EDUCATIVE

1. GROUP MEMBERS
1. Samuel Kinuthia CT201/0002/16
2. Mutiso Eric CT201/0050/18
3. Caroline Nungari CT201/0027/18
4. Martha Wangui CT201/0029/18
5. John Macharia CT201/0019/18

2. Definition of terms …………………………………….….. ……..3
History of big data ………………………………………………….4
General working principles of big data ……………………5
Benefits of big data …………………………………………………6
Disadvantages of big data ……………………………………….7
Applications of big data ………………………………………....8
Future of big data……………………………………………………..9
Conclusion……………………………………………………………….10
References……………………………………………………………….11

3.  Big data - refers to larger, more complicated data sets,
particularly those derived from new data sources.
Because these data sets are so large, typical data
processing technologies can't handle them.
 Data analytics – systematic computational analysis of
data.
 Data Science-is the domain of study that deals with vast
volumes of data using modern tools and techniques to
find unseen patterns, derive meaningful information and
make business decisions.
 Predictive analysis-is use of data, statistical algorithms
and machine learning techniques to identify the
likelihood of future outcomes based on historical data

4.  First traces of big data were seen way back in 1663 when John Graunt was
studying information on the bubonic plague. He was the first person ever to
use statistical data analysis.
 The world first saw the problem with the overwhelming data in 1880’s.
 Throughout the 20th century, data evolved at an unexpected speed. Big data
became the core for evolution.
 Machines for storing information magnetically and scanning patterns in
messages were developed during that time.
 In 1965, the us government built the first data center with the intention of
storing millions of fingerprints sets and tax returns.
 People began to notice how much data Facebook, YouTube, and other
online services generated around 2005. This led to tools like Hadoop to
handle these huge amounts of data.

5.  Ensuring that the goal is clear : This involves having in mind how big
data could be used to help achieve specific business objectives.
 Detection of trends : big data insights can often help businesses detect
changes in customer behavioral patterns and warn them to prepare for
game changing trends on time.
 Analyze relevant information : Businesses should only focus on relevant
information only since there is usually big volumes available for
consideration.
 Establish connections : you should always analyze information clusters in
connection with other processes and factors of influence.

6.  Creates opportunities to make better decisions : data driven insights
provide a foundation for more informed and reliable decisions.
 Increases productivity and efficiency : with more data being processed the
organization could identify areas with higher potential.
 Helps improve customer service and customer experience : businesses
could personalize the services for their customers.
 Helps reduce cost : by streamlining operations and improving efficiency
could introduce significant cost savings.
 Helps with fraud and anomaly detection in business operations :
 Provides greater agility and speed to market : developments in real time
processing of big data enable organizations to become more agile.

7.  Questionable data quality : data driven decisions could only be as good as
the quality of the data sets under consideration and the resulting analysis.
 Heightened security risks : the data that is usually included in data lakes is
usually sensitive records requiring special protections levels.
 Compliance headaches : especially with information collected on
consumers, organizations must navigate an increasingly complex and strict
environment for data privacy.
 Big data skills shortage : organizations require data scientists to fully
utilize the benefit from big data. These area though has skill shortage.

8.  Product development : Big data is used by companies like Netflix and
Procter & Gamble to predict client demand.
 Predictive maintenance: Organizations can perform maintenance more
cost-effectively and maximize components and equipment uptime by
identifying indicators of possible faults before they occur.
 Customer experience
 Fraud and compliance : Big data can help you spot patterns in data that
signal fraud and aggregate massive amounts of data to speed up regulatory
reporting.
 Machine learning: Instead of programming machines, we can now teach
them. This is made possible by the availability of massive data for training
machine learning models.
 Drive innovation: Big data may assist you in innovating by examining the
interdependencies between persons, institutions, entities, and processes,
and then figuring out new methods to use those findings.

9.  Data volumes will continue to increase and eventually migrate to the cloud.
The majority of big data experts agree that the amount of generated data
will be growing exponentially in the future. They will require the cloud for
storage.
 The demand for data scientists will rise as the need for big data analysis
continues to be needed.

10.  The topic of big data has been gaining a lot of interest due to its
unprecedented opportunities and benefits. In this current era there are
voluminous amounts of data being produced in which there are underlying
patterns and details for hidden knowledge which should be analyzed and
utilized.
 This has led to the development of different varieties of tools for handling
this huge amounts of data.
 The findings from the analysis of this data has provided organizations with
the opportunity to provide better services while driving the product
development.

11. Russom, P. (2011). Big data analytics. TDWI best practices report, fourth
quarter, 19(4), 1-34.
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data
analytics: a survey. Journal of Big data, 2(1), 1-32.