Business Intelligence: Current Trends, Tools, and Applications

This article discusses the current trends in business intelligence, including data sources, data exploration, data mining, optimization, and decisions. It also covers the tools and practical applications of business intelligence in various industries, such as customer relationship management, healthcare, education, and more. Additionally, the article reflects on the strategic impact of business intelligence utilization through research article findings.

Business intelligence

Table of Contents
INTRODUCTION...........................................................................................................................1
Current trends...................................................................................................................................1
Business Intelligence..............................................................................................................1
Data sources – operational data..............................................................................................1
Data Warehouse/ Data mart....................................................................................................1
Data exploration – statistical analysis and visualization........................................................2
Data mining............................................................................................................................2
Optimization...........................................................................................................................2
Decisions................................................................................................................................2
Predictive Analytic Software concepts and principles.....................................................................3
Тооls and Practical Applications of Business Intelligence....................................................3
Тооls and Practical Applications of Data sources – operational data....................................3
Тооls and Practical Applications of Data Warehouse/ Data mart .........................................3
Тооls and Practical Applications of Data exploration – statistical analysis and visualization3
Тооls and Practical Applications of Data mining...................................................................3
Тооls and Practical Applications of Optimization.................................................................4
Тооls and Practical Applications of Decisions.......................................................................4
Realizing the strategic impact of business intelligence utilization..................................................4
Research Article findings.......................................................................................................4
CONCLUSION................................................................................................................................5
References:.......................................................................................................................................6

INTRODUCTION
The following discussion is based on the current trends which includes concept of
business intelligence and data sources along with the data warehouse and data exploration which
is followed by the data mining and optimization and also the decisions. Discussion is also made
on the predictive analytic software concepts and principles which includes the tools and practical
applications of all these current trends in an in depth manner. Moreover, realizing the strategic
impact of business intelligence utilization which includes the research article findings are also
discussed with proper conclusion.
Current trends
Business Intelligence
It is defined as the technologies used for data analysis by the organization which provides
historical and current along with the predictions of the operations of the business. It is a very
broader term which encompasses the performance benchmarking and predictive analysis. Trends
includes artificial intelligence, data security, data discovery, SAAS business intelligence,
collaborative business intelligence, data automation, predictive and prescriptive data analytics
tools, mobile business intelligence, embedded analysis and real time data and analytics
(Bordeleau, Mosconi and Santa-Eulalia, 2018).
Data sources – operational data
As per the given case study, Malaysian Public Listed Companies, it is defined as the
operational data store which is used in order to report operations and even as the sources of data
for the company's data warehouse. Trends includes smarter, faster and more responsible artificial
intelligence, decline of the dashboard, decision intelligence, X analytics, augmented data
management, cloud is a given, data and analytics worlds collide, data market places and
exchanges, block chain in data and analytics and form of relationship which values the
foundation of data and analytics.
Data Warehouse/ Data mart
As per the given case study, Malaysian Public Listed Companies, it is defined as the
structure which is used to retrieve data of the client. Data mart is a subset of data warehouse.
Trends includes better bridging of clouds and on premise, more open than even before,
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empowering business users to drive into data, end to end from start to finish, complex data marts,
column based storage, mixed workloads, data warehouse automation and cloud centric data
warehouse (Tripathi, Bagga and Aggarwal, 2020).
Data exploration – statistical analysis and visualization
As per the given case study, Malaysian Public Listed Companies, it is defined as the first
step of conducting the data analysis with the help of data visualization and other tools in a
statistical manner such as size and quantity to explore the nature of data. Trends includes
automation and democratization along with the user experience and analytics as a core business
function which is followed by the accessibility and future of analytics.
Data mining
As per the given case study, Malaysian Public Listed Companies, it is defined as the
process of retrieving and recognizing the large data sets which involves machine learning and
statistics or database systems. Trends includes applications exploration, integration of data,
visual data mining, biological data mining, software engineering, web mining, distributed data
mining, real time data mining, multi database data mining and privacy protection in data mining
(Richards, Yeoh, Chong and Popovič, 2019).
Optimization
As per the given case study, Malaysian Public Listed Companies, it is defined as the
process which uses different methods of data quality in order to maximise the speed and
comprehensiveness of data extraction and analysis of it for use. Trends includes data as a service,
responsible and smarter artificial intelligence, predictive analysis, quantum computing, edge
computing, natural language processing, hybrid clouds, dark data, data fabric, XOps and
summing it up.
Decisions
As per the given case study, Malaysian Public Listed Companies, it is defined as the
using of factual data and metrics so that they can guide to the strategic business decision making
in terms to fulfil the goals and objectives of the company. Trends includes contending with
spiralling data volumes and adopting a BizOps approach along with the employing SRE models
(Sun, Sun and Strang, 2018).
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Predictive Analytic Software concepts and principles
Тооls and Practical Applications of Business Intelligence
As per the given case study, Malaysian Public Listed Companies, business intelligence
tools includes data pine, clear analytics, SAP business objects, SAS business intelligence, domo,
metric insights, Zoho analytics, micro strategy, good data, IBM cognos analytics, QLIKVIEW
and yellowfin business intelligence. Business intelligence practical applications includes
customer relationship management, health care and wellness, education, retail, depends of the
area Banking, financial service, insurance, manufacturing, telecom and government (Liang and
Liu, 2018).
Тооls and Practical Applications of Data sources – operational data
As per the given case study, Malaysian Public Listed Companies, Data sources –
operational data tools includes oracle and IMS along with the SAP and CICS which is followed
by the flat files and management reports and also the web based applications. Data sources –
operational data practical applications includes that it provides current data with cleanliness from
more than one sources. It also provides the data integrated from more than one systems.
Тооls and Practical Applications of Data Warehouse/ Data mart
As per the given case study, Malaysian Public Listed Companies, Data Warehouse/ Data
mart tools includes redshift, BigQuery, Panoply, stitch, blendo, fivetran, tableau online, qlik,
chartio, looker, zapier and IFTTT. Data Warehouse/ Data mart practical applications includes
that it identifies risk along with the management of directions, it conduct performance analysis, it
tracks performance and provides feedback as well (Ain, Vaia, DeLone and Waheed, 2019).
Тооls and Practical Applications of Data exploration – statistical analysis and visualization
As per the given case study, Malaysian Public Listed Companies, Data exploration –
statistical analysis and visualization tools includes matplotlib, scikit learn, plotly, seaborn,
pandas, D3.js, bokeh, altair, yellow brick, folium and tableau. Data exploration – statistical
analysis and visualization practical applications includes that it assesses the relationship among
variables in the dataset and provides bigger picture to get the insights faster.
Тооls and Practical Applications of Data mining
As per the given case study, Malaysian Public Listed Companies, Data mining tools
includes rapid miner, oracle data mining, IBM SPSS modeler, knime, python, orange, kaggle,
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rattle, weka and teradata. Data mining practical applications includes future health care, market
basket analysis, education, manufacturing engineering, customer relationship management, fraud
detection, intrusion detection, lie detection, customer segmentation, financial banking, corporate
surveillance, research analysis, criminal investigation and bio informatics (Caseiro and
Coelho, 2019).
Тооls and Practical Applications of Optimization
As per the given case study, Malaysian Public Listed Companies, Optimization tools
includes page speed insights, hotjar, google search console, screaming frog, google optimise,
Gtmetrix and wave. Optimization practical applications includes transportation and production
planning along with the design and data fitting.
Тооls and Practical Applications of Decisions
As per the given case study, Malaysian Public Listed Companies, Decisions tools
includes google data studio, tableau, Microsoft power business intelligence, limespot, nosto,
dynamic yield, payhelm, google analytics, trendalytics, origin, garrett wade and fore ladies golf.
Decisions practical applications includes E-commerce sites are the biggest application of data
drive decision making such as Amazon (El-Adaileh and Foster, 2019).
Realizing the strategic impact of business intelligence utilization
Research Article findings
Article on “Realizing the strategic impact of business intelligence utilization” describes
about the concept of business intelligence with proper introduction. After that, utilization of
business intelligence is discussed for better understanding its application in the actual world. It
also explains the striving towards business sustainability with three strategic impact such as
enhancing economic performance and strengthening environmental performance along with the
reinforcing social performance with an appropriate conclusion by reflecting on the dynamic
business environment.
Impact of business intelligence on current business as per the case study by using the
current trends and tools are such that it helped in identifying ways to increase profit by analysing
customer behaviour so that the comparison of data with competitors can be performed in order to
track the performance and optimizing operations. This is done to predict success and spot market
trends so that the discovery of issues or problems can also be performed.
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CONCLUSION
It is concluded that the business intelligence is an important concept to learn and study so
that its applications can be applied in the real world organizations. Therefore, it is important and
essential to analyse and examine the current trends including data sources – operational data,
data warehouse/ data mart, data exploration – statistical analysis and visualization data mining,
optimization and decisions. It is necessary and significant to determine and gain the knowledge
about the predictive analytic software concepts and principles including the tools and practical
applications of the current trends discussed above. It is crucial and imperative to assess and
investigate about the strategic impact of business intelligence utilization including research
article findings. Hence, this report covers all such areas in order top better understand the
conception of business intelligence.
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References:
Books and Journals
Ain, N., Vaia, G., DeLone, W.H. and Waheed, M., 2019. Two decades of research on business
intelligence system adoption, utilization and success–A systematic literature
review. Decision Support Systems, 125, p.113113.
Bordeleau, F.E., Mosconi, E. and Santa-Eulalia, L.A., 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.
Caseiro, N. and Coelho, A., 2019. The influence of Business Intelligence capacity, network
learning and innovativeness on startups performance. Journal of Innovation &
Knowledge. 4(3). pp.139-145.
El-Adaileh, N.A. and Foster, S., 2019. Successful business intelligence implementation: a
systematic literature review. Journal of Work-Applied Management.
Liang, T.P. and Liu, Y.H., 2018. Research landscape of business intelligence and big data
analytics: A bibliometrics study. Expert Systems with Applications, 111, pp.2-10.
Richards, G., Yeoh, W., Chong, A.Y.L. and Popovič, A., 2019. Business intelligence
effectiveness and corporate performance management: an empirical analysis. Journal of
Computer Information Systems. 59(2). pp.188-196.
Sun, Z., Sun, L. and Strang, K., 2018. Big data analytics services for enhancing business
intelligence. Journal of Computer Information Systems. 58(2). pp.162-169.
Tripathi, A., Bagga, T. and Aggarwal, R.K., 2020. Strategic impact of business intelligence: A
review of literature. Prabandhan: Indian Journal of Management. 13(3). pp.35-48.
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