Term 1 Group 2: Current Trends in Business Intelligence Report

This report delves into the dynamic realm of business intelligence, examining current trends that are shaping the landscape of data-driven decision-making. It provides a detailed overview of the evolving trends in business intelligence, highlighting the increasing importance of artificial intelligence and data security. The report also explores the advancements in data warehousing, focusing on column-based storage and mixed workload systems. Furthermore, it investigates the latest trends in data mining, including multimedia and ubiquitous data mining techniques. The principles of analytical software are also discussed, providing a comprehensive understanding of the subject. The report concludes with an analysis of a related journal, evaluating its strengths and weaknesses, and emphasizing the necessity of market research and analysis in the ever-changing business environment. The report is a valuable resource for students on Desklib.

Data Handling &
Business intelligence
Level 5 Term1
Group2

Current trend in
Business Intelligence
Table of Contents
INTRODUCTION................................................................................................................................3
MAIN BODY.......................................................................................................................................3
Current trends in business intelligence ...........................................................................................3
Current trends in data Warehousing ................................................................................................3
Current trends in data mining..........................................................................................................4
Principles for analytical software ...................................................................................................5
About the journal.............................................................................................................................5
CONCLUSION....................................................................................................................................6
REFERENCES.....................................................................................................................................7
Books & Journal:.............................................................................................................................7

INTRODUCTION
In business, use of technology is increasing at rapid scale and functions and operations of
business are carried out digitally (Yang, 2020). Business intelligence is defined as strategies &
technologies that is utilised by businesses to make data driven decisions. Main aim of this report is
to critically understand the concept of technology & business intelligence. This report comprises
of current trends ion data warehousing, business intelligence & data mining and understanding of
concept of principles by accessing to analytical software.
MAIN BODY
Current trends in business intelligence
Business intelligence has been evolving with period of time. In this fast changing
technological world, it is becoming crucial to follow market trends so that objectives are achieved
in manner. So, there are various types of current trends in business intelligence which are described
below-
Artificial intelligence-
It is being considered as most hot and leading trends of all time. This is because concept of
AI is developing at rapid scale (Sun, 2018). Moreover, AI with engineering in business integration
leads to strengthening the security of business at higher scale. Most of organisation is moving
towards this trends & improving their level of technology to deal with competition.
Data security-
This is considered as another business intelligence trends in which data as well as
information are being at top priority for businesses. It is because there is regular improvement of
security in regards with data security. Moreover, implementation of privacy regulations such as
General Data Protection Regulation in EU & CCPA ( California Consumer Privacy Act) are
responsible for building blocks for protecting of data security as well as users personal credentials.
Data security is also improving their layers of security at higher scale & it is becoming necessary to
cope up with these concepts (Sriramoju, 2017).
Current trends in data Warehousing
Data warehousing are defined as process of collecting & managing data from different
sources to provide meaningful information. It is essential to be aware about data warehousing in
business so that credentials are stored & used in most efficient manner.

Column based storage is on rise- Storing of data is becoming crucial & which increases
their way storage capacity in fast way. This current trend of column based storage is rising as it help
businesses to store data in systematic order and response top query in fast response time. So, it is
beneficial for entity to use this column based storage in appropriate way.
Mixed workload-
In data warehousing, there are various types of workloads such as basic reporting, online
analytical processing, continuous/ real time load, data mining, are being used as mixed work
system (Sathiyamoorthi, 2017). Furthermore, it is also seen that avoiding them leads to various
types of problems such as low performance, sustainability issues, increased administration costs
etc. that can occur & leads to negative decisions making. With this mixed workload, it is easy for
businesses to achieve better productivity workplace.
Data warehousing Automation
Data warehousing is wider concept and that is managed by IT departments and subordinates
in organisations. It took time to establish a data warehouse which also leads top cost expensive
procedure & time intensive. With help of DWA, it reduce and minimises the relying criteria on IT
staff. Also, capability of system to counter changing business conditions & market dynamics in less
time makes DWA a valuable business tool. It enables business users to extract latest data from their
BI tools and take accurate and decisions within time interval.
Current trends in data mining
Data mining is to be defined as approach which is used to extract data from several sources
& then use them in systematic manner. Moreover, with help of different algorithm, data mining is
also done in simpler manner. In this digital era, it is crucial to make assure that in this digital era,
this there are various ways through data can be generated like through social media tools &
platforms, primary and secondary sources etc. So, there are different current trends which are
further discussed as follows-
Multimedia data mining-
It is being viewed as most used approach in which data mining is done through various
ways like video, graphics, text, images, audio etc. With help of this method, it is easy to gather
informations as well as data in specific manner (Sahani, 2018).
Ubiquitous data mining-
It is another current trend of data mining in which gathering of information through mobile
devices, privacy cost etc. It is also an effective approach of data mining which is being at trending
spot.
Distributed data mining-

In this method, data is being stored in different organisations, locations, and it is essential
to be aware about these distributed locations. So, sophisticated algorithms are being used to extract
data from various locations or places (Peral, 2017).
Geographic data mining-
It is new trending trend in which information & data is being collected through
environmental, geographical data which comprise of data gathered from space (Ptiček, 2019).
Principles for analytical software
It is essential to generate information about principles of analytical software so that it is easy
to use software and its features appropriately. So, there are various principles which are considered
& are given as follows-
Domain needs to be well represented and examined.
Functionality of analytical software should be properly defined.
The modules which are linked with partitioned in way that depicts hierarchy of desired
system.
About the journal
This journal is about impact of informational technology and trends of business intelligence
that impact upon organisational performance as well as their working pattern. Through analysing
this journal gives brief information about how business can adjust according to changing
circumstances within market due to trends, behaviour etc.
Strength of author Lim, Y.
Main strength if author is that presence of good written skills and deep knowledge about
concept of business intelligence.
Another strength is that proper clarity is being provided about concept and work which
gives deep insight of subject.
Weakness
Weakness of author is that due to extensive market research information, to collect
appropriate information is difficult.

CONCLUSION
After a brief analysis of above report, it has been concluded that it is essential to be aware
about business as well as technology so that goals are achieved efficiently. So, discussions has been
made about current trends in data warehousing, business intelligence, data mining and principle of
analytical software. Thus, it has been evaluated that in this flexible market, it is necessary for
organisation to conduct proper market research and analysis so that decisions making is being
done in correct way.

REFERENCES
Books & Journal:
Kathiravelu, 2016, September. A dynamic data warehousing platform for creating and accessing
biomedical data lakes. In VLDB Workshop on Data Management and Analytics for Medicine
and Healthcare (pp. 101-120). Springer, Cham.
Larson, 2016. A review and future direction of agile, business intelligence, analytics and data
science.International Journal of Information Management, 36(5), pp.700-710.
Lautenbach, 2017. Factors influencing business intelligence and analytics usage extent in South
African organisations.South African Journal of Business Management, 48(3), pp.23-33.
Pang, 2020. Applying Software Engineering Design Principles to Agile Architecture. In Software
Engineering for Agile Application Development (pp. 82-108). IGI Global.
Peral, 2017. Application of data mining techniques to identify relevant key performance
indicators.Computer Standards & Interfaces,54, pp.76-85.
Ptiček, 2019. The potential of semantic paradigm in warehousing of big data.Automatika,60(4),
pp.393-403.

Sahani, 2018. Classification of intrusion detection using data mining techniques. In Progress in
computing, analytics and networking (pp. 753-764). Springer, Singapore.
Sathiyamoorthi, 2017. Fundamentals of Data Mining and Data Warehousing. In Advancing Cloud
Database Systems and Capacity Planning With Dynamic Applications (pp. 1-26). IGI
Global.
Sriramoju, 2017. Opportunities and security implications of big data mining. International Journal
of Research in Science and Engineering, 3(6), pp.44-58.
Sun, 2018. Big data analytics services for enhancing business intelligence. Journal of Computer
Information Systems, 58(2), pp.162-169.
Yang, 2020. An analytical C3 continuous tool path corner smoothing algorithm for 6R robot
manipulator.Robotics and Computer-Integrated Manufacturing, 64, p.101947.