The growth and evolution of technology have changed how people interact amongst themselves and their environment. Currently, applications connect billions of users and machine systems and allow them to share information, resulting in rapid data growth. The rise of big data has become a critical factor in businesses’ ability to achieve their goals, grow, and outsmart their competitors. Consequently, different technologies, methodologies, and systems have been created to gather, transform, process, and store big data (Wickramasinghe, 2021). This paper aims to discuss big data, its characteristics, challenges, techniques used in big and data analysis, and how big data can support business institutions.
Big data is a collection of large, hard-to-manage volumes of structured and unstructured data that flood enterprises on a routine basis (Arachchi, 2020). The data can be processed to provide insights that can be applied to improve business decision making. The main characteristics of big data are collectively classified as the 5V’s and are as follows;
Volume: It is the size or amount of data available for analysis. Businesses gather and store data from multiple sources, including smart devices, customer transactions, industrial machinery, and social media (Ishwarappa and Anuradha, 2015). For the collected and stored data to be recognized as big data, it must be enormous to exceed the standard data storage and processing techniques (Wickramasinghe, 2021).
Variety: It is the diversity of the available data types and comprises raw, unstructured and semi-structured data collected from smart devices, sensors, and social collaboration technologies (Sami and Sael, 2016).
Veracity: It refers to the truth and accuracy of the data, which usually influences the confidence of decision making by executives (Ahmed, Basha, Arumugam, and Patil, 2021). Since data is gathered from multiple unrelated sources, it contains biases, noise, abnormalities. Therefore, businesses need to connect and correlate relationships, hierarchies, and multiple data linkages to promote the quality and truthfulness of the data. The veracity of data is also associated with validity to define the correctness and accuracy for the intended use.
Velocity is the rate at which data is received, stored, managed, and retrieved. For example, velocity in big data context can refer to the specific number of social media posts or web searches per unit of time. With the evolution of the global network and the growth of the internet of things, data flow has increased at an unprecedented speed and must be handled appropriately promptly.
Value: it is the most essential characteristic of big data. It is associated with insight discovery and trend recognition, leading to more effective operational and managerial decision-making in businesses (Williams, 2016).
Other characteristics of big data include;
Variability: this is associated with the inconsistencies in the data that occur due to natural or artificial phenomena such as peaks, daily, seasonal, and event-triggered peaks. Variability creates complexity in data management, especially if the underlying data is unstructured.
Volatility is associated with the retention policy of data and defines the amount of time the data should be considered meaningful (Dean, 2014). When the retention period expires, the data is considered useless and can only be destroyed.
Visualization is the characteristic of big data that allows it to be more comprehensive and easier to understand and read.
The main challenges of big data analytics include;
Insufficient Understanding of big data: Despite the massive adoption of big data, some organizations have staff with little to zero knowledge of big data, its benefits, and the infrastructure required to facilitate data usage (Gaur, 2020). Consequently, the lack of clear Understanding puts any big data adoption project at risk, which may lead organizations to lose a lot of resources and impede their growth progress.
Confusion on the varieties of big data technologies: The growth in the acceptance rate and adoption of big data has led to the development of multiple techniques of big data analytics. As a result, confusion can arise about the varieties of the available big data technologies in the market if there is no clear view of what will satisfy business needs (Sharma, 2021).
Cost: Adoption of big data projects can be very costly. The use of big data in business requires procurement of new sets of supporting infrastructures, new hires of administrators and developers, and increased running costs incurred from the growth in electricity consumption, payment of the development, setup, configuration, and maintenance of big data software.
Challenges associated with data security and privacy: the technologies used in big data analytics are evolving and growing every day. However, the security aspects of these technologies are still neglected because of the thought that security will be provided at the application level (Bekker, 2018). Consequently, privacy breaches and data loss have been on the rise.
Challenges of Integrating data from multiple sources: There are many big data sources, including social media, sensor data, smart meters, customer logs, emails, and employee reports (Campos, 2022). The process involved in combining the massive amount of data collected for analysis and reporting can be too complex that a lot of omission and data losses might be experienced at this stage.
Data Growth and Storage Issues: large amounts of data are collected by businesses per unit of time. As time flies, the volumes of data collected keep growing, creating the challenge of data handling and storage.
The following techniques are currently available for the analysis of big data;
A/B testing: this is a technique that allows the comparison of control groups with different test groups to examine the effect of treatments or changes on the specified objective variable. Big data fits into this technique because it can test massive numbers.
Data mining: this is a technique used to extract patterns and insights from enormous data sets through the combination of methods from machine learning and statistics with database management.
Data fusion and integration: The techniques allow organizations to streamline data from different sources and solutions such as Apache pig, Apache Spark, Hadoop, MapReduce, and Amazon EMR (Pattnaik and Prasad, 2016. The insights origination from integrated data are more efficient and are more accurate than those from a single data source.
Machine learning: the technique uses computer algorithms to generate data assumptions and to provide predictions that are based on known properties learnt from the training datasets (Tripathy, Sooraj, and Mohanty, 2018). The technique is heavily used in filtering spam emails, learning customer preference, and determining the best content for prospective customer engagement.
Statistics: The technique is used to gather, organize, and interpret data from surveys and experiments.
Natural Language Processing (NLP): the technique is applied in big data analytics to analyze human language.
Other techniques used in big data analytics include predictive modelling, network analysis, association rule learning, and spatial analysis.
Big data can support business in the following ways.
Development of products: big data provides a promising avenue for businesses to collect and apply customer feedback in product development. Big data furnishes decision-makers with customers’ perceptions of products and services, enabling them to make needful changes (Farmer, 2022). For example, suppose data insights show that customers significantly prefer model A of a product over model B. In that case, a decision can be made to change model A into model B’s structure to drive the sales up.
Risk Assessment: big data allows business executives to assess the short-term and long-term risks that are likely to affect the business positively or negatively. For example, the application of predictive analytics technique allows analysts to forecast future sales and the factors that are very likely to influence the sales at that given time. As a result, the business is always up-to-date with the latest trends and developments in their line of duty.
Creation of New Revenue Streams: Apart from providing businesses with valuable insights about the market, non-personalized big data trends can also be monetized to other organizations within the same line of products or services to generate more revenue for the company (Simplilearn, 2012). For example, an organization dealing with stock trading can sell the stock trends generated from big data analysis to financial institutions.
Provision of competitive advantage: Through big data, business enterprises gain insights into consumer behaviors and market demands. As a result, businesses that have adopted big data are always ahead of their competitors because they have a solid idea of their target market. For example, a business that deals with sales of sports shoes and uses big data is always aware of the type of shoes that customers like and the season that they are highly marketable.
Improved decision making: business decisions based on appropriate data analysis are efficient and focused on achieving organizational goals. For example, manufacturing industries can use data insights to decide when manufacturing processes should be running and when they should be switched off to save on cost and increase the longevity of the industrial machinery.
References
Ahmed, S. T., Basha, S. M., Arumugam, S. R., and Patil, K. K. 2021. Big data analytics and cloud computing: A beginner’s guide. Milestone Research Publications.
Arachchi, T. K. 2020. Why big data? Retrieved from https://towardsdatascience.com/why-big-data-bf0d65933782
Bekker, A. 2018. 7 major big data challenges and ways to solve them. Retrieved from https://www.scnsoft.com/blog/big-data-challenges-and-their-solutions
Campos, L. 2022. 5 challenges associated with big data and how to solve them. Retrieved from https://blog.hubspot.com/website/big-data-challenges
Dean, J. 2014. Big data, data mining, and machine learning: Value creation for business leaders and practitioners. John Wiley & Sons.
Farmer, D. 2022. 8 benefits of using big data for businesses. Retrieved from https://www.techtarget.com/searchbusinessanalytics/feature/6-big-data-benefits-for-businesses
Gaur, C. 2020. Top 6 big data challenges and solutions to overcome. Retrieved from https://www.xenonstack.com/insights/big-data-challenges
Ishwarappa, and Anuradha, J. 2015. A brief introduction on big data 5Vs characteristics and Hadoop technology. Procedia Computer Science, 48, 319-324. doi:10.1016/j.procs.2015.04.188
Pattnaik, K., and Prasad, B. S. 2016. Introduction to big data analysis. Studies in Big Data, 1-20. doi:10.1007/978-3-319-27520-8_1
Sami, S., and Sael, N. 2016. Extract five categories CPIVW from the 9V’s characteristics of the big data. International Journal of Advanced Computer Science and Applications, 7(3). doi:10.14569/ijacsa.2016.070337
Sharma, R. 2021. Top 6 major challenges of big data & simple solutions to solve them. Retrieved from https://www.upgrad.com/blog/major-challenges-of-big-data/
Simplilearn. 2012. How big data can help you do wonders in your business [Updated]. Retrieved from https://www.simplilearn.com/how-big-data-can-help-do-wonders-in-business-rar398-article
Tripathy, B., Sooraj, T., and Mohanty, R. 2018. Big data techniques in social network analysis. Big Data Analytics, 47-67. doi:10.1201/9781315112626-3
Wickramasinghe, S. 2021. Big data vs analytics vs data science: What’s the difference? Retrieved from https://www.bmc.com/blogs/big-data-vs-analytics/
Williams, S. 2016. Business intelligence strategy and big data analytics: A general management perspective. Morgan Kaufmann.
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