Big Data Integration: Phases & Plan

This document discusses the phases and plan for implementing Big Data integration in an organization. It covers the strengths, weaknesses, advantages, and challenges of Big Data collection and analysis. The document also provides recommendations and suggestions for successful implementation. The primary phases for implementation include planning, tools & technology stack, change management & implementation, and data collection, moving, and analysis. The use of Big Data analytics for prescriptive, predictive, and diagnostic analysis is also discussed. Challenges and issues related to technical faults, employee resistance, and information security are also addressed.

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Big Data Integration
Phases & Plan
2/12/2019

Big Data Integration
Table of Contents
Introduction...........................................................................................................................................2
Strengths & Weaknesses of the Big Data Plan......................................................................................2
Advantages of Big Data Collection.......................................................................................................2
Phases for Implementation....................................................................................................................3
Planning Phase..................................................................................................................................3
Tools & Technology Stack................................................................................................................3
Change Management & Implementation...........................................................................................4
Data Collection, Moving, and Analysis.............................................................................................4
Key Questions.......................................................................................................................................5
Use of Big Data Analytics by Organization...........................................................................................6
Prescriptive Analytics........................................................................................................................6
Diagnostic Analysis...........................................................................................................................6
Predictive & Sentimental Analysis....................................................................................................6
Challenges & Issues..............................................................................................................................6
Suggestions & Recommendations.........................................................................................................7
Conclusion.............................................................................................................................................7
References.............................................................................................................................................9
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Introduction
Big Data is a term that refers to the massive sets of data comprising of structured, semi-
structured, and unstructured sets of data. The integration of Big Data tools with the existing
components and applications in a business organization is necessary in the present scenario
(Dhar, 2014). It is because there are massive data sets that need to be stored and managed for
the execution of the business operations and for carrying out decision-making processes and
activities. The document includes the plan and phases for the implementation and integration
of Big Data in an organization. The strengths, weaknesses, advantages, issues, and usage is
also covered.
Strengths & Weaknesses of the Big Data Plan
The primary strengths of the Big Data Plan are the clearly defined set of phases that can be
applied during the implementation. The clear definition of these phases will allow the
implementation team to have clarity on the goals and the occurrence of the risks will also be
avoided. The other strength of the Big Data Plan is the effective utilization of the technology
for advanced analytics and the integration of the tools with the other platforms and
applications (Garcia, Ramirez-Gallego, Luengo, Benitez & Herrera, 2016).
There are certain weaknesses that are also associated with the Big Data plan. There may be
security risks that may appear during the phases that are listed in the implementation plan.
The occurrence of these security risks may have an adverse implication on the information
properties, such as integrity, privacy, availability, and confidentiality of the data.
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Advantages of Big Data Collection
There are various advantages that are associated with Big Data collection and the same will
be offered to the organization.
The primary benefit is that the organization will be able to understand the customer choices
and preferences with the gathering of the Big Data sets. The data sets that will be collected
from different sources will be analysed using the Big Data tools. The trends and patterns
associated with the customers, identification of the target audience, and regulation of the
demand and supply as per the customer interest will be carried out (Bughin, 2016). The
customers will be provided with the desired set of services and products which will lead to
the improvement of the return of investment and will also lead to the expansion of the
customer base.
Another advantage that Big Data collection will offer to the organization will be cost-savings.
The data sets will be stored, managed, and streamlined. There will be lesser tools that will be
required for handling the data sets and the organization will be able to utilize the data in the
decision-making processes. The accuracy of the business decisions will improve which will
lead to enhanced cost-effectiveness for the organization (Kaur, 2018).
There will be business opportunities that will also emerge for the organization as the market
trends and patterns will be identified. Also, the organization will succeed in gaining
competitive edge in the market as the customer base will be expanded and the customer
relations will also be improved.
There are different forms of analysis that can be carried out on the Big Data sets that are
collected. The use of Big Data tools can be done to carry out sentimental analysis to get the
feedback from the market, customers, and competitors. The brand image and brand value will
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be improved accordingly as the organization will be able to identify its strengths and
weaknesses (Dumbill, 2013).
Phases for Implementation
Planning Phase
The primary phase that shall be involved in Big Data implementation is the planning phase. It
is the phase in which the organization shall identify the aim and goals for using and
integrating Big Data tools in its business architecture. The scope and requirements shall be
defined on the basis of the business case. The estimation and planning in the areas, such as
schedule, budget, risks, resources, stakeholders, quality, and communication shall be done in
this phase. The goals that are defined for Big Data implementation shall be SMART goals
(Daki, El Hannani, Aqqal, Haidine & Dahbi, 2017).
Tools & Technology Stack
There are various Big Data tools and Business Intelligence concepts that have been defined.
The organization shall do needs assessment to identify the tools and platform that it requires
as per its business operations and activities. The hardening of the Big Data technology stack
shall be done in this phase and strategizing for the hardening of the databases, middleware,
hardware, and applications shall be carried out. High availability, security, and resource
utilization shall be the key areas that shall be focussed upon.
Some of the Big Data techniques and strategies that may be used may include data
exploration, social analytics, performance management, and decision science. The
identification of the strategies and the tools shall be done in this phase.
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Change Management & Implementation
There may be certain infrastructural changes that may be needed to be done in the
organization before implementation of Big Data. The identification of these changes and their
execution shall be done before Big Data tools are implemented. The integration of the Big
Data tools shall then be done with the existing applications and tools. The migration of the
data sets and services and the syncing of all of the components shall then be carried out
(Sethi, 2015).
Data Collection, Moving, and Analysis
A distributed system is required for utilizing the Big Data concepts. It is required that the
collection, distribution, analysis, and display of the data is synced with each other. The
collection and data points will be connected with the Big Data application and the data that
will be collected from varied sources will be sent to the Hadoop cluster. The data may also
come from the social feeds. The use of Flume or Scribe may be done for loading the data sets
in Hadoop.
Big Data Collection
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The next step will include collation and interpretation of the data sets. The organization shall
make use of Apache Hadoop MapReduce for this purpose. These data sets will then be
combined with the data coming from other applications being used in the organization. These
may include Customer Relationship Management (CRM) data, transactional data, point-of-
sale data, and other databases. The use of Big Data connectors will be done for the task and
the data will be efficiently moved to the Oracle database (Hussain & Roy, 2016).
Moving the Big Data
The data mining and statistical models will be developed from the data sets that will be
analysed. The identification of the trends and patterns from these data sets will be done and
the same will be provided as the outcomes. The list of actions to be taken on the basis of the
decisions will also be displayed.
Key Questions
Some of the key questions that the client shall answer before the implementation and usage of
Big Data tools are as listed below.
 What are the technical tools and applications that are already being used in the
organization?
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 What are the security protocols that are followed by the organization for maintenance
of information privacy and security?
 What are the primary categories of information that are used and handled by the
organization?
 Are there existing Big Data platforms implemented in the organization?
 What is the organization’s policy for risk management, change management, and
quality management?
Use of Big Data Analytics by Organization
There are various ways in which the organization may use Big Data analytics.
Prescriptive Analytics
The organization can carry out prescriptive analytics using the Big Data tools and may take
business decisions on the basis of the results. There are various options and choices that are
now available for the execution of every business operation. The identification of the best
possible solution applicable for the organization will be revealed which will assist the
organization in enhancing its business performance.
Diagnostic Analysis
There may be certain business issues that may be associated with the organization along with
the presence of various risks. The use of Big Data analytics may be done by the organization
to carry out the root cause analysis (Yu, 2017). The primary causes behind the problems and
issues will be identified which will allow the organization to improve upon the gaps and
weaknesses.
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Predictive & Sentimental Analysis
The primary aim of the organization is to retain the existing customers and attract new
customers to maximize the customer base and earn higher profits & revenues. The use of this
technique will allow the organization to understand the trends and patterns associated with
the customers and the market. The customer preferences and the reasons associated with the
same will be revealed to predict on the future demand and choices. The sentimental analysis
will allow the organization to find out the target audience and the business decisions &
strategies will be regulated accordingly (Kumar & Singh, 2019).
Challenges & Issues
There may be certain challenges that the organization may face during the implementation
and integration of Big Data tools and platforms.
There may be certain technical challenges that may appear, such as technical faults & failures
or compatibility issues. It may hinder the overall progress and may cause unnecessary delays.
The resistance of the employees towards the changes is another major issue that may come
up. The employees may not accept the integration of Big Data in the business operations
which may lead to additional training costs and operational errors.
Information security and privacy challenges are other primary issues that may be observed.
There may be different forms of security risks and attacks that may be carried out by the
attackers. Some of these risks and issues may include cryptanalysis attacks, malware attacks,
man in the middle attacks, data breaches, denial of service attacks, data leakage, data loss,
account hijacking, and cross site scripting attacks (Constantine, 2014). The data sets that are
involved in the Big Data tools are collected from various sources that vary in terms of
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volume, velocity, type, and structure. The implementation of the same security controls for
all the data categories is not possible which may enhance the probability of these attacks.
Suggestions & Recommendations
The integration of Big Data in the decision-making processes of the organization shall be
carried out in a series of phases. This will provide the organization to have short-term goals
and the chances of success will increase. There shall also be security tools and controls that
must be available. The amalgamation of logical, administrative, and technical controls shall
be done and these shall then be integrated with the Big Data tools. Such a practice will make
sure that the probability of these risks is brought down.
The integration of Big Data with the decision-making process shall involve detailed planning
and analysis in terms of the scope, schedule, resources, costs, risks, and quality. Decision
support systems are the information systems that are being commonly used in the
organizations in the present times. The integration of the Big Data tools with these systems
shall be done and there shall be compatibility and feasibility checks that must be performed
in advance.
It is also recommended that the use of encryption must always be done for all the data sets so
that access control is improved. The issues of data loss and leakage will also be avoided as a
result.
Conclusion
The integration of Big Data tools with the existing components and applications in a business
organization is necessary in the present scenario. There are various advantages that are
associated with Big Data collection and the same will be offered to the organization. The
accuracy of the business decisions will improve which will lead to enhanced cost-
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effectiveness for the organization. The primary phase that shall be involved in Big Data
implementation is the planning phase. It shall then be followed by the phases as tools &
technology stack, change management & implementation, data collection, moving, and
analysis. There are various ways in which the organization may use Big Data analytics. Some
of these may include prescriptive analysis, predictive analysis, and diagnostic analysis. There
may be certain challenges that the organization may face during the implementation and
integration of Big Data tools and platforms. These may include technical challenges,
resistance of the employees towards the changes, and information security & privacy issues.
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References
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doi: 10.1186/s40537-016-0048-1
Constantine, C. (2014). Big data: an information security context. Network Security, 2014(1),
18-19. doi: 10.1016/s1353-4858(14)70010-8
Daki, H., El Hannani, A., Aqqal, A., Haidine, A., & Dahbi, A. (2017). Big Data management
in smart grid: concepts, requirements and implementation. Journal Of Big Data, 4(1).
doi: 10.1186/s40537-017-0070-y
Dhar, V. (2014). Why Big Data = Big Deal. Big Data, 2(2), 55-56. doi:
10.1089/big.2014.1522
Dumbill, E. (2013). Making Sense of Big Data. Big Data, 1(1), 1-2. doi:
10.1089/big.2012.1503
Garcia, S., Ramirez-Gallego, S., Luengo, J., Benitez, J., & Herrera, F. (2016). Big data
preprocessing: methods and prospects. Big Data Analytics, 1(1). doi: 10.1186/s41044-
016-0014-0
Hussain, A., & Roy, A. (2016). The emerging era of Big Data Analytics. Big Data Analytics,
1(1). doi: 10.1186/s41044-016-0004-2
Kaur, H. (2018). Big Data in Cloud Computing Benefits and Challenges. International
Journal Of Computer Sciences And Engineering, 6(6), 1069-1071. doi:
10.26438/ijcse/v6i6.10691071
Kumar, S., & Singh, M. (2019). Big data analytics for healthcare industry: impact,
applications, and tools. Big Data Mining And Analytics, 2(1), 48-57. doi:
10.26599/bdma.2018.9020031
Sethi, R. (2015). Analysis and Application of Data Mining by the Implementation of Big
Data. International Journal Of Computer Applications, 128(2), 45-47. doi:
10.5120/ijca2015906456
Yu, Y. (2017). Introduction: Special issue on computational intelligence methods for big data
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