Big data refers to data that is large in volume, variety, and velocity, making it difficult to process using traditional methods. The rise of real-time data has driven growth in big data analytics. Analytics processes involve data loading, cleansing, analysis, and reporting to help address challenges in data management and driven decision making. Tools like Hadoop, MapReduce, and NoSQL technologies help store and process big data, while visualization, machine learning, and predictive analytics help analyze large, varied data. Big data analytics can transform complex problems into simple solutions and provide benefits across industries through automated reports and data-driven customization.

2.  Big data denotes the data that are considerably
high in the variety, velocity that makes them
difficult for handling with traditional tools and
techniques.
 The big data analytics improved in the recent
digital world as real time data had been driving
the business.

4.  There exists a great problem in data driven
decision-making. The data driven decision-
making problems are reduced by adopting
analytics process, which leads to resolve
various challenges in data management
(Maltby, D., 2011).

6.  Data Analytics process consists of various stages
such as Data loading, Data cleansing, Analysis,
Reporting, and much more (Rajaraman,V., 2016).
 Numerous tools are implemented for big data
analytics in the organizations such as Hadoop,
Mapreduce, NoSQL technologies for storage and
processing (Russom, P., 2011.).
 In the Analytics, it consists of visualization tools,
machine learning, and predictive analytics for
handling huge variety of data (Zakir, J., et.al, 2015).

8.  Numerous tools are implemented for big data
analytics in the organizations such as Hadoop,
Mapreduce, NoSQL technologies for storage
and processing (Russom, P., 2011.).
 In the Analytics, it consists of visualization
tools, machine learning, and predictive
analytics for handling huge variety of data
(Zakir, J., et.al, 2015).

11.  Effects of usage of Big data analytics led to
transformation of the complex problems into
simple solutions (Srinivasa, S. and Bhatnagar,
V., 2012).

12.  Big data can be applied in any industry and
the process yields lot of advantages for the
firms regarding automated reports, data
driven decision-making, and all other
difficult tasks in simpler means.
 Big data even facilitates customizations to
their processes as per requirements.

13.  Thus, the Big data analytics remains the great
solution for the problem of managing large
quantity of real time data
 Challenges such as storage, complexity,
management, pre-processing, analytics,
privacy, security, real-time analysis are easily
resolved in Big data analytics that paved way
for continuous development in data driven
decision-making and management.

14.  Maltby, D., 2011, October. Big data analytics. In 74th
Annual Meeting of the Association for Information
Science andTechnology (ASIST) (pp. 1-6).
 Rajaraman, V., 2016. Big data analytics. Resonance,
21(8), pp.695-716.
 Russom, P., 2011. Big data analytics. TDWI best practices
report, fourth quarter, 19(4), pp.1-34.
 Srinivasa, S. and Bhatnagar, V., 2012. Big data analytics.
In Proceedings of the First International Conference on Big
Data Analytics BDA (pp. 24-26).
 Zakir, J., Seymour, T. and Berg, K., 2015. BIG DATA
ANALYTICS. Issues in Information Systems, 16(2).

15. Poster: Big Data Analytics in IT
Big data denotes the data that are considerably high in
the variety, velocity that makes them difficult for handling
with traditional tools and techniques. The big data
analytics improved in the recent digital world as real time
data had been driving the business.
DataAnalytics process consists of various stages such as Data
loading, Data cleansing,Analysis, Reporting, and much more
(Rajaraman,V., 2016).
Numerous tools are implemented for big data analytics in the
organizations such as Hadoop, Mapreduce, NoSQL
technologies for storage and processing (Russom, P., 2011.). In
the Analytics, it consists of visualization tools, machine
learning, and predictive analytics for handling huge variety of
data (Zakir, J., et.al, 2015).
Figure 2: Process in Big DataAnalytics
Effects of usage of Big data analytics led to
transformation of the complex problems into simple
solutions (Srinivasa, S. and Bhatnagar,V., 2012).
Big data can be applied in any industry and the process
yields lot of advantages for the firms regarding
automated reports, data driven decision-making, and
much more.
There exists a great problem in data driven decision-
making. The data driven decision-making problems are
reduced by adopting analytics process, which leads to
resolve various challenges in data management (Maltby,
D., 2011).
Maltby, D., 2011, October. Big data analytics. In 74th
Annual Meeting of the Association for InformationScience
andTechnology (ASIST) (pp. 1-6).
Rajaraman,V., 2016. Big data analytics. Resonance, 21(8),
pp.695-716.
Russom, P., 2011. Big data analytics. TDWI best practices
report, fourth quarter, 19(4), pp.1-34.
Srinivasa, S. and Bhatnagar,V., 2012. Big data analytics. In
Proceedings of the First International Conference on Big
Data Analytics BDA (pp. 24-26).
Zakir, J., Seymour,T. and Berg, K., 2015. BIG DATA
ANALYTICS. Issues in Information Systems, 16(2).
•Thus, the Big data analytics remains the great solution
for the problem of managing large quantity of real time
data
•Challenges such as storage, complexity, management,
pre-processing, analytics, privacy, security, real-time
analysis are easily resolved in Big data analytics that
paved way for continuous development in data driven
decision-making and management.
Introduction Process in Big Data Analytics Solutions from Big Data
Characteristics of Big Data
Need of Data Analytics Process
References
Conclusion