownerships.
Arrange team like cross functional that consists of members from various departments to actively collaborate on data initiatives and projects. Improve open interacting culture, innovation and experiments. Establish a loop related to feedback for collecting stakeholder inputs and endlessly work on data practices hinged on feedback. So, by embracing these practices, companies improve the analytics of big data values and help for success of company.
Conclusion
Big data analytics is referred tools and methodologies that are used to comprehend information by collecting, deriving information and processing from data sets that have high velocity and volume. This information of dataset came from various sources including smart devices, social media and email. By using insights that are obtained from analytics of big data, organizations are able to optimize performance, predict outcomes that occur in future, make strategic decisions, improve innovation, growth and efficiency of company. So, its benefits for organizations are endless and prevent them from fraud to acquire a competitive edge among competitors by helping gain more customers.
Despite benefits, it also faced some challenges in costs, data accuracy, security, uncertainty in data, complexity and regulations of data privacy. In the era of abundant data, makers actively use big data analytics to uncover hidden insights and gain advantages in decision-making across various areas. So, it had capabilities to give advancement basis on humanitarian, technological and scientific levels.
References
Batko, K., & Slezak, A. (2022). The use of Big Data Analytics in healthcare. Journal of Big Data, 9(3), 1-24.
Elgendy, N. S., & Elragal, A. (2016). Big Data Analytics in Support of the Decision Making Process. Procedia Computer Science, 100, 1071-1084.
Elgendy, N., & Elragal, A. (2014). Big Data Analytics: A Literature Review Paper. Lecture Notes in Computer Science, 214-227.
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey,opportunities and challenges. Journal of Big Data, 6(44), 1-16.
Ochuba, N. A., Amoo, O. O., Okafor, E. S., Akinrinola, O., & Usman, F. O. (2024). Strategies for Leveraging Big Data and Analytics for Business Development: A Comprehensive Review Across Sectors. Computer Science and IT Research Journal, 5(3), 562-575.
Pouyanfar, S., Iyengar, S., Yang, Y., chen, d. S., & Shyu, M. l. (2018). Multimedia Big Data Analytics: A Survey. ACM Computing Surveys, 51(1), 1-34.
Ramadan, M., Shuqqo, H., Qtaishat, L., Asmar, H., & Salah, B. (2020). Sustainable Competitive Advantage Driven by Big Data Analytics and Innovation. Applied Sciences, 10(9), 1-14.
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