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There is a general perception that you cannot manage what you don’t measure.


Because of big data, managers can measure and therefore know more about their businesses and transform that knowledge into improved decision making and performance. In this context big data play an important role. 

What is the role of big data compared with the analytics that were used in the past? 

Key Differences between Big Data Analytics and Previously Used Analytics Techniques

Big data suggests data of numerous observations, high velocity, and huge variety. Big data analytics is a process that deals with the analysis of very large amount of data. This paper describes the role of big data analytics and the improvisations that have been brought in the analytics due to the introduction of big data. This article also discusses on the key differences of the role of big data and the previously used analytics techniques in the analytics field. It proceeds through the comparison of big data technologies cloud based analytics and Hadoop with the technologies that were used before the invention of big data in 2005 (Datafloq.com 2018).

Figure 1: Big data technology map

Source: (Hu et al. 2014)

To meet the pace of the data growth, big data is becoming an asset in the business, accounting, and management fields (Bhimani and Willcocks 2014). It was first introduced by Roger Mougalas from O’Reilly Media in the year 2005. The concept of big data has brought an evolution in accounting and management. The rate of increase of globally produced data is around 35% to 50% every year and without Big Data, it is almost impossible to analyse the significant acceleration and trends of those data. The use of cloud computing helps to store information with greater flexibility at a very low cost (Bhimani and Willcocks 2014).  Financial fraud modelling and distress modelling, quantitative modelling and stock market prediction are very useful techniques managed by big data. The implementation of big data provide cost advantages, accurate decision based analysis and meeting of customer’s needs. More than 51% of the corporate leaders prioritize big data highly. The retail banks, wealth management advisories, credit card companies, and insurance firms are dependent big data analytics to get the proper insight of the huge amount of multi-structured data obtained from multiple sources.. The following diagram represents the four V components associated with big data which are Value, Velocity, Variety, and Volume (Hu et al. 2014).

Figure 2: Representation of the 4Vs feature of big data

Source: (Hu et al. 2014)

  1. Descriptive analysis which provides real-time dashboard analysis (Declues, 2018).
  2. Predictive analysis which helps in forecast (Declues, 2018).
  3. Diagnostic analysis which suggests decision on the basis of past experience.
  4. Prescriptive analysis which offers recommendation on the basis of analysis.

The early ideas of big data started in the year 1965 with the plan to establish World’s first data center for storing more than 900 million confidential data on magnetic tape (World Economic Forum. 2018). Later, E.F. Codd introduced the concept of relational database in the year 1970 that provided a hierarchical structure of the database. In 1976, Material Requirement Planning (MRP) became very popular due to its speed up feature and efficiency in resolving business problems. Finally, big data was first introduced in 1989 in an article written by Erik Larson. Then the background of big data was built in 1999 in the article “Visually Exploring Gigabyte Datasets in Real Time” which quoted that computing is the purpose of making insight (Chen, Mao and Liu 2014). At the same time, the term “IoT” was taken into account as a mean of online communication with the increasing number of devices. In 2001, the renowned analyst Doug Laney explained the primary characteristics of big data in his paper on data management. With the release of Web 2.0 in 2005, a huge amount of data started generating and large amount of unstructured data needed to be managed (World Economic Forum 2018). Thus, Hadoop was created by Yahoo! as an open source framework to store and analyse big data. Gradually, the size of data started increasing at a huge rate with the appearance of many social networks and in 2009, according to a report “Big Data: The Next Frontier for Innovation, Competition”, many US organizations started working with data of more than 20 terabytes. This ultimately took the data scientists to the actual realization of big data in 2011 and, in the year 2014, big data analytics became a topmost priority in the field of analytics.

Big Data Technologies and Cloud Based Analytics

There are several technologies which are very close to the concept of big data. These fundamental technologies are Hadoop, cloud computing, and Internet of Things (IoT). However, cloud computing differs from big data in two particular aspects. Their core concepts are quite different though big data is dependent on cloud computing for better operation. Moreover, cloud computing involves IT architecture transformation and big data provides decision making (Hu et al. 2014). Again, Hadoop is a program that deals with the operation of the asset that is big data. On a specific note, Hadoop has been designed to handle big data with the help of its two core components – MapReduce and Hadoop Distributed File System (HDFS).

In the previous discussion, the highlighted technologies that had been used to manage data before the introduction of big data are MRP and business intelligence and IoT. All these technologies were used to manage small amount of data. Moreover, MRP was designed for handling inventory management system. It is known as the It helps in business data collection, data analysis and planning. However, there is lack of data accuracy and one needs clean records for MRP. In addition to this, MRP is very costly to implement, incomprehensive, and time consuming. It works the best only under certain circumstances (MRPEasy 2018).

On the other hand, IoT data was thought as a particular case of big data. IoT indicates trillions of physical devices that are connected through wireless networks and processors for sharing information. The IoT analytics is responsible for the improvement of supply chain management, employee empowerment, and products and services enhancement. The basic difference between big data analytics and IoT analytics is that IoT collects and compresses high volume and huge amount of machine generated data to perform various operations like detection of fraud and security breaching, data optimization, and ad bidding (Accenture.com 2018). In addition, IoT analytics is efficient in performing edge analytics and managing streaming data. On the other hand, big data analytics analyses very large amount of human generated data for revenue protection, predictive analysis, capacity management, and customer protection.

The term business intelligence came into the analysis field in the year 1958 and it helps to analyse big or small data to produce valuable business insights. Data collection, analysis and report generation using business intelligence tools facilitate in making important business decisions. The well-designed response delivery to very big business problems is a special feature of business intelligence where the big data may impose some tricky new questions without giving any clue to the analyst who is dealing with the big data. Moreover, business intelligence is particularly useful for improvised data visualization, system automation, and well-determined reporting using a single system for implementation and management. On the contrary, big data is useful in customer analytics, fraud detection, new services and products innovation, and operational analytics, maintaining data of high velocity and variety, as stated by a study conducted by DataMeer (Debortoli, Müller and Brocke 2014).

From the above discussion about the comparison of big data analytics and past techniques used in analytics, it can be concluded that there has been a revolutionary change in the analytics field due to enormous data generation and the need for their meaningful analysis, making the last decade the era of big data. The use of new and old scripting languages (Python, Hive, Spark, R and Pig) have made big data more structured and suitable for analysis. The impact of big data can be visualized from three perspectives – cost reduction, better decision making, and new products and services launching (Vaidhyanathan 2018). Big data offers more agile framework and risk handling assurance than previous analytics methods and tools. Despite of revolutionary analysis capability of the very large data, big data is not efficient in clusters segmentation problems which can be efficiently done by business intelligence tools.

References

A brief history of big data everyone should read. [online] Available at: https://www.weforum.org/agenda/2015/02/a-brief-history-of-big-data-everyone-should-read/ [Accessed 17 Jul. 2018]

Accenture.com. (2018). Internet of Things (IOT) Analytics | Accenture. [online] Available at: https://www.accenture.com/in-en/internet-of-things-analytics [Accessed 21 Jul. 2018].

Bhimani, A. and Willcocks, L., 2014. Digitisation,‘Big Data’and the transformation of accounting information. Accounting and Business Research, 44(4), pp.469-490.

Chen, M., Mao, S. and Liu, Y., 2014. Big data: A survey. Mobile networks and applications, 19(2), pp.171-209.

Datafloq.com. (2018). A Short History Of Big Data. [online] Available at: https://datafloq.com/read/big-data-history/239 [Accessed 17 Jul. 2018].

Debortoli, S., Müller, O. and vom Brocke, J., 2014. Comparing business intelligence and big data skills. Business & Information Systems Engineering, 6(5), pp.289-300.

Declues, J. (2018). Four Types of Big Data Analytics and Examples of Their Use. [online] Ingrammicroadvisor.com. Available at: https://www.ingrammicroadvisor.com/data-center/four-types-of-big-data-analytics-and-examples-of-their-use [Accessed 21 Jul. 2018].

Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, pp.652-687.

MRPEasy. (2018). Advantages and disadvantages of using MRP system. [online] Available at: https://manufacturing-software-blog.mrpeasy.com/2015/12/13/advantages-disadvantages-using-mrp-system/ [Accessed 21 Jul. 2018].

Vaidhyanathan, S. (2018). An Introduction to Big Data Analytics | Evolution Big Data Analytics. [online] Agira Technologies. Available at: https://www.agiratech.com/introduction-to-big-data-analytics/ [Accessed 21 Jul. 2018].

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