Enterprise Wide Real-Time Data Analytics Adoption

This document discusses the adoption planning and benefits of real-time data analytics in business. It also explains the application of stream processing and complex event processing. Additionally, it provides recommendations for developing an interactive dashboard for business intelligence.

Enterprise Wide Real-Time Data Analytics
Adoption
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Contents
INTRODUCTION...........................................................................................................................3
TASK 1............................................................................................................................................3
Discuss about the data analytics adoption planning....................................................................3
Understand that how marketplace dynamic and business motivation beneficial........................3
How will extract, transform and Load pipeline, explain how to apply scenario.........................4
Describe about the advantage and disadvantage of real time data analysis, how it will happen
during cloud computing and big data adaptation in online analytic engine................................4
TASK 2............................................................................................................................................5
Evaluate the data processing within enterprise data analytics strategy in memory data grids....5
How it will apply stream processing and deals with taxation.....................................................5
Describe about the complex event processing.............................................................................6
TASK 3............................................................................................................................................7
Recommendation for development of interactive dashboard that can perform business
intelligence for user organization................................................................................................7
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................8
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INTRODUCTION
Data analytic is based on the matrix which mainly consists of qualitative as well as
quantitative process which mainly used for improving real time processing. In order to extract
data and also classified into specific way, on the basis of behaviour, nature and pattern as
fulfilling the requirement of business effectively. On the other hand, Data science is concept that
mainly developing a new model and algorithm which supported by using programming
language. This report will discuss about the real time data analysis where how it will motivate
the business in term of growth and development. Furthermore, it will use analytical approach to
extract and transform according to the requirement of business.
TASK 1
Discuss about the data analytics adoption planning.
Big data initiative are becoming strategic in nature that should be based on business driven.
The adoption of big data planning helps for transforming, but it is more often innovative.
Transformation activities are planned before adopting of analytics because these are typically
low-risk designed to deliver the effectiveness (Chong and Deshinta, 2019). During adoption
planning, it should be considered the important points that help for increasing the power of big
data capabilities. It enable this sort of changes.
 First of all, it has been identified the nature of big data and its power. It is mainly
addressed the issues that need to be considered in planning.
 It will be tracking the provenance of large amount of data set from its procurement. In
order to utilise new requirement of organization.
 Big data planning even open additional opportunities to assume moving beyond on-
premise environment.
Understand that how marketplace dynamic and business motivation beneficial.
Big data analytic is important concept that mainly support for marketplace, directing the
organizational decision making. It help for growing the business while accelerating the
deployment of big data. The primary aim is to develop the critical insight that provide
competitive advantage (Cruz-Jesus, Pinheiro and Oliveira, 2019). Big data analytics can offer the
best deals for adopting modern technologies. To capture a large amount of information into
single data sets. On the other hand, it can be defined as ability of organization to effectively use
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big data for capture, store and analyse data in proper manner. That’s why, it has been motivated
the business to achieve their significant goal and objective.
How will extract, transform and Load pipeline, explain how to apply scenario.
By applying the technique to given scenario, it has found that obtained large amount of
structured from online and offline medium in order to predict the future sales. The process is the
most important thing where it can be defined how data should be collected, transformed and
loaded. As per scenario, the organization will use database with strong GPU performance, CPU
and other platform (Mahmood and Panwar, 2019). These are well suited for extract load and
transform pipeline. It useful for merging large filed with drifting data schema.
The data processes to be accumulating while performing different task during data
extraction, transformation and loading.
 Select or transfer large amount of data from file transfer
 Read each data file and keep process, loading that data to be exit in database
environment.
 A series of stored procedure and view transform raw data while storing into other table.
 Loaded the data into production table
 Indices are estimated or calculated data.
 Retain the source of data if in case pipeline failed, it automatically migrated with data
storage.
Describe about the advantage and disadvantage of real time data analysis, how it will happen
during cloud computing and big data adaptation in online analytic engine.
Having a lot of information or data into organization become consider as important part,
which able to store, analyse and visualise its real time processing. There are many organization
adopt the modern technologies such as cloud computing and other type of online analytical
engine like Google analytics. These are helping for organization to increase the efficiency and
performance of business in global marketplace (Phillips, Wren and McKniff, 2019). In this way,
it can easily capture, record data and represent into visualization format.
In this way, it is the best way to identify the overall sales and production record of product
in global marketplace. Furthermore, it can be identified the advantage as well as disadvantage of
real time data analysis.
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Advantage
 It is instantly identify the error and bug within organization.
 Immediately implement the new strategies for increasing competitive advantage.
 It could be better to increase the higher conversion rate.
Disadvantage
 It will require special computer power during real time analysis.
 The real time action is affecting on the culture or environment.
TASK 2
Evaluate the data processing within enterprise data analytics strategy in memory data grids.
Nowadays, Enterprise require to make fast decision in term of respond and also changing
the market condition. It also helps for maintaining and controlling competitive edge. The
explosion of data which may require to analyse and find trends insight business challenges. In
organization, the memory data grid have been assumed as value in storing fast changing
application data and also scaling overall performance (Sleep, Hull and Gooner, 2019).
Recently, it adopt as data analytic strategy which easily integrated into grid to achieve
powerful and analysis. For example- enterprise use IMDG for purpose of store, analyse incoming
stream of market. In this way, it can be generated the new data as an alerts and strategies for
optimising financial operations. In order to minimise capabilities, simply provide competitive
advantage.
On the other hand, in Memory data grid enable to operational intelligence that are quickly
analysed. It also supported for distributed computing and streaming network, influencing overall
sales and production data.
How it will apply stream processing and deals with taxation.
Streaming processing is based on the concept which directly interact with other computing
and storage data. It is continuous process which perform different activities such as sensor
events, user activity and financial trades. In this way, it will easily create data as a series of event
over time (Verma and Chaurasia, 2019). Before executing stream process, data must require to
store within database system and application would query or compute as per requirements.
As per scenario, it has identified that percent and weather will change then system
automatically trigger as alter message or reaction. In this way, it can easily deals with taxation
and compiled with large amount of data set. Afterwards, it will try to create trigger for receiving
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an event from different streams. Furthermore, trigger can perform action to update, aggregate
statistics data into future reference.
Figure 1 Stream Processing
Describe about the complex event processing.
It is a set of technique or method for capturing, storing and analysing streams of data. They
can easily identify the threat and opportunities in real time processing. This method enable
system, application to respond against the trends, patterns, events during data analysis (Yaqoob
and et.al., 2019). It is to be considered as event stream processing which are typically associated
with search for complex pattern in the incoming data.
Complex event processing is used for business scenario in which large volume of event
occurring. For Example- it recognise the stock prices, which match against the pattern and also
decided whether to trigger a buy or sell in decision.
Figure 2 Complex Event processing
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TASK 3
Recommendation for development of interactive dashboard that can perform business
intelligence for user organization.
For developing an interactive dashboard that will recommend to implement the business
intelligence system. It is based on the computer sub system which intended to help in decision
making. In order to establish the communication through modern technologies, knowledge and
analytical model to resolve problem (Verma and Chaurasia, 2019). It has been suggested to use
BIS offer the potential for significantly improve overall strategic and operational performance.
Furthermore, it become helpful for user organization when they can easily access
information or data efficiently. Many organization engaged in the business data warehouse
which are putting more efforts ranged from combining multiple legacy system. In this way, it has
developed user interface tool for analysis report and generate in the visualise forms. The
interactive dashboard is underlying the structure that generate variety of reports. Business
intelligence system are visualisation and integration, typically maintain information flow through
graphic display (Verma and Chaurasia, 2019). It is also known as dashboard. It server as function
when reports key enterprise maintain performance data and handle the real time processing.
In perspective, it has been suggested that system become merely promoted by vendors,
consultants. Dashboard perform task by using business intelligence system that provide access of
powerful analytical system. It will be created the user-friendly environment which support
enterprise analysis and also integrated with decision-making.
CONCLUSION
From above discussion, Data analytics is a type of conceptual framework that contain both
qualitative as well as quantitative process which mainly used for improving real time processing.
In order to extract data and also classified into specific way, on the basis of behaviour, nature
and pattern as fulfilling the requirement of business effectively. It has summarised about the real
time data analysis where how it is motivating the business in term of growth and development.
Furthermore, it can be used the analytical approach to extract and transform according to the
requirement of business.
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REFERENCES
Book and Journals
Chong, F.K. and Deshinta, A.D., 2019. A Review of Data Analytics Adoption in Business
Industry. INTI JOURNAL. 2019(34).
Cruz-Jesus, F., Pinheiro, A. and Oliveira, T., 2019. Understanding CRM adoption stages:
Empirical analysis building on the TOE framework. Computers in Industry. 109. pp.1-13.
Mahmood, M.R. and Panwar, M.K., 2019, November. Real Time Data Analytics for Process
Safety Governance-Case Study. In Abu Dhabi International Petroleum Exhibition &
Conference. Society of Petroleum Engineers.
Phillips-Wren, G. and McKniff, S., 2019. Aligning Operational Benefits of Big Data Analytics
and Organizational Culture at WellSpan Health. In Aligning Business Strategies and
Analytics (pp. 115-131). Springer, Cham.
Sleep, S., Hulland, J. and Gooner, R.A., 2019. THE DATA HIERARCHY: factors influencing
the adoption and implementation of data-driven decision making. AMS Review. 9(3-4).
pp.230-248.
Verma, S. and Chaurasia, S., 2019. Understanding the Determinants of Big Data Analytics
Adoption. Information Resources Management Journal (IRMJ).32(3). pp.1-26.
Yaqoob, I. and et.al., 2019. The Role of Big Data Analytics in Industrial Internet of
Things. arXiv preprint arXiv:1904.05556.
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