Data Handling, Business Intelligence, and Predictive Analytics Report

This report delves into the critical aspects of data handling and business intelligence, emphasizing their significance in today's business landscape. It explores current trends in data warehousing, business intelligence, and data mining, offering a comprehensive evaluation of their impact. The report highlights the customer-centric approach prevalent in modern markets, where businesses strive to understand and meet customer expectations. It also examines the role of predictive analytics software in leveraging data to gain a competitive edge, covering techniques like regression analysis and automated segmentation. The report uses Waitrose & Partners as a case study, and analyzes the application of predictive analytics in various sectors, including marketing, sales, and risk management. The conclusion underscores the importance of data-driven decision-making for business success.

Data handling and business
inteligence

Table of Contents
Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
Identify and critically evaluate the current trends in data warehousing, business intelligence
and data mining......................................................................................................................1
Demonstrate a comprehensive knowledge and systematic understanding of essential concepts
and principles by using predictive analytic software.............................................................3
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................6

INTRODUCTION
Data handling and the business intelligence is a set of processes, technologies and the
architectures that all collectively transferred the raw material into the consequential information
that is much important for the business. Data handling is the overall managed process under
which the different needed and liable data will get managed and ensures the data must get
secured and safe. It helps when the business will develops some of the policies and procedure for
the welfare of the business (Bordeleau, 2018). On the other hand the business intelligence is
software or the tool that makes the valuable and profitable output from the collected data. The
major importance of business intelligence is that it helps the managers to make effective decision
making by cost cutting, identifying the opportunity etc. The report is based on the Waitrose and
partners and it is the British supermarket and they were selling the groceries and one of the
largest employee owned retailers and having the headquartered in Bracknell, UK. This report
includes the current trends of the data warehousing, business intelligence and data mining with
the comprehensive knowledge of essential concepts by using predictive analytic software.
MAIN BODY
Identify and critically evaluate the current trends in data warehousing, business intelligence and
data mining.
As per the viewpoint of Ajay Bhargava, 2020, the today’s market is more follow the
customer centric approach as the market has more competitors and all wants to gain the better
profit and for that they all try to grab the attention of the customers. The enterprises were going
to measure the expectations of the customers as it is the ever increasing advancement that is
exerted on daily basis. It changes the buying behaviour of the customer and with that the
company will get influence the choices of customers on constant basis in the multidimensional in
personality. To understand the better aspect of the customer is always be the drawback of the
company and it is the strength as well for those that has meet the requirements of the customers.
The companies always try to enhance the profit, reduces the production cost, decreases the risk,
regulate the legal and other regulation as well to effectively change and serve best to the
customers (Kasemsap, 2018). The companies were try to collect much data as that is possible
and that influence the business processes, customers and the different products and services.
Thus to witness the timely changes and the exponential data and the upsurge of the data the
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companies must collect the diversified range of data. Thus the business intelligence life cycle
must get used to reduce the quality of data and integration and there are some of the trends that
develops the industry are as defined below:
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 Hardware: It is the trends that include the virtualisation, mobile devises, appliances that
helps to interact with the systems.
 Software: It is the basis that is based on the trends a like visualization techniques, XML
standards etc.
 Convergence: It includes the different models as like SaaS, SOA and other technologies
as well.
 Consolidation: It includes the different tools as like for data warehousing and for
business intelligence and have vendor within the business.
 Other: There are some of the different alignment that is more strongly set to develop the
quality of DW and BI.
As per the viewpoint of Rob Marvin, 2015, the business intelligence has the biggest trends and
there are some envision as well as like evolution of self services as it was not delivered as from
the tools but it has been transferred from the interface and the apps (Mitrovic, 2020). It brings
more advancement in the customaries visualisation and emphasis the capabilities of the business.
The business will move forward from the traditional visuals and more forwards to the browser
based application that could be easily used in the mobile devices or from the tablets from which
the customer get more easiness. If the screen is in the hand than it is more worth then on the desk
as it supports the users and they get the best experiences while moving from one place to other.
In that all were using the same information with more efficiency in timely manner and also
imparts more easiness as well.
Demonstrate a comprehensive knowledge and systematic understanding of essential concepts
and principles by using predictive analytic software.
As per the viewpoint of Rob Marvin, 2016, Predictive analytic is being the practical
outcome of the business intelligence and the data. In today’s scenario the business will get the
rank on the basis of the loyal customers, social listening, real time app, performance of the
product, market etc. In this predictive analytic is the way through which all the information will
get leverage and helps to gain the tangible heights that furthermore helps to stay in lead in the
heavy competition. The company must use that is various manner as from marketing and mining
of the data to get apply it in machine learning in order to attain the optimise pattern. Basically it
is the certain process and its learning will get generated from the computers to give the new sight
to the business (Olsen, 2018). Company must use the predictive analytics to save the prior time,
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cost, risk, gain better competitive edge as well. Predictive analysis is the bunch of analysis that
gets rolled one into another.
The major technique behind this is regression analysis that has induces the prediction
related to the correlated variables that is used to prove or disprove the assumptions. It is all about
to recognise the follow up of the business to project the probability of success (Singh, 2018). The
better uses of predictive analysis are to reduce the complexity from the business. As like
predictive scoring helps to know the priority of the business and helps to make the lead which is
based on the action that are taken by the business. It is the mathematical dimension that is more
speculate and determine by a lot of experiments. The models will get identified that are acquired
by the business as from where the sales will get maximised with the existing customers. The
business will tries to cover the uncovered part by which the initiative has been taken by the
company to gain more receptive of sales. The automated segmentation has been induced by
which the company could lead the personalised message to the customers and in that the attribute
of the business will get transferred. The sales and marketing will make the direct communication
with the customer by transferring the relevant message.
The predictive analytics has the vast impact on the industrial scale with the usages of
Internet of Things (IoT). For example the Google also uses the ML algorithms and with that they
manage the predictive maintenance and provide the cloud to the customers and add the better
infrastructure to the public. It has the positive change in the retail industry as well and helps to
make the financial transaction free from risk and fraud (Wani, 2018). It is basically the mapping
of the artificial brain and it generates the endless possibilities in the market with the parallel
gradation in the technological advancement.
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CONCLUSION
It has been concluded from the above report that the business intelligence and the data
handling has been followed by all the companies to make the better judgements that has positive
impact on the performance and decision making process of the company. It is also needed that
the data of all the ongoing activities that are performed within the business will get collected in
beneficiary manner by which the business must analyse their performances by which they get
better achievements that lead more success and growth.
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REFERENCES
Books and Journals
Bordeleau, F.E. and et. al., 2018, January. Business Intelligence in Industry 4.0: State of the art
and research opportunities. In Proceedings of the 51st Hawaii International Conference
on System Sciences.
Kasemsap, K., 2018. Multifaceted applications of data mining, business intelligence, and
knowledge management. In Intelligent Systems: Concepts, Methodologies, Tools, and
Applications (pp. 810-825). IGI Global.
Mitrovic, S., 2020. Adapting of international practices of using business-intelligence to the
economic analysis in Russia. In Digital Transformation of the Economy: Challenges,
Trends and New Opportunities (pp. 129-139). Springer, Cham.
Olsen, G. and et. al., ServiceSource International Inc, 2018. Inbound and outbound data
handling for recurring revenue asset management. U.S. Patent 10,078,677.
Singh, V. and Verma, N.K., 2018. Deep learning architecture for high-level feature generation
using stacked auto encoder for business intelligence. In Complex systems: solutions and
challenges in economics, management and engineering (pp. 269-283). Springer, Cham.
Wani, M.A. and Jabin, S., 2018. Big data: issues, challenges, and techniques in business
intelligence. In Big data analytics (pp. 613-628). Springer, Singapore.
ONLINE
Bhargava. A., 2020. Trends in data warehousing and business intelligence [Online] Available
through: < http://www.b-eye-network.com/view/8374 >.
Marvin. R., 2015. 10 business intelligence trends in 2016 [Online] Available through:
<https://uk.pcmag.com/cloud-services/73741/10-business-intelligence-trends-for-2016>.
Marvin. R., 2016. Predictive analytics, big data, and how to make them work for you 2016
[Online] Available through: <https://in.pcmag.com/salesforcecom-professional-
edition/105182/predictive-analytics-big-data-and-how-to-make-them-work-for-you>.
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