Information Systems and Big Data Analysis: Challenges and Support

This report provides a comprehensive overview of big data analysis within the context of information systems. It begins by defining big data and detailing its key characteristics, such as volume, variety, veracity, value, and velocity. The report then addresses the significant challenges associated with big data analytics, including a lack of skilled professionals, issues related to data growth, and the difficulties of integrating data from diverse sources. Various techniques for analyzing big data are also explored, emphasizing collaboration, question formulation, and the establishment of key performance indicators (KPIs). Furthermore, the report discusses how big data technology supports businesses, providing examples from sectors like healthcare, where data analysis enhances pharmaceutical outcomes and clinical trial predictions. The conclusion underscores the importance of information technology and big data for organizational success, highlighting the need for effective strategies to leverage data for growth and development.

Information Systems and
Big Data Analysis

Table of Contents
INTRODUCTION ..........................................................................................................................3
MAIN BODY ..................................................................................................................................3
What is big data and also discuss about its characteristics ........................................................3
Explain about the challenges of big data analytics and the techniques which are currently
available to analyse the big data ................................................................................................4
How big data technology support the business with support of examples wherever necessary
.....................................................................................................................................................5
CONCLUSION ...............................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Information system refers to the components set for storing, collecting and processing
data for providing knowledge, information and digital products. Within this technical world, big
data analysis is very important where it is the use of advanced analytic techniques which is
against very large and diverse data sets. It includes structured, semi structured and unstructured
data from different sources and in different sizes as well. Within this competitive environment ,
IT and analysis of big data is very important for further actions and steps as well. The present
report will cover discussion about the information for big data and characteristics as
well(Chistyakova and et.al., 2020). In addition to this, the report will cover analysis about the
challenges for big data analytics and the techniques which are currently available to analyse the
big data. Moreover, the report will cover discussion about the technology for big data which
supports the business with support of examples.
MAIN BODY
What is big data and also discuss about its characteristics
Big Data: It basically refers to the sets of data which are very difficult to dealt through
traditional data processing application software. It basically contain different forms which arrives
in enhancing volumes and with higher velocity as well. There are certain examples of big data
which include transaction processing system, customer databases, emails, documents, mobile
apps and social networks as well. Big data is also known as the three Vs which indicates about
variety, volume and velocity as well. There are certain characteristics of big data which has been
defined into the following manner:
 Volume: The word itself big data depicts that it has an enormous size and also contains
the large scale of volume as well. It has been generated from different sources such as
machines, business processes, social media platforms, human interactions and networks
as well.
 Variety: Big data can be collected from different sources and it can be structured, semi-
structured and unstructured as well(Faccia and et.al., 2019).It will only be collected from
from sheets and databases and in the current period of time the data will come in the
array forms which include PDFs, Audios, photos, videos etc.

 Veracity: It can be defined as the process of being able to manage and handle the data
into efficient manner. It means that how much the data is reliable and it has different
ways in order to filter or translate the data(Hassan Zadeh and et.al., 2019). Within the
business development, big data is very necessary in order to accomplish the tasks on the
daily basis.
 Value: It is basically the essential characteristic of big data and also the data which
cannot be processed or store as well. It is the reliable and valuable data that has been
processed, stored and analysed as well.
 Velocity: It basically creates the speed through which the data can be created on the real
time basis. It basically contains the linking of incoming data sets speeds, activity bursts
and rate of change as well.
These are the main characteristics of big data which needs to be understood into deeper terms.
Explain about the challenges of big data analytics and the techniques which are currently
available to analyse the big data
There are certain challenges of big data analytics which has been defined into the
following manner:
 Lack of knowledge professionals: For running these modern technologies, organisation
requires skilled data professionals. These professional basically include data analysts,
data scientists to work with the tools and make sense of the sets of data. For any
company, this is the biggest challenge where there is drag of lack of massive data
professionals.
 Issues of data growth: The most important challenge of massive data is storing the huge
sets of knowledge into proper terms(Ionescu., 2019). The quantity of knowledge which
has been stored in databases of companies and data centres which has been increasingly
into rapid terms. Over the period of time, these data sets grow exponentially into smarter
terms then it gets challenging to handle as well.
 Integrating data from the spread of sources: Within the corporation, data comes from
different sources such as customer logs, ERP applications, social media pages. The main
challenging task is related to combining all the data to organise the reports into proper
form.

There are certain techniques which can be used in order to analyse the data into the current form
which has been defined into the following manner:
 Collaborate the needs: Before initiating into any techniques so its very difficult to sit
into collaborative terms with all the main stakeholders within the organisation.
 Establish the questions: when the core objectives have been out lined then it is
important to consider which questions will need answering to help in terms of achieving
the mission(Kunanets., Vasiuta and Boiko., 2019). It is the most important technique
which help in terms of shaping the foundation of the success.
 Set the KPI s: When sources have been set and data has also been cleared then it is
necessary to set the host of key performance indicators which will help in tracking,
measure and shaping the progress into number of areas. KPI s are critical in terms of both
quantitative and qualitative analysis research. It is one of the primary method of data
analysis which should not be overlook.
These are the techniques which are used into the current form which is available in order to
analyse the data.
How big data technology support the business with support of examples wherever necessary
Big data technology plays a very significant role within the business where all the tools
and processes are related to managing and utilising the large sets of data. With support big data,
there are business organisations who can use and figure out the most valuable customers as well.
It can also support in terms of helping the business which creates services, new experiences and
products as well. It is not easier to use the big data into significant terms at the organisational
level which lead the companies to outperform the competition(Lee and Huh., 2019). There are
different industries, new entrants and also there are established companies who use the strategies
of data driven in order to capture, compete and innovate as well. There are different examples of
usage of big data which can be observed almost into every sector which includes from IT to
healthcare.

In context to healthcare sector, it has been observed that data pioneers have been
analysing the pharmaceuticals outcomes. It has been observed that companies have more focus
towards on discovering the benefits and risks which were not clear during the initial trials at
clinical level(Maass and et.al., 2018). It can also lead towards the better analysis of the trials and
also support in terms of predicting the outcomes. There are certain adopters who have use the
data from the sensors embedded into different products which ranges from industrial goods to
children's toys. It will support the company in terms of determining how products are used in this
real world and it becomes easier to create, design the new services and future products as well.
According to the research, it has been find out that big data can create lots of new opportunities
of growth which can give rise to the new businesses. For example, analysing and aggregating the
data of industry. At the organisational level, with support of data it is easier to have the ability to
estimate the metrics which include customer loyalty which was handled into the previous
manner. So from these examples it has been proved that big data plays a very significant role in
terms of supporting the business into positive manner.
CONCLUSION
The above stated report concludes that information technology plays a very significant
role at the organisational level. It has been concluded that big data is very necessary in order to
achieve out of box performance by the organisation. There are certain characteristics of big data
which help the organisation in terms of further growth and development as well. It has been
determined that there are certain challenges of big data analytics and also there are certain
techniques which are very helpful for the organisation for their positive development. At the
organisational level, big data is very necessary to analyse so that it contribute into context of
further growth and development of the organisation. So in the overall manner, big data analytics
and information technology is very necessary for achieve the tasks within the shorter period of
time. In this manner, this concept achieves its significance according to changing period of time.

REFERENCES
Books and Journals
Chistyakova and et.al., 2020. Big data analysis in film production. In Cyber-Physical Systems:
Advances in Design & Modelling (pp. 229-236). Springer, Cham.
Faccia and et.al., 2019, August. Accounting information systems and ERP in the UAE: an
assessment of the current and future challenges to handle big data. In Proceedings of
the 2019 3rd International Conference on Cloud and Big Data Computing (pp. 90-94).
Hassan Zadeh and et.al., 2019. Social media for nowcasting flu activity: Spatio-temporal big data
analysis. Information Systems Frontiers, 21(4), pp.743-760.
Ionescu, L., 2019. Big data, blockchain, and artificial intelligence in cloud-based accounting
information systems. Analysis and Metaphysics, 18, pp.44-49.
Kunanets, N., Vasiuta, O. and Boiko, N., 2019, September. Advanced technologies of big data
research in distributed information systems. In 2019 IEEE 14th International
Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 3, pp.
71-76). IEEE.
Lee, S. and Huh, J. H., 2019. An effective security measures for nuclear power plant using big
data analysis approach. The Journal of Supercomputing, 75(8), pp.4267-4294.
Maass and et.al., 2018. Data-driven meets theory-driven research in the era of big data:
Opportunities and challenges for information systems research. Journal of the
Association for Information Systems, 19(12), p.1.