Big Data Analysis: Characteristics, Challenges, and Technologies

This report provides an overview of big data analysis, focusing on its characteristics, challenges, and supporting technologies. It highlights the key characteristics of big data, including volume, velocity, variety, and veracity. The report identifies significant challenges in big data analytics, such as poor data quality and the need for robust support systems. It also explores the application of specific technologies like data mining and Hadoop for data storage, processing, and analysis. Furthermore, the report references examples of how big data technologies are used by businesses to improve decision-making and overall performance, such as the example of M&S. The report concludes by emphasizing the need for businesses to effectively manage data to tackle security and privacy concerns.

Big Data
It is based on the modern technology in which describe large amount of data or
information- both structure and unstructured format. It help to analyse for insights
that lead to better decisions & strategic business moves.
Challenges of big data analytics
• Poor quality data- there is nothing harmful in data analytics than inaccurate
information. in most of cases, inappropriate data will be generating an error at
the time of execution. Consequences, it may affect on the decision-making of
enterprise (Mikalef and Krogstie, 2020).
• Lack of support- Data analytics cannot be effective without support for
organisation. At some point, it will be increasing a risk or threat during
processing of data or information.
• Getting data into big data platform- Data is increasing every single day, which
means that enterprises have to tackle a limitless data on regular basis.
Therefore, it may have increased the issue of security and privacy.
Characteristics of big data
Here are considered five characteristics of big data as follows:-
 Volume
 Velocity
 Variety and veracity
REFERENCES
Ranjan, J. and Foropon, C., 2021. Big data analytics in building the competitive intelligence of
organizations. International Journal of Information Management, 56, p.102231.
Mikalef, P. and Krogstie, J., 2020. Examining the interplay between big data analytics and
contextual factors in driving process innovation capabilities. European Journal of Information
Systems, 29(3), pp.260-287.
Technique used in big data
• Data Mining- it is consider as big data technique in which support for
extracting data from different sources. For example when customer data is
combined to find which segments are likely to react as an offer.
• Hadoop- it was developed to store, collect, process and analyse large amount
of data with the help of programming model (Ranjan and Foropon, 2021).
Hadoop as big data technologies for data warehouse needs. This trend seems
to continue and growing.
How big data technology support business with example
Big data technology support companies store large amount of data or information while
enabling significant cost benefits. Such modern technologies include Hadoop and cloud-based
analytics. For Example- M&S is retailer that help for analysing large amount of information
and improve decision making. it tends to enhance security, in which big data technology
application can resolve themselves.