Analyzing the Strategic Impact of Data Handling and BI Techniques

This report examines the strategic impact of data handling and business intelligence, covering data warehousing, data mining, and business intelligence tools. It highlights the importance of data warehousing for managing and collaborating data, enabling better business insights. Data mining is presented as a process for extracting patterns from large datasets, while business intelligence focuses on evaluating data to support decision-making. The report discusses current trends such as AI-driven data warehousing, the use of single data warehouses, and advancements in data mining applications across various industries. Practical applications, like Amazon Web Services' use of data warehouses and American Express's fraud detection, are provided to illustrate the real-world impact. Essential skills for professionals in these fields include analytical, computer, and communication skills. The report concludes that data warehousing, data mining, and business intelligence are crucial for providing uniformity to data and supporting corporate decision-making. Desklib provides solved assignments.

DATA HANDLING AND
BUSINESS
INTELLIGENCE 1

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
CONCLUSION................................................................................................................................3
REFERENCES................................................................................................................................1

INTRODUCTION
Data handling refers to covers all exercise and strategy put in place to handle data
possessions and business intelligence describes the data investigation through software tools,
initially to monetize business data (Li, 2020). This report will highlight the concepts through
article related to the strategic impact of business intelligence utilization.
MAIN BODY
1. Research informed literature
According to the Amuthabala and Santhosh, (2019) it has been specified that data
warehousing is a procedure of managing and collaborating data from various sources in order to
provide consequential business insights. It is a way in which the data has been stored in
electronic medium and large amount of information stored by the business which is designed for
analysis instead of transaction processing. It is helpful in improving the efficiency and speed for
assessment of varied data sets makes easier for decision makers that will guide the business
strategies.
As per the view of business intelligence the El-Adaileh and Foster, (2019) states that It is a
technology focused process for evaluation of data and delivering practicable information that
helps the executives, workers and managers make appropriate business decisions.
However, the concept of data mining is described by Chamikara and et.al., (2020) as it is a
process of take out and determines patterns in huge data sets linking various methods at the
intersection of machine learning, catalogue system and facts or figures.
2. Knowledge and understanding of subject
There have been various improvements with the accumulation of the new competency
towards the concept of data warehousing. However, data warehousing technologies are still
limited with certain difficulties of implementation and utilization of traditional data warehouses.
The latest trends regarding the data warehousing includes the single data warehouse in which all
the data related to the firm are available within one service. It includes large scale server-less
data warehouse. The higher usage of SAAS promotes easy accessibility, security along with
worldwide connectivity. Recently, the artificial intelligence operations in the data warehousing
will also become an important learning algorithm will generate more accurate actionable insight

to make data operations more efficient (Shen and et.al., 2019). The latest trend also includes the
IT modernization services in most responsive manner.
Data mining are utilised in diversified areas, as it includes retail industry, financial data
analysis, telecommunication, biological data analysis and many more. It helps the firm to design
multidimensional data analysis and data mining. It involves the construction of data
warehouses which are based on the benefits of data mining. The latest trends through which this
concept has been evolving within the field such as application exploration, integration of data
mining with database system and data warehouse system, visual data mining, new methods of
mining complex data and many other related activities.
On the other hand the latest trends within the business intelligence are becoming more
and more main stream with the help of automation enhancement with the increase in popularity.
Collective business intelligence and collective operations makes it easier for users to find
insights both together as well as in alone manner. The latest trends includes as more
organizations are relying on the predictive business analytics, adoption of hyper computerization
and entrenched analytics will be adopted, it also plays vital role in development of human
intelligence and with the help of effective business intelligence technique the firms are able to
see more strict policies related to the data governance.
3. Analysis
Data warehouses and their tools are moving forward from the centre in which all the data
collects within a cloud based data warehousing. There are various essential data warehousing
tools that help to derive values form the collected data. The tools are:
 Xplenty: It is a cloud based data addition display place to create simple and visible data
pipelines of the data warehouse. It helps to collects the data in combined format. With the
help of Xplenty the firm is able to centralise all metrics and sales tools.
Oracle: It has already very much popular among the data warehousing platform which has been
build in order to provide business within reach and analytics to the users (No, Lee and Seong,
2018). It targets at enhancing the operations efficiency and optimizing the user experiences.

On the other hand data mining is also an activity that a business engages in order to find out
purposeful information from all sources of data. The tools that affect the data mining are
explained below:
 Rapid miner: It is a data science software platform provides an incorporated platform for
varied stages of data modelling includes data preparation, data cleansing, visualization
and many more. This is an open source structure and written tool in Java which shares
most popular within the data mining.
 Knime: It is an open source of data analysis platform helps an individual in order to build
and scale the data within no time frame. Their major aim is to prepare predictive
intelligence accessible to inexperienced users.
However, the business intelligence tools are the application software that are utilised in order
to recover, sort, filter, process and report the data from business intelligence solutions. The top
business intelligence tools are explained below:
 Spreadsheets: These are majorly utilised into Microsoft excel and web based
spreadsheets which provides front end user interface.
 OLAP tools: It is an online analytical processing which helps the users to appropriately
analyse the data from multiple sources in a multidimensional views according to the
views of the business.
Practical application and deployment
In order to describe the practical example of data warehousing, Amazon web service firm
is utilises data warehouses to manage transactions, understand the data and keep all data in
organised manner. The data warehousing helps the firm to make large amount of information
more usable. With the help of Amazon Redshift the firm is able to completely manage the
petabyte scale cloud based data warehouse product designed for huge scale data set analysis and
storage (Abdullah and et.al., 2020). It enables the firm to utilise the data for the business and
customers in most appropriate manner.
The early form of data mining is utilised by the American express in order to manage and
detecting the fraud and bringing merchants and customers closer to each other. With the help of
such services the company ensures the protection of customers finances which describes the

importance of monitoring against cyber attacks is dominant in nature. It enables the
establishment to find out the patterns and correlations within large data sets in order to predict
the final outcomes.
While the business intelligence technique has been utilised in various companies in which
Microsoft is one of them which provides fast and accurate reporting to the firm, provide valuable
business insights, competitive analysis, increases customer satisfaction and many other benefits
to the firm.
Skills for professional practices
The practices that are related to the data warehousing, data mining as well as business
intelligence requires certain skills within an individual is great analytical, computer related as
well as effective communication skills. The person should have high amount of experience with
ETL tools and techniques along with working knowledge of structured query language and other
reporting techniques. Apart from this, the other kind of professional skills that are highly
required within an individual are include excellent research and problem solving abilities,
appropriate degree in relevant field, work experience, experience with data and architecture.
CONCLUSION
From the above report it has been concluded that data warehousing, mining as well as
business intelligence are highly important as it provides uniformity to all gathered data which
makes it simpler for corporate decision makers to evaluate and share data insights in most
appropriate manner.

REFERENCES
Books and journal
Abdullah, A.H., and et.al., 2020. Using Active Learning with Smart Board to Enhance Primary
School Students' Higher Order Thinking Skills in Data Handling. Universal Journal of
Educational Research, 8(10). pp.4421-4432.
Amuthabala, P. and Santhosh, R., 2019. Robust analysis and optimization of a novel efficient
quality assurance model in data warehousing. Computers & Electrical Engineering, 74.
pp.233-244.
Chamikara, M.A.P., and et.al., 2020. Efficient privacy preservation of big data for accurate data
mining. Information Sciences, 527. pp.420-443.
El-Adaileh, N.A. and Foster, S., 2019. Successful business intelligence implementation: a
systematic literature review. Journal of Work-Applied Management.
Li, Z., 2020. Geospatial big data handling with high performance computing: Current approaches
and future directions. In High Performance Computing for Geospatial Applications (pp.
53-76). Springer, Cham.
No, Y.G., Lee, C. and Seong, P.H., 2018. Development of a prediction method for SAMG entry
time in NPPs using the extended group method of data handling (GMDH)
model. Annals of Nuclear Energy, 121. pp.552-566.
Shen, C., and et.al., 2019. Group method of data handling (GMDH) lithology identification
based on wavelet analysis and dimensionality reduction as well log data pre-processing
techniques. Energies, 12(8). p.1509.
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