Information Systems and Big Data Analysis

This assignment focuses on analyzing the characteristics, challenges and uses of big data for an organization. It explores the techniques available to analyze big data and converting it into useful information. The assignment also discusses about technologies available to support business in analyzing the big data to solve business problems.

This poster basically focused on analyzing the characteristics,
challenges and uses of big data for an organization. The main
is to explore the techniques available to analyze big data and
converting it into useful information. The assignment also
discusses about technologies available to support business in
analyzing the big data to solve business problems. These new
technologies are efficient enough to screen large data base
within short-duration. As per rule of research; increasing the
size of sample gives more accurate data. Hence, by screening
large information, the probability of error in result decreases.
Big Data and their characteristics
The term "big data" refers to information that is so large, fast,
or complex that it is difficult or difficult to manage using
conventional methods. The exhibit that accesses and collects
a lot of data for research has been around for quite some time.
Characteristics of big data
Volume: Organizations gather information from a variety of
sources, including contracts, smart devices (IoT), mechanical
devices, recordings, online media, and more. Previously, it
would have been a major issue, however, cheaper stock-
taking at levels like Information Lakes and Hadoop has made
the pressure easier.
Velocity: With the development of the Internet of Things,
information moves in clusters at incredible speed and should
be handled appropriately. Smart RFID tags, sensors and
meters monitor the need to closely and continuously manage
these floods of information.
Variety: Data comes from a wide range of organizations,
from numerical information organized in standard datasets to
unstructured content reports, messages, charts, sounds, stock
information and currency exchanges.
Veracity: This Big Data element is related to the past one. It
indicates the level of trust of information.
3. Get meaningful information using Big Data Analytics: It is
vital that business associations gain meaningful knowledge from
the analysis of Big Data, and it is also important that only the
relevant sector comes close to this data. An important test
examined by organizations in the Big Data study fixes this big
hole in a definitive way.
4. Obtain voluminous data in the big data platform:
Unsurprisingly, the information improves over time. This
basically shows that trade associations have to deal with a lot of
information on a regular basis. The amount and variety of
information available these days can surpass any information
engineer which is why making information available is simple
and beneficial for brand owners and executives.
5. Uncertainty of Data Management Landscape: With the advent
of Big Data, new developments and organizations are being
encouraged every day. Be that as it may, a key test the
organizations in the Big Data study looked at was to find out
which innovation would be appropriate for them without exposing
new problems and anticipated risks.
6. Archiving and quality of information: Business associations are
developing rapidly. With the significant development of large
business organizations and associations, the amount of
information provided has increased. The potential of this vast
amount of information is becoming a real test for everyone.
Information collection options such as information lakes /
warehouses are typically used to collect and store large amounts
of unstructured and organized information in its local
configuration.
Since most of the information you receive is unstructured,
navigate through the unnecessary data and use the rest for
management.
Value: Of the various features of Big Data, perhaps the most
important is value. No matter how fast the information is
created or the amount it contains, it needs to be reliable and
useful. Furthermore, the information is not relevant for
preparation or review. A study states that low-quality
information can bring nearly 20% of bad luck to an
organization’s revenue.
Challenges of Big data
By measuring the information provided on a regular basis it
seeks to store, manage, use and analyze its information. In fact,
even large commercial companies are struggling to find ways
to make this amount of information useful. Today, the amount
of information generated by large companies is improving, as
noted above, at 40 to 60% per year.
1. Need to synchronize between different data sources: As
information records become more and more diverse, there is an
important test to integrate them scientifically. If this goes
unnoticed, it will create holes and lead to erroneous messages
and bits of knowledge.
2. Severe shortage of professionals who understand Big Data
analytics: Analyzing information is essential to make this vast
amount of generated information systematic and valuable.
With the huge increase in information, great interest was
created for big data researchers and big data analysts.
Information Systems and Big Data Analysis
INTRODUCTION

7. Data security and privacy: When companies discover
how to use Big Data, there is only a wide range of possible
outcomes and openings. To the extent possible, it also means
the potential risks of large amounts of information in terms
of information security and protection. The Big Data tools
used for analysis and capacity use the various sources of
information. This ultimately poses a serious risk of opening
up to information, leaving it helpless.
Techniques of Big data
1. A / B Test: This method of intelligence analysis involves
comparing a target group and a collection of test collections
to determine which drugs or variants work on a particular
target variable. McKinsey makes the case for breaking down
what duplication, text, images or format improve conversion
rates on a corporate web page on the Internet.
2. Information integration and integration: By combining a
group of strategies that analyze and coordinate information
from a variety of sources and configurations, the knowledge
fragments are more competent and conceptual than if they
were created through source individual information.
3. Data mining: A standard tool used in big data analysis,
information extraction removes projects from large data
indexes by combining measurement strategies and artificial
intelligence, within data management. One pattern that
would be used when informant information is extracted is
which categories should respond to an offer.
4. Machine learning: Particularly in the field of artificial
reasoning, artificial intelligence is also used to analyze
information. Born from software engineering, he works with
PC computing to dispel data-driven suspicions. It provides
far-reaching expectations for human analysts.
5. Natural language processing (NLP): Known as a subset of
software engineering, artificial intelligence, and root science,
this information analysis tool uses computation to break
down (normal) human language.
6. Statistics: This approach works for collecting, organizing
and validating information, within overview and testing.
Application of Big data analysis
Making better business decisions: Big data give
organizations the tools they need to make better choices -
choices that are based on information, not speculation or
prediction. However, for this to happen, everyone in the
group should speak with the information they need to
develop the dynamics further. This means that information
shouldn't be the same place for IT departments and business
analysts; all things being equal, business customers across
the organization should have the ability to analyze and
analyze information with the goal of finding answers to their
most important business questions. This widespread
recognition of information is often referred to as information
democracy.
For example: Wal-Mart’s sales monster offers a stark picture
of this real-world information democratization. Crucially,
however, Wal-Mart allows relatives to access information in
a controlled manner, thus ensuring that non-geek types do
not get over the information and can get the appropriate
responses what they need to find.
Understanding customers: Awesome information searches
help organizations to understand customer behaviour and
take appropriate action to meet their needs.
For example, Disney uses Big Data ingenuity to understand
the behaviour of guests at its sports grounds, so it is far from
a much more interesting perspective for resort visitors. All
thanks to the introduction of the MagicBand. These visitor
wristbands work like IDs, passageways, room keys and even
paid devices, so all visitors have to do is slide the strap over
the sensors to pay.
Delivering smarter services or products: Awesome
information searches also assist organizations in providing
clearer administration or elements that rely on user reviews .
For example, the Royal Bank of Scotland (RBS) also uses Big Data
to better support its customers. The regular bank knows a lot about
their assets, from what they like to buy, to where they go on holiday.
RBS is beginning to address the potential of this information to
address customer problems more easily.
Conclusion
Big data is the key behind important areas like business, advertising,
contracts, monitoring and auditing. It changed the business approach
of organizations based on messages and objects around the world.
Therefore, the analytical and dynamic effects of Big Data must have
equal weight.
REFERENCES
Ankam, V., 2016. Big data analytics. Packt Publishing Ltd.
Guller, M., 2015. Big data analytics with Spark: A practitioner's
guide to using Spark for large scale data analysis. Apress.
Prajapati, V., 2013. Big data analytics with R and Hadoop. Packt
Publishing Ltd.
Russom, P., 2011. Big data analytics. TDWI best practices report,
fourth quarter, 19(4), pp.1-34.
Tsai, C.W., Lai, C.F., Chao, H.C. and Vasilakos, A.V., 2015. Big
data analytics: a survey. Journal of Big data, 2(1), pp.1-32.