Information Systems and Big Data Analysis: A Comprehensive Report

This report provides an in-depth analysis of information systems and big data. It begins with an introduction to information systems and big data analytics, highlighting their importance and applications. The report then delves into the challenges associated with big data analytics, such as inaccurate insights, complex implementation, and the need for skilled professionals. It also explores various techniques used for analyzing big data, including A/B testing, data mining, machine learning, and natural language processing. Furthermore, the report examines the characteristics of big data, focusing on the 5Vs (Volume, Velocity, Variety, Veracity, and Value). The report concludes by illustrating how big data technology supports business functions, such as reducing costs, enhancing sales and revenues, improving pricing decisions, and providing a competitive advantage. The report cites relevant research to support its findings.

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INFORMATION SYSTEMS AND BIG DATA ANALYSIS
INTRODUCTION
Information system which is integrated by collection of components that are used for
gathering, keeping and processing the data that can further use for optimised output (Alotaibi,
2020). Big data used to be an amount of large data that cannot be analysed by using traditional
method, thus Big data analytics technology comes in practice that process and extract the large
data set to deliver best information.
Challenges of big data analytics and techniques that are present for analysing Big data
Big data analytics is complex approach for examining the large data to uncover useful information like
hidden patterns, market trends and so on (Dai and et.al 201). By using this analytical tool, it can ultimately fuel
the rapid and fast decision making, predicting of future result and overall improvised business intelligence. There
are some potential challenges associated with this are discussed below:
Analytics fails in providing better and timely insight:
It is mainly due to insufficiency of data when analytics is not able to generate enough data and new insights,
which generally encounters because of poor data arrangement and shortfall of data integration. Long data
response is also a problem where system is only created for batch processing then it causes challenge in
providing data insights in real time.
Inaccuracy in analytics:
When system depends on poor, unreliable and inaccurate data then it produces poor results. As well as when the
essential requirements for the system is omitted because of human intervention within testing, development
process then system defects can be found which impose barriers in processing data for better outcomes.
Use of data analytics is complex:
When there are no such workers who have knowledge in it then it will be difficult to extract useful information
from data (Belhadi and et.al 2019). Therefore, shortage of data scientist can be a challenge without them it is not
possible to analyse data.
Expensive maintenance:
Every system needed a maintenance for desirable output that costs more, new technologies are able to process the
large data volumes in faster and better way. Thus sooner analytics which is based on technologies can become
outdated and for its update requires a huge investment.
Techniques available for analysing big data
It has been found that there are range of different techniques are currently available for analysing the big
data are as follows:
A/B testing: This testing generally concern with comparison of control group by applying distinct test groups for
discerning what specific modification can be implemented to modify objective variable. In big data it tests the
huge figures (Grover and et.al 2018).
Data mining: It is one of the common tool that is used in big data analytics, it usually extracts the patterns from
collected data by implementing different statistical methods and machine learning in a database management.
Machine learning: Well-known an subset of artificial intelligence, it is commonly utilised for analysing the data.
It works along with computer system algorithm to deliver assumptions or predictions on the basis of data.
Natural language processing (NLP): It is subpart of AI, linguistics, this technique utilises the algorithms for
processing the human language (natural).
There are other techniques as well which are spatial analysis, predictive modelling that can also be used
for analysing huge data sets, these also manage the data and reveals the business host and market trends.
Big data and characteristics of Big data
Big data is the data that comprises of huge varieties, that arrives in massive volume
with higher velocity. More simply it is larger and complex data which cannot be deal with
traditional tools. Where this data is being collected by the organisation that presents the
information in semi structured or in unstructured manner that needs to be analysed in order
to support decision making process and other business process (Abkenar and et.al 2021).
Big data is generally used for enhancing operational operability, helps in providing better
consumer experience with personalised marketing. These data help in providing better
insights that further utilised in making informed decisions. Whereas Big data analytics
technology is being used for processing big data that ranges in size from terabytes to
zettabytes. Therefore, big data is important or essential, for its analysation Software utility
that is Big data technology is required, it extract the information to arrange data in order
and helps in handling real time large amount of data for further processes.
Characteristics of big data
 There are 5v’s of big data that are (“Velocity”, “value”,” Volume”,” Veracity”,”
variety”), these five are innate characteristics of the big data. When data scientist
has knowledge in these five characteristics than it enables them to derive more
meaningful values from data while it also helps in making the form consumer
centric.
 Volume: It define as total amount of data that is present, it is consider as base of
big data. Simply when data is large in volume than it considers as big data, it
usually depicts the relation between size and processing efficiency.
 Velocity: This V of big data shows at what speed data is gathered and how rapidly
it moves. It is a temporarily value measurements of the data, where data is quickly
changing. Therefore, it is an important aspect for the organisation to review their
data as how quickly it is updating in order to make informed decision at right time.
 Veracity: This V attribute concern with accuracy and quality of collected data. As
sometimes there is a missing pieces and inaccuracy in data that needs to be
considered for delivering expected output (Wong, Zhou and Zhan, 2019). Hence
veracity is often understood as level of trust in stored data, and also defines quality
and insight of data.
 Value: Generally, value is provided by big data and completely linked with
organisation as what they can do with gathered data. To pull value from large data
is essential as value of data increases significantly when information is collected
from reliable sources. If data is valuable and reliable then more optimum results
can be reached.
 Variety: This V concern with diversity about data types. It discusses that different
variety of data is needed to be keep and processed, challenges in this associate with
standardization and arrangement of all data which is being collected.
Ways through which Big data technology supports business
Before big data technology data is been managed or analysed by usual
programming languages and other structured query language. However, these
approaches are not sufficient to manage large data, that is why big data technology
comes in practice to analyse and process unstructured, large, complex data. This
technology is broadly used in businesses to keep and manage their large data sets
for carrying out faster and better decision process. Particularly this technology
enables the businesses to profile their consumer in greater reach way. It makes
them to connect with real time and efficient conversation with customers, from
which businesses can easily provide solutions for audience query to make them
their potential consumer.
Reduces overall costs: Efficiency is main aspect for minimising the business cost,
with big data companies can easily receive the information that are required for
avoiding inaccuracy within their operational task (Sun, Sun and Strang, 2018). For
an example, data helps in providing details as consumer does not want to purchase
gift wrapping at checkout. Thus from this information firms can easily proceed to
remove this offers.
Enhances sales and revenues: Big data technology allows the businesses to
achieve in depth insight into the beliefs and shopping perceptions of the audience.
Thus through which they can easily tailor down their offering in order to meet
consumer requirements. For example, in tourism sector firms can easily analyses
the right time when there are more travellers and hence it ultimately raises the
demand for tourist spot.
Improvises pricing decisions: One of the major advantage from this technology is
its helps in making informative and better decisions. As pricing over the products
decides whether the product is succeeding or not. This technology enables the firm
to assess the finances of business and assists in figuring out the pricing for products
by comparing it to rivalries. For example, by using this business can easily lower
and raise the price of product based on market demand.
Provides competitive advantage: Big data technology opens up the greater
opportunities for business which helps them to concentrates on audience
preferences. Data tools basically analyses the local market and provides the better
insight of buying behaviour of consumer and their attitudes towards particular
product (Raguseo and Vitari, 2018). Once businesses get the idea about what their
consumer is looking forwards then they will be able to design products that meets
audience requirements. For example, consumer in the market is looking for
sustainable and tech savvy product, through this data business can easily work on
prototyping of data and on its designing.
CONCLUSION
From the above it has been concluded that having big
data technology in practice helps in extracting the large
data sets. Above has discussed the characteristics of big
data and some challenges linked with its analysis.
Furthermore, report have illustrated the ways through
which this technology cooperates with business functions.
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