Interpreting Big Data: Characteristics, Techniques, and Challenges

This study focuses on interpreting what is big data and its key characteristics. It also analyzes the techniques available for analyzing big data and the challenges faced in big data analytics. Additionally, it explores examples of big data technologies and their role in supporting businesses.

Assessment

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
Interpreting what is big data and its characteristics....................................................................1
Analysing the techniques which are currently available to analyse big data and also the
challenges of big data analytics...................................................................................................2
Examples of big data technologies and how it can be used to effectively support business,
please use examples wherever necessary....................................................................................4
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................6

INTRODUCTION
Big data is considered to be as a term which in turn has been used to effectively describe the
large volume set of data which are both unstructured and structured (Malek and et.al., 2017).
This in turn inundates a business on a daily basis. It is a collection of data which grows
exponentially over a period of time. This study focuses on interpreting what is big data and the
key characteristics. Furthermore, this study also analyse the techniques which are currently
available to analyse big data and also the challenges of big data analytics. This study also focuses
on how big data technologies can be used to effectively support business.
MAIN BODY
Interpreting what is big data and its characteristics.
Big data is also referred to as a field which is useful in effectively treating ways to
effectively analyse and also systematically extract useful set of information in order to deal with
the key data sets which are large and also complex in order to be dealt with the traditional data
processing application software (Petrenko, 2018). Big data is a term which has been used to
effectively describe the large volume set of data which are both unstructured and structured. Big
data is considered to be prominent in recognizing the patterns and also helps in interpreting the
key anomalies. It is useful in effectively managing the security and helps in preventing any
significant set of issues.
One of the key significant advantage which is mainly linked with the big data in turn
mainly includes new set of possibilities for the company. Big data is considered to be as one of
the key significant way through which it is useful in improving the operational efficiency by
bounds and leaps. It is also significant in extracting meaningful set of patterns and helps in
extracting significant level of information. Big data is highly prominent in effectively analysing
the latest set of market trends and also allows one to effectively keep pace with the competitors
within the market. Big data analytics is significant in offering the real time insights and is
relevant in maintaining the manual errors (Furht and Villanustre, 2016). Big data is highly
relevant because it helps in the better decision making. It is significant for the companies to grow
and compete. This eventually helps in increasing the productivity and is relevant in effectively
analysing the large number of data and improving the business performance and attain greater set
of efficiency. Big data helps in improving the customer service, increased degree of agility and
revenue and helps in the detection of the fraud.
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The key characteristics of the big data are:
VOLUME: The quantity or the volume associated with the stored and generated data.
The volume of the data is determined by the potential and value insight which in turn is referred
to as a big data. The size associated with the big data is larger than petabytes and terabytes.
Veracity: Veracity is also another key characteristics of the big data. It is mainly
associated with the data value and data quality. The quality of the data which is associated with
the capture data can be varied greatly and also affect accurate analysis.
Velocity: The speed at which the data has been generated and also processed in order to
meet the challenges and demands which lie in the path of development and growth. However,
big data can be available on a real-time basis. Big data has been produced more continuously
when compared with the small data (Storey and Song, 2017). There seems to be two types of
velocity associated with the big data which mainly includes frequency of handling, publishing
and recording and frequency of generation.
Variety: The type as well as the nature of the data is considered to be another key
characteristics associated with the big data.
Analysing the techniques which are currently available to analyse big data and also the
challenges of big data analytics.
Big data is considered to be as an application of the specialized set of techniques within the
process in order to effectively implement large set of data. It becomes difficult to manage data
with the use of on- hand database management tool.
Machine learning: This is one of the key prominent big data techniques which relevant in
extracting the patterns and improving the business results and outcomes with greater degree of
efficiency and performance. It is significant in determining the key methods which they more
like react to in order to attain the best possible results and outcomes.
Data mining: It is one of the key significant big data technique because data mining is
prominent in effectively extracting the large number of patterns from large data sets by
effectively combining key prominent methods linked with the machine learning and statistics
associated with the database management (Malek and et.al., 2017). It is significant in effectively
predicting the key relevant patterns and analyse the current trends within the market.
A/B split testing: This is one of the key prominent big data techniques which mainly
involves effective compare of the control group with the specific set of test groups. This is
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significant in discerning what changes and treatment will help in improving given objective
variables. This techniques appropriate for testing huge set of numbers and is considered to be
significant in gaining meaningful set of differences.
Regression analysis: It is one of the key significant big data techniques which in turn is in
the specific form of the predictive modelling technique (Lokers and et.al., 2016). This mainly
focuses on effectively investigating the relevant form of relationship associated with the
independent and dependent variables. This technique is useful in time series modelling,
forecasting and also finding the causal effect relationship between variables.
Statistical analysis: It is one of the key significant big data techniques which is relevant in
summarizing large set of data and improves the business performance and efficiency. It is highly
significant in drawing relevant degree of conclusion in subject to improve the business
performance and efficacy.
The key challenges which has been linked with the big data mainly comprise of data
storage, information privacy, capturing data, sharing, data storage, visualization, etc. Scaling up
and scaling down the big data in accordance with the current demand is considered to be one of
the biggest challenge related with big data analytics (Storey and Song, 2017). Big data projects
tends to evolve and grow on a consistent basis. Overcoming big data resources and talent
constraints is also one of the biggest challenge which has been faced related with the big data
analytics. Collecting and also interpreting diverse and massive dataset is also one of the key
significant challenge which has been faced in the big data analytics. The big data tools are
considered to be useful because it helps in collecting real-time data from internal as well as
external sources. It is significant because it helps in collecting large volume of unstructured and
structured set of data from each relevant sources. However, the data which has been collected
from various sources tends to differ in quantity and format. Maintaining security from a data
integrity and privacy is considered to be one of the biggest challenge which has been faced by
the big data analytics. The business must focus on using affective big data tools and techniques
which is useful for the organisation to process, store and analyse the information in an efficient
manner. The business must also focus on affectively implemented a robust security strategy in
order to analyse, collect, store, utilise and manage lots at a volume of data on a real-time basis by
eliminating risk Associated with data exposure and data breaches (Arias and Bae, 2016). It is of
utmost importance to keep the big data security strategy dynamic and flexible in order to address
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the security as well as privacy issues. Poor visualization is another biggest challenge which has
been faced which tends to blend together with irrelevant as well as mundane findings. It is also
challenging to collaborate as well as share big data analytics projects due to the transparency,
security, accessibility and other data transfer issues.
Examples of big data technologies and how it can be used to effectively support business, please
use examples wherever necessary.
Big data inundates a business on a daily basis. It is a collection of data which grows
exponentially over a period of time. Big data technologies in turn are considered to be of higher
significance because it improves the business performance and helps in analysing the current
trends of the business.
Edge computing: Edge computing systems is considered to be one of the key significant big data
technology which is useful in reducing the amount of information which must be transmitted
over the specific network. This is useful in reducing network traffic as well as related cost. Edge
computing is one of the key prominent technology which is helpful in decreasing demand upon
the data centre and cloud computing facilities (Oussous and et.al., 2018). It is also useful in
Freeing of the capacity associated with other workloads and eliminate a potential single point
failure.
Artificial intelligence: This big data technology is considered to be prominent because it helps in
changing data analytics by using results from past analytics over a specified period of time. It is
considered to be prominent for business because it helps in providing accurate and future
predictions based upon the current events. It is also relevant for the business for powering up the
business analytics by significantly identify issues and potential problems which cannot be
detected by humans. However, Organisation which do not comply with the artificial intelligence
for the data analytics will lag behind key competitors who use artificial intelligence for big data
analytics.
In – memory database: That option associated with in- memory database computing has been
increasing because business tends to seek easy and quick access to the data and analytics in order
to take informed decision. Use of in- memory computing tends to offer insight which the
management of the company needs to use in order to increase the operational efficiency,
marketing, sales and improve finances of the company.
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Block chain: Block chain is considered to be as distributed database technology which mainly
underlie bitcoin digital currency (BIG DATA TECHNOLOGIES THAT ARE EXPECTED TO
FLOURISH IN 2020, 2020). One of the key significant feature of the Block chain database
technology is that, that once the data has been indulged or return it cannot be changed or deleted.
Block chain technology is highly secure and if useful in making excellent choice for business
decision making.
NoSQL Database: This is one of the key significant technology which helps Data administrators
to manipulate, manage and query the structured data which has been stored within relational
database management system. It is significant because it tends to offer high degree of flexibility
and handle wide variety of data types in large volumes. It is significant in effectively predicting
the key relevant patterns and analyse the current trends which leads to improved business
performance and efficiency with degree of accuracy and relevance.
CONCLUSION
From the conducted study it has been summarized that, big data is a term which has been used to
effectively describe the large volume set of data which are both unstructured and structured.
Volume, veracity, variety and velocity are considered to be as the key characteristics of the big
data. A/B split testing is significant in discerning what changes and treatment will help in
improving given objective variables. Maintaining security from a data integrity and privacy is
considered to be one of the biggest challenge which has been faced by the big data analytics. One
of the key significant feature of the Block chain database technology is that, that once the data
has been indulged or return it cannot be changed or deleted.
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REFERENCES
Books and Journals
Arias, M.B. and Bae, S., 2016. Electric vehicle charging demand forecasting model based on big
data technologies. Applied energy, 183, pp.327-339.
Furht, B. and Villanustre, F., 2016. Big data technologies and applications. Berlin, Germany:
Springer.
Lokers, R and et.al., 2016. Analysis of Big Data technologies for use in agro-environmental
science. Environmental Modelling & Software, 84, pp.494-504.
Malek, Y.N and et.al., 2017. On the use of IoT and big data technologies for real-time
monitoring and data processing. Procedia computer science, 113, pp.429-434.
Oussous, A and et.al., 2018. Big Data technologies: A survey. Journal of King Saud University-
Computer and Information Sciences, 30(4), pp.431-448.
Petrenko, S., 2018. Big Data Technologies for Monitoring of Computer Security: A Case Study
of the Russian Federation (pp. 1-249). Cham: Springer.
Storey, V.C. and Song, I.Y., 2017. Big data technologies and management: What conceptual
modeling can do. Data & Knowledge Engineering, 108, pp.50-67.
Storey, V.C. and Song, I.Y., 2017. Big data technologies and management: What conceptual
modeling can do. Data & Knowledge Engineering, 108, pp.50-67.
Online
BIG DATA TECHNOLOGIES THAT ARE EXPECTED TO FLOURISH IN 2020. 2020.
[ONLINE]. Available through< https://www.analyticsinsight.net/big-data-technologies-
expected-flourish-2020/ >
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