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Introduction
This report is about current trends in business intelligence. The report will include ideas and
arguments that are being presented by the authors related to the topic. It has been seen that data
loses its value without being analysed and processed, and we need tools like Hadoop to develop
the data and discover answers to problems. The phenomenon known as "big data," which refers
to the amount, diversity, and velocity of data, has had an effect on corporate intelligence as
well as the use of information. In the realm of business intelligence, recent developments have
seen the emergence of new ideas such as quick analytics and data science. Integrating data,
having analytical skills, having high-quality content in business processes, and having a culture
that encourages decision-making are all components of business intelligence.
Business intelligence
Executives, managers, and employees may all benefit from business intelligence (BI), which
is the process of analyzing data and disseminating relevant, actionable information to aid in
strategic decision-making. Technology facilitates this procedure. The business intelligence
(BI) process involves gathering information from both internal IT systems and external sources,
cleaning and organizing it, running queries against it, and producing data visualizations, BI
dashboards, and reports for use by business users in operational decision making and strategic
planning. Because of this, companies may now provide analytics findings to their clientele.
(Jakhar, & Krishna, 2020).
It is reasonable to anticipate that effective business intelligence initiatives will result in
increased profits, simplified processes, and a competitive advantage over other businesses. The
toolset that business intelligence uses to get the job done includes a number of different
methods to data management and analysis, as well as technology that are used for analytics,
data management, and reporting (Lennerholt, van Laere, & Söderström, 2018).
Business intelligence (BI) is the process through which a company derives actionable insights
from its collected data. Among the various domains that may be analyzed using business
intelligence are those of competition, consumers, industries, commodities, and operations. A
study found that faster and more accurate reporting (76%), better market selection (75%),
greater quality customer service (54%), and higher firm revenues (42%) were the major
benefits of business intelligence (Ahmad et al., 2020).
Big data
Big data is distinguished by its volume, diversity, and velocity, which are the three primary
aspects that define it. The three variables represent large volumes of data, different distributions
of that data, and different speeds at which new data is produced. For example, a researcher by
the name of Allam was able to create around 400,000 rows per minute of live views connected
to the data. They consistently delivered in excess of one million reports for the purpose of big
data analysis. The capacity to give insight into customer transactions, product monitoring,
video storage, advertising, consumer interactions, revenue management, and investment are
just some of the ways in which businesses may profit from the analysis of large amounts of
data. Other potential benefits include: As a result of the analysis of large amounts of data,
decisions may be made in real time, and information may be exchanged throughout the various
levels of government (Omar, Minoufekr, & Plapper, 2019).
Big data analysis issues in business intelligence
Big data analysis involves a variety of problems, but the advantages to organizations of
exploiting this data to their advantage are evident. The primary challenges to big data analysis
were the lack of intelligent huge data sources, the absence of widely accessible real-time
analytic capabilities, and the insufficiency of network bandwidth for application execution.
Examined include interoperability, data fragmentation, inadequate availability, and the need to
tighten data protection and privacy regulations (Omar, Minoufekr, & Plapper, 2019).
It is challenging to deploy BI big-data exploration (BI-BDE) since it demands costly tools and
a great deal of computational capacity for analysis. Attackers continue to target Big Data due
to the possibility it provides to store vast quantities of heterogeneous, accumulated data from a
range of sources. Essential are data protection laws and various types of regulation with
regulatory requirements. In addition, while big data analytics is still in its infancy, there are no
strict criteria for safeguarding against the possible risks of collecting and analyzing enormous
volumes of data. The challenge lies in determining how best to establish standards for drafting
contractual limits on the disclosure of information to unauthorized users, preventing copies of
information, performing background checks on employees who may have access to the report,
and limiting the use of such information (Jayakrishnan, Mohamad, & Yusof, 2019).
Current Business Intelligence Trends
DQ and MD Management
The management of any business—at every level, from strategy to tactics to daily operations—
relies heavily on qualitative data. According to results from The BI Survey, businesses have
always prioritized providing reliable information. There will be a greater need for high-quality
data in 2022 as information becomes an ever more important industrial element. In the end,
faulty information causes businesses to make the incorrect choices (Tseng et al., 2021).
Data Governance
New developments in business intelligence have highlighted the need of data governance in
fostering agile response times. The term "data governance" refers to the management and
protection of a company's data assets, which includes the people, procedures, and technology
necessary to do so. Therefore, business data has to be easily digestible, accurate,
comprehensive, reliable, secure, and accessible. The following is a list of the primary goals:

Minimizing risks

Establishing guidelines for the use of information

Efforts made to improve communication both inside and outside

Making data more useful

Time and money are both saved (Mariani et al., 2018).
Business intelligence tools/software
SAP Business Objects
In-depth reporting, analysis, and data visualization capabilities are offered by the SAP Business
Objects software package. The platform excels in a number of fields, including enterprise
resource planning (ERP), digital supply chains, and customer relationship management
(CRM). The availability of self-service, role-based dashboards that users can utilize to build
custom dashboards and applications is a fantastic feature of this platform. SAP offers a
multitude of functions in an efficient setting for both users and managers. But be aware that
the product's sophistication will probably come with a greater price (Sun, Sun, & Strang, 2018).
Datapine
With the help of the end-to-end BI suite Datapine, data analytics is now accessible to non-IT
professionals as well. With the help of datapine's solution, both data analysts and business users
can easily combine various data sources, perform complex data analysis, create eye-catching
business dashboards, and offer insightful business information (Omar, Minoufekr, & Plapper,
2019).
MicroStrategy
The extensive dashboarding and data analytics, cloud solutions, and hyperintelligence that
MicroStrategy offers, as well as its lightning-fast performance, may be beneficial to
corporations. Users will be able to accomplish much more than they were able to do in the past
with this solution, including seeing patterns, spotting new opportunities, boosting productivity,
and other things. A user may connect to a single source or multiple sources, with the incoming
data coming from anywhere, including a cloud-based service, an enterprise data management
system, or a classic paper-based spreadsheet. In addition, users have the ability to link to a
single source or numerous sources. It may be used by anybody who has access to a computer
or a mobile device. Setup, on the other hand, often calls for the participation of a large number
of individuals and demands a good deal of prior experience with the program that is being used
(Rahman, 2021).
Conclusion:
From the report, it can be concluded that the current trends in Business Intelligence are growing
and advancing with each passing day. Researchers of this field are consistently making
innovations to bring this to new level. The report also focused on providing valuable
information on this topic and it can covers important areas and challenges of business
intelligence which will help readers.
References
Jakhar, R., & Krishna, C. (2020). Business intelligence: as a strategic tool for organization
development (a literature review). ANWESH: International Journal of Management &
Information Technology, 5(1), 44-46.
Vidal-García, J., Vidal, M., & Barros, R. H. (2019).
Lennerholt, C., van Laere, J., & Söderström, E. (2018). Implementation challenges of self
service business intelligence: A literature review. In 51st Hawaii International Conference on
System Sciences, Hilton Waikoloa Village, Hawaii, USA, January 3-6, 2018 (Vol. 51, pp. 50555063). IEEE Computer Society.
Ahmad, S., Miskon, S., Alabdan, R., & Tlili, I. (2020). Towards sustainable textile and apparel
industry: Exploring the role of business intelligence systems in the era of industry 4.0.
Sustainability, 12(7), 2632.
Rahman, A. A. A. (2021). Bibliometric approach of Business Intelligence as technical
infrastructure to enhance the organizational performance, competitiveness and decision
making. Journal of Legal, Ethical and Regulatory Issues, 24, 1-12.
Omar, Y. M., Minoufekr, M., & Plapper, P. (2019). Business analytics in manufacturing:
Current trends, challenges and pathway to market leadership. Operations Research
Perspectives, 6, 100127.
Sun, Z., Sun, L., & Strang, K. (2018). Big data analytics services for enhancing business
intelligence. Journal of Computer Information Systems, 58(2), 162-169.
Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data
in hospitality and tourism: a systematic literature review. International Journal of
Contemporary Hospitality Management.
Tseng, M. L., Tran, T. P. T., Ha, H. M., Bui, T. D., & Lim, M. K. (2021). Sustainable industrial
and operation engineering trends and challenges Toward Industry 4.0: A data driven analysis.
Journal of Industrial and Production Engineering, 38(8), 581-598.
Jayakrishnan, M., Mohamad, A. K., & Yusof, M. M. (2019). Understanding big data analytics
(BDA) and business intelligence (BI) towards establishing organizational performance
diagnostics framework. Int. J. Recent Technol. Eng, 8(1), 128-132.