Accounting for Management: Big Data and Analytics Methods Comparison

This report examines the evolution of data analytics in accounting, focusing on the impact of big data. It compares big data technologies, such as cloud computing and Hadoop, with earlier methods like MRP and business intelligence. The report highlights the four V's of big data (Volume, Velocity, Variety, and Value) and contrasts various analytical techniques, including descriptive, predictive, diagnostic, and prescriptive analyses. It traces the historical development of data management from early database systems to the emergence of big data in the 2000s. The report concludes by emphasizing the revolutionary changes in the analytics field due to massive data generation, the advantages of big data in cost reduction, decision-making, and product innovation, and the continued relevance of older methods in specific contexts.

Running head: ACCOUNTING FOR MANAGEMENT
Accounting for Management
Comparison between the Role of Big Data and Analytics Method Used in the Past
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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

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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)
The four big data techniques which aid business are –

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i. Descriptive analysis which provides real-time dashboard analysis (Declues, 2018).
ii. Predictive analysis which helps in forecast (Declues, 2018).
iii. Diagnostic analysis which suggests decision on the basis of past experience.
iv. 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.

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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

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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.

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References
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