Analyzing Information Systems and Big Data: Techniques & Business

This report provides a comprehensive overview of information systems and big data analysis, beginning with an introduction to big data and its core features: volume, variety, velocity, veracity, and variability. It delves into the challenges associated with big data, such as data security, validation, silos, growth issues, lack of understanding, and synchronization. The report outlines various techniques for analyzing big data, including machine learning, regression analysis, A/B testing, and data mining. Furthermore, it elucidates how big data technology can assist businesses by improving communication with consumers, enabling risk analysis, identifying trends, re-developing products, and creating new revenue streams. The report concludes with references to books and journals used in its compilation.

INFORMATION SYSTEM
AND
BIG DATA ANALYSIS
Table of Contents
INTRODUCTION...........................................................................................................................2
MAIN BODY...................................................................................................................................2
Big data and its features-.............................................................................................................2
Big Data Challenges - ................................................................................................................3
Techniques presently available for analysing the big data -.......................................................4
How the big data technology can assist business, explanation with examples-..........................5
POSTER ..........................................................................................................................................6
REFERENCES.................................................................................................................................7

INTRODUCTION
Big data is a combination of group of sets involves managing data sets so large and it is
often a complex process of examining large amount of data to uncover hidden patterns,
correlations and others. Big data analysis involves visualization, integration, cleansing and
management . It helps businesses and organisation make better decision by revealing information
that would have otherwise been hidden(Baig, Shuib and Yadegaridehkordi, 2019) . It includes
capturing data, data storage, sharing, transfer, information privacy and data source. Big data
refers to the large , diverse sets of information that grow at ever- increasing rates . They grow
very rapidly with the increasing pace of time. It is all about having high value, actionable
insights from the data assets. This report states that it is a collection of data and ability to use it
our advantage across a wide range of areas, including business.
MAIN BODY
Big data and its features-
Big data can be defined as data sets whose size or type is beyond the ability of
traditional relational databases to collect , manage the data. It is the process of collecting useful
information by analysing different types of data sets. It may look simple, but this a complex
process(Dindic and Johansson, 2022) . Big data helps in organising, transforming the data on the
basis of organisation requirements. Big data analytics helps organisation in decision-making
process. Companies use the big data system to improve operations , provide best customer
services. It allows company to identify trends, patterns
Features-
 Volume - Volume refers to the size of the big data. The data can be considered big or not
can be considered on the basis of volume. Organisations collects data from different
sources . The range of data can be measured by this characteristics of big data.
Previously, storing and analysing the data was a big challenge.
 Variety – It refers to all the structured and unstructured data that has the possibility that
ha the probability of getting generated through machines or by human beings. The

diversity of different data types according to their nature. It refers to the fundamental
change in analytical requirements from traditional approach. It entails the types of data
that vary in format and how it is organised and ready for processing.
 Velocity- It refers to how quickly data is generated and how quickly that data moves.
The rate at which data is generated or the speed of the data. This refers to the speed at
which the data is getting accumulated(Ghani and et. al., 2019) . It determines the
potential of the data that how fast it is working.
 Veracity - It describes the data's accuracy and quality and trustworthiness. It refers to the
incomplete data or the presence of errors, missing values. The truth or accuracy of data
and information assets, which often presents executive-level confidence.
 Variability – It plays an important role in big data analysis . It describes the number of
inconsistencies in the data or inconsistent speed at which the big data is loaded.
Variability mainly focuses on understanding and interpreting the correct meanings raw
data. It is difficult to manage a large amount of unstructured information.
Big Data Challenges -
There are many challenges that occurs while doing big data analytics that are discussed
below-
 Securing Data - Security is one of the most complicated Big Data challenge specifically
for organisations that have important and personal data or have a lot of sensitive
information(Lagsten and Andersson, 2018) . When it comes to the protection of the
company data most of the organisations thought that they have a right security policies
for the safety of confidential data . Often companies are so busy in understanding,
analysing their data sets that they forward data security for later . Companies are
recruiting the professionals for the security of data .

 Data Validation - Data validation is a major challenge comes between in while doing
big data analytics for the company. Sometimes it is a time consuming process but
particularly if the validation is manually performed. It includes scripting and open source
platform. These require experience , which can get expensive.
 Data silos - This is one another big problem that can occur while dealing with it. They
describe isolated data where only one group of organisation can access a set of source of
data. Its a stored data that is not available to entire organisation but only to some parts of
it.
 Data Growth Issues - This is the foremost challenge of big data which stores the huge
sets of information accordingly(Leb and Retscher, 2021) . With the rapidly increasing
time , the data is also growing and with that enterprises are struggling to store large
amounts of data. And the mostly data comes from photos, audio, documents, files etc..
 Lack of proper understanding of massive data - Due to insufficient knowledge or lack
of understanding, companies are failing in their big data execution. Despite the benefits,
companies have been very slow to adopt technology and put a certain plan in place for
how to create a data centric culture.
 Synchronization - It is a critical problem. It is the process of sharing two or more
locations which share the same data. It is a challenge for companies to establish data
consistency between databases and automatically copies.
Techniques presently available for analysing the big data -
Big Data techniques are given below-
 Machine learning- It determines or includes that can learn from the data. It is a
software that can describe the possible outcome out of a certain group of event. It
determines the probability of winning a case, and setting legal billing rates.

 Regression Analysis - It manipulates independent variables to see how they affect the
dependent variables. It is used for predicting , time series modelling and finding casual
effect relationship between the variables(Matsubayashi and et. al., 2018). It is a statistical
technique. Here, the objective is to forecast the future situation .
 A/B testing - It is one of the most popular controlled experiments used to optimize web
marketing strategies. It is an test on two variants to see which one performs the better
based on a metric. This technique involves comparing a control group with a variety of
test group.
 Data Mining – It determines patterns by merging methods from statistics and machine
learning from large data sets within database management. It enables organisations to
predict future trends and make more informed business decisions.
How the big data technology can assist business, explanation with examples-
 Communication with consumers – Big data analysis help companies to understand their
customers in a better way. In today's market, customers are petty smart that they know
very well that what is their priorities (Naeem, and et .al., 2022). Big data helps company
to transform their company relation with their customers. It allows companies to know
their customers in order to enhance the marketing strategies and the customer experience.
 Risk analysis - Risk analysis will create a major impact on the business report. It helps
in forecasting the risk of business that can harm it. With the help of cybercrime, big data
analysis will help to detect patterns that indicate a potential cybersecurity threat . It will
permit company to quantify and model risks that can confront their operations.
 Identify Trends - Through big data , it is also possible for businesses to identify trends,
which can be useful in product research and development. From customer behaviours to
buying patterns, big data will provide the management with the information that it need
to analyse that how trends will change over time.

 Re- Develop products- Big data helps organisation in a best possible way to collect
information and feedback. Using big data to inform new product development has many
advantages. It helps us to create products according to the consumer needs and
preferences , minimizes the risks associated with a new products launch. By surveys or
anything firm can forecast the needs of the consumers or outsiders so it will help in
producing new products and developing existing ones.
 New Revenue System – It will discuss the importance of generating new revenue
streams with the help of data analytics. Data analytics will uncover the resources to
increase the revenue. This data is valuable for the firm and also for the others (Qi, 2020).
POSTER

REFERENCES
Books and Journals
Baig, and et. al., 2019. Big data adoption: State of the art and research challenges. Information
Processing & Management, 56(6), p.102095.
Dindic, H. and Johansson, L., 2022. Digital information systems: How is the psychosocial work
environment affected by daily work with a digital information system?.
Ghani, and et. al., 2019. Social media big data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Lagsten, J. and Andersson, A., 2018. Use of information systems in social work–challenges and
an agenda for future research. European Journal of Social Work, 21(6), pp.850-862.
Leb, A. and Retscher, G., 2021. Study for the Development of a Guidance and Information
System Based on Wi Fi for TU Wien. In FIG Working Week 2021 20-25 June Smart
Surveyors for Land and Water Management Challenges in a new Reality (p. 15).
Matsubayashi, and et. al., 2018, November. A research on document summarization and
presentation system based on feature word extraction from stored informations. In 2018
Conference on Technologies and Applications of Artificial Intelligence (TAAI) (pp. 60-
63). IEEE.
Naeem, and et. al., 2022. Trends and future perspective challenges in big data. In Advances in
intelligent data analysis and applications (pp. 309-325). Springer, Singapore.
Qi, C.C., 2020. Big data management in the mining industry. International Journal of Minerals,
Metallurgy and Materials, 27(2), pp.131-139.