Big Data: Characteristics, Analytics Challenges, And Techniques Essay.

Big Data and its Characteristics

Big Data mainly includes managing complex and large Data sets that traditional processing software applications are inadequate to curate, capture, process, and manage data in an appropriate period. Thus, the following section is going to provide a brief evaluation of the concept and characteristics associated with Big Data. Other than this, challenges that are often found to be in Big Data analytics along with the techniques that are presently available for analysing Big Data will also be underlined. In addition to this, by considering suitable examples, the way Big Data technology can support Business Operations And Performance will be demonstrated briefly.

The concept of Big Data is found to be an amalgamation of unstructured, semi-structured, and structured data obtained by the business entities, which can be extracted for valuable information. This extracted information can further be utilised in predictive modelling, machine learning, and different advanced applications of analytics projects. As mentioned by Almeida (2018), the primary and integral purpose of Big Data is to maximise the speed within which products are launched across the target. It helps to minimise the number of resources and time required to obtain target audiences, and market adoptions that help to keep customers satisfied. On the other hand, it can be mentioned that in this dynamic business world, the incorporation of Big Data allows companies to determine the way they can utilise data in practice. Along with this, Big Data also helps to determine the specific type of data that an organisation needs to accomplish specific strategic goals.

As mentioned by Hussien (2020), volume, variety, velocity, veracity, and value are the key characteristics that are associated with Big Data.

Data is found to be important to determine the data value, and a specific Data set can be considered as Big Data or not is completely dependent on the data volume. Thus, volume is considered as one of the key characteristics that require to be considered to deal with the solution of Big Data.

Big Data is found to be represented by different types and data formats between unstructured, structured, and semi-structured data. In present times, there are different types of data sources such as PDFs, videos, photos, monitoring devices, audios, and others. All these unstructured data can be managed and analysed effectively with the help of Big Data analytics.

Velocity represents the specific speed with which data is being created in real-time. The primary characteristics of Big Data are to deliver required and relevant data quickly and accurately (Liu and Zhang, 2020).

Big Data Analytics Challenges and Techniques

Veracity signifies reliability of data and helps to manage as well as deal with a wide range of datasets efficiently

Value is considered as another key characteristic of Big Data and it helps to signify where a specific data set is reliable and valuable to analyse, process and store.

With the growing dependency on technology, huge amounts of data sets are being generated every second, and it becomes difficult for companies or any business entities to analyse, utilise, manage, and store those data accurately. As mentioned by Elshawi et al. (2018), every business entity has been struggling hard to incorporate suitable strategic ways to make huge data sets relevant and useful. Following are the commonly identifiable challenges associated with Big Data analytics.

Business organisations have been increasing rapidly and due to the excessive growth of business entities as well as companies, excessive data is being produced. For example, popular storage to store huge amounts of data such as data warehouses or data lakes is being utilised to store and share large amounts of structured and unstructured data sets in a native format (Cockcroft and Russell, 2018). However, challenges have been identified while data warehouses or data lakes try to amalgamate the inconsistent and unstructured data from different sources.

In the present dynamic business world, most of business entities are required to deal with massive data to accomplish their business goals or objectives. However, due to a lack of knowledge and understanding, business entities fail to incorporate the concept of Big Data in their operations (Al-Badi et al. 2018). For instance, in most of the cases, due to lack of support and training, employees fail to enhance their knowledge in the field of Big Data. This in turn, negatively influences upon generating business success by incorporating Big Data.

It is identified that companies often find it difficult to select the simplest and most suitable tool to store and analyse Big Data sets. Due to this, companies often undertake inappropriate decisions by selecting inappropriate Big Data tools (Gaur, 2020). This in turn, negatively affects upon the ongoing and future business performance.

Data analysis is considered as the systematic approach to examine data sets as well as draw conclusions regarding the information those data sets contain with the help of methods, software, and systems. Technologies related to data analytics are found to be utilised across the commercial and industrial-scale as Big Data helps to make informed and optimal business decisions. Different types of techniques that are being considered for analysing Big Data are:

Ways Big Data Technology Could Support Business

Data integration is found to involve the amalgamation of data associated with various sources and provide users with a specific unified view of those data. On the other hand, data fusion is considered as the process of collecting data from various sources, however, it is not emphasised upon producing useful, accurate, and consistent information as compared to those provided by the unified data sources (Gao et al. 2020). In this regard, it can be mentioned that consideration of both data integration and data fusion can be more effective in terms of providing insight into information in an accurate and efficient manner than in case produced through specific unified sources.

With the rapid emergence and acceptance of modernised technologies like Artificial Intelligence (AI), ML is being widely utilised as a technique for data analysis. ML helps companies to provide accurate predictions about their business needs, future opportunities, consumer demands, and others accurately that human analysts fail to provide.

Clustering is considered as one of the well-known unsupervised techniques and an important tool for Big Data analysis. This technique can be utilised in pre-processing steps to minimise data dimensionality before executing the learning algorithm (Saeed et al. 2020). It can also be utilised as a statistical tool for discovering useful patterns in specific data sets.

In the context of the present dynamic business environment, Big Data supports the business by providing effective tools for making smarter and optimal decisions that are mainly based upon real-time data instead of assumptions. Valuable insight and data are found to play a significant role for any business entity to accomplish strategic business goals (Ajah and Nweke, 2019). In most of the cases, it is identified that marketers fail to anticipate the needs of their target customers accurately and hinder their chance to keep customers satisfied by meeting their needs. In this context, Big Data plays a crucial role to accomplish organisational goals through accurate and efficient business prediction. Incorporation and utilisation of Big Data have become one of the crucial aspects for most of the leading companies to sustain their business competitiveness and growth.

For instance, Disney is found to leverage the usage of Big Data technologies to predict and analyse the behaviour of the target visitors across the theme park. This allows Disney to provide more efficient and customer-centric services that flourish their growth and market popularity (Marr, 2021). On the other hand, Walmart is found to utilise Data Mining techniques to analyse the pattern that could be utilised to offer product recommendations to the target customers. In addition to this, there are different renowned companies such as Netflix, Uber Eats, Amazon, Starbucks, and others that have already been utilising the advantages and features of Big Data to strengthen their business operations and competitiveness (Marr, 2021). In this regard, it can be mentioned that incorporation of Big Data has the potential to improve organisational performance along with future business propensity by enhancing overall operational efficiency. Thus, it can be mentioned that adoption and incorporation of Big Data is and will support business entities to make accurate business predictions based on which optimal business decisions can be made that flourish business growth by meeting the demands and execution of target customers.

Conclusion

In relation to the overall discussion, it can be articulated that Big Data can be considered as the combination of different tools and processes in relation to managing and utilising a large number of data sets. From the discussion, it has been identified that in this modern business environment, the incorporation of Big Data technology has improved overall operational efficiency and helps businesses to make optimal decisions. There are different types of identified Big Data challenges and techniques that need to be considered while incorporating Big Data in businesses. In addition to this, it has also been identified that different renowned global businesses such as Walmart, Amazon, Netflix, and others have already incorporated Big Data technologies that help them to strengthen market competitiveness and business growth.

References

Ajah, I.A. and Nweke, H.F., 2019. Big data and business analytics: Trends, platforms, success factors, and applications. Big Data and Cognitive Computing, 3(2), p.32.

Al-Badi, A., Tarhini, A. and Khan, A.I., 2018. Exploring big data governance frameworks. Procedia computer science, 141, pp.271-277.

Almeida, F., 2018. Big data: Concept, potentialities, and vulnerabilities. Emerging Science Journal, 2(1), pp.1-10.

Cockcroft, S. and Russell, M., 2018. Big data opportunities for accounting and finance practice and research. Australian Accounting Review, 28(3), pp.323-333.

Elshawi, R., Sakr, S., Talia, D. and Trunfio, P., 2018. Big data systems meet machine learning challenges: towards big data science as a service. Big data research, 14, pp.1-11.

Gao, J., Li, P., Chen, Z. and Zhang, J., 2020. A survey on deep learning for multimodal data fusion. Neural Computation, 32(5), pp.829-864.

Gaur, C., 2020. Top 6 Big Data Challenges and Solutions to Overcome [Online]. Available at: <https://www.xenonstack.com/insights/big-data-challenges> [Accessed on 28 February 2022]

Hussien, A.A., 2020. How many old and new big data v’s characteristics, processing technology, and applications (bd1). International Journal of Application or Innovation in Engineering & Management, 9(9), pp.15-27.

Liu, Z. and Zhang, A., 2020. Sampling for big data profiling: A survey. IEEE Access, 8, pp.72713-72726.

Marr, B., 2021. Data Strategy: How to Profit from a World of Big Data, Analytics, and Artificial Intelligence. London: Kogan Page Publishers.

Marr, B., 2021. How Does Big Data Help Companies?. [Online]. Available at: <https://bernardmarr.com/how-does-big-data-help-companies/> [Accessed on 28 February 2022]

Saeed, M.M., Al Aghbari, Z. and Alsharidah, M., 2020. Big data clustering techniques based on spark: a literature review. PeerJ Computer Science, 6, p.e321.