Big data is the latest buzzword in modern technology world and has already become an integral aspect of modern businesses. The report provides a comprehensive overview of big data along with its characteristics that define big data. In addition to this, the report has also highlighted various important challenges related to big data analytics including lack of technical knowledge, requirement for complex infrastructure, quality of analytics, and most importantly security and privacy challenges. Along with this, various important applications of big data analytics has also been provided in this report.
Big data is a technical term for defining data that are huge in volume. The term has been specifically coined to reflect huge amount of data generated on a daily basis from a variety of sources thanks to mainstream adoption of technology and digital services. In fact this data is so huge in volume that traditional data storage facilities are not capable enough of handling this data (Zakir, Seymour and Berg 2015). Not only volume, this data are also significantly complex, and on top of that data comes in variety of types, which may be structured or unstructured depending on type of data being generated. There are certain characteristics that describe big data and to study big data identifying these characteristics is important. There are five important characteristics that constitutes big data and these characteristics are as follow (Rajaraman 2016):
- Volume: The most important characteristics of big data is its volume. Although it is not enough to consider a dataset as big data just because it is huge in volume, but this is a primary criteria for labelling any dataset as big data. Although big data as its name suggest is inherently large in volume, but it cannot be specified by any number that justify how large a dataset has to be for considering it as big data. However, the dataset has to be sufficiently large enough so that it is possible to draw certain conclusions regarding any trend or pattern related to something.
- Velocity: Apart from volume, velocity is an important characteristic of big data which defines frequency at which data is generated. The way digital services are being adopted by consumers, it leads to generation of data at a speed that is overwhelming and requires dedicated infrastructure to handle this data. Therefore, for big data velocity of data generation is significantly higher than traditional data sets.
- Variety: Big data consists of data of various types which are broadly classified into structured and unstructured data. While structured data are well organized and easy to analyse, unstructured data requires special tools and techniques for analyzing data.
- Veracity: As big data is not only huge in volume and velocity, it comes from a variety of sources. Therefore, it is possible that data is distorted before processing and there might be variation in representation of data. Therefore, big data may have issues like distortion, inconsistency, noise, and bias in data which if not properly managed may affect quality of data analysis and decisions obtained from data analysis.
- Value: As big data helps in uncovering certain trends or patterns within datasets, this is of huge value to organizations and businesses and helps in making informed decisions. Therefore, any dataset to be considered as big data needs to offer values in terms of decision making and business applications.
Big data analytics is not only relevant but of immense value in modern data analytics context. Organizations are actively adopting big data analytics as part of their business solutions realizing its potential and values that offer (Aggarwal 2015). However, there are certain challenges regarding big data analytics and some important challenges for adopting big data analytics are as follow (Muharemagic 2015):
- Lack of knowledge of data analytics: As big data analytics involves working with a huge volume of data that are significantly complex, it requires highly sophisticated and advanced technical knowledge. This is one of the most important challenges of big data analytics.
- Quality of analysis: Big data analytics is of no value if data on which analytics tools are applied contains errors or if data is of poor quality. As big data is huge in volume and velocity and data is generated from a variety of sources, it is possible that data is distorted before processing. If data is not properly sourced or prepared before analysis, there might be variation in representation of data, leading to issues like distortion, inconsistency, noise, and bias in data. If these issues are not properly managed, then it may affect quality of data analysis and decisions obtained from data analysis.
- Availability of required infrastructure: Big data is so huge in volume that traditional data storage facilities are not capable enough of handling this data. Not only volume, this data are also significantly complex, and on top of that data comes in variety of types, which may be structured or unstructured depending on type of data being generated. Therefore, it is important to ensure that proper infrastructure is available for supporting big data analytics which is an significant challenge considering amount of investment required for acquiring complex hardware and infrastructure such as data storage, servers, processing power and other required computational resources for analysis of big data.
- Ethical challenges: The primary ethical challenge of big data comes from its privacy concerns. As big data consists of various type of data and collected from variety of sources, ensuring security and privacy of data is a significant challenge, especially if datasets contain confidential and personal information. If data security is compromised, it may allow others to access these data and incorporate those data into applications for which data was not collected. Therefore, it is violation of privacy and also an ethical challenge.
Some popular techniques for big data analysis are as follow (Furht and Villanustre 2016):
- Association rule learning: Association rule learning helps to discover important correlations between variables contained in large databases. It analyses trend related to one variable and then based on that it try to predict trends related to other variables having significant correlation.
- Classification tree analysis: This is a popular technique for analyzing large data set and helps to categorize observations. A decision tree is constructed for analyzing observations and then decisions about those observations are obtained accordingly.
- Machine learning: In machine learning a model is first trained with historic data and based on training data the model helps in identifying trends or patterns within datasets automatically. This is a popular technique for big data analytics.
- Statistical analysis: Statistical analysis generates statistics from data stored for analytical purpose and then analyses results for obtaining insights from data. Statistical analysis require statistical algorithms for models to analyse data and obtain insights from data analysis.
Big data technology has applications in variety of business areas and could support businesses by helping to improve its processes, making improved and improved decisions, and increase overall sales and profit for the organizations. Following are some ways that big data analytics could support businesses (Vassakis, Petrakis and Kopanakis 2018):
- Big data technology in sales and marketing: Big data analytics if applied right has potential to increase sales and revenue for the businesses. For example, organizations could collect data from their customers through consumer survey, their interaction with the organization through CRMS portals to identify their purchase behaviors for example which products they like, benefits they are looking for or any additional features they want and all of these data can be incorporated into creating effective marketing plan to target relevant customers and increase sales and revenue accordingly.
- Big data technology in inventory and supply chain management: Big data analytics can also be beneficial for inventory and supply chain management. For example, if an organization fails to keep their inventory up to date according to product demand, it will find it challenging to supply as per market requirements. However, keeping inventory full beyond customer demands will result in losses. Big data analytics by analyzing sales history can predict demand for a particular product and therefore, optimizing inventory becomes lot easier while ensuring supply demand balance, which organizations often fail for lack of insights into product demands and customer requirements.
- Big data technology in product design and development: Big data analytics can support smart and innovative product design that not only meet quality requirements, but also ensures customer satisfaction. For example, big data analytics can help in identifying market trends by analyzing market data regarding a category of product or services and insights obtained from this analysis can be helpful in research and development of product. This approach is highly beneficial while developing any new product from scratch while making it superior from existing products in the market.
- Big data technology in improving service quality: Organizations can collect feedback about their customers and analyse complaints and suggestions from customers by applying big data analytics tools to identify trends within data. Insights from this data will help to identify important issues that have been reported by customers and based on that organizations can improve their service quality and enhance customer satisfaction accordingly.
References
Aggarwal, C.C., 2015. Data mining: the textbook (Vol. 1). New York: springer.
Furht, B. and Villanustre, F., 2016. Big data technologies and applications.
Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic, E., 2015. Deep learning applications and challenges in big data analytics. Journal of big data, 2(1), pp.1-21.
Rajaraman, V., 2016. Big data analytics. Resonance, 21(8), pp.695-716.
Vassakis, K., Petrakis, E. and Kopanakis, I., 2018. Big data analytics: applications, prospects and challenges. In Mobile big data (pp. 3-20). Springer, Cham.
Zakir, J., Seymour, T. and Berg, K., 2015. Big Data Analytics. Issues in Information Systems, 16(2).
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