Data Handling, Business Intelligence, and Strategic Impact Analysis

This report delves into the critical aspects of data handling and business intelligence, emphasizing its significance in modern business operations. It explores the strategic impact of business intelligence utilization, referencing an article that highlights the importance of analytical tools such as graphs, charts, and dashboards in transforming data for informed decision-making. The report then reviews current trends in business intelligence, including data quality management, the use of business intelligence in marketing and sales, data discovery, machine learning applications, predictive analysis, collaborative business intelligence, augmented analytics, self-service business intelligence, and data automation. Each trend is analyzed for its impact and potential, providing a comprehensive overview of the evolving landscape of data-driven business strategies. The report concludes by underscoring the importance of these concepts for businesses aiming to leverage data effectively.

Data handling and
business intelligence

Table of Contents
INTRODUCTION...........................................................................................................................1
'Realizing the strategic impact of business intelligence utilization'. ..........................................1
Conclusion ......................................................................................................................................3
REFERENCES................................................................................................................................5

INTRODUCTION
Data handling and the business intelligence is seems out to be the most important aspects
of the businesses. It covers some significant policies and the practices on the basis of putting the
handing data assets. The business intelligence is useful in analysing several tools and monitoring
the business data. The data handling procedure and the business intelligence helps in
transforming several data to inform the strategic and business decisions. There is a need to have
significant analytical tools aspects including graphs, charts, reports, dashboards and many others.
The discussion is based on an article 'Realizing the strategic impact of business intelligence
utilization' (Itani, Lecron and Fortemps, 2019).
'Realizing the strategic impact of business intelligence utilization'.
The management and measurement of several performances are quiet complex and
ranges difficulties that gives a significant practices and research. The on-going effort helps in
methodology improvement and it seems that some traditional business performances helps in
financial management in which there is no limits and traditional indicators that can reach out the
strategic as well as qualitative indicators. The recent dynamic environment in businesses are
basically seems out to be the most valuable and volatile in terms of intangible assets.
The production factor suggest that knowledge is helpful in analysing towards the
company's performances. The organisational attempt that helps in strengthen the competition in
employees as well as consumers. The tendencies have been seems out to be the part that have
been included in three portions such as technological changes, the behavioural organisations,
innovations and many others. The organisation perspective have been continued on the basis of
consumer management, investments and many others which helps in increasing of the
productivity.
Literature review based on Business intelligence
As a data analysts, the business intelligence includes some other reflective technologies on the
basis of cloud platforms, learning from the machines, the artificial intelligence and powerful
internet of things are all of which that set to transform several capabilities of the technological
aspects in future. Also, the data centric companies adopt several types of analytics as well as
make sense of the growing volumes in business data. Several leading business intelligence trends
currently is estimated to grow above 2.72 million jobs at the end of 2020. According to the basis
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of Forbes, the business dashboard, self-services in the business intelligence are some popular and
useful cases for the organisation
Trends of leading business intelligence at current state
Data quality management: It refers as one of the major challenge which have facing the data
analysts on the basis of globe. A good quality data is quiet critical and derive from the right
insights on the basis of available data and the right decisions as well. The quality data must be
simply measured and include on the basis of several parameters including completeness, validity,
uniqueness, consistency, timeliness and accuracy. In terms of some other trends, it can be simply
suggest that the data quality management is one of the crucial trend because it simply reflects in
avoiding the poor quality data and improvising in the data quality standards as well as
regulations across the globe (Lauterbach, 2019).
Business intelligence for the marketing and sales: It is seems out to be as one of the leading
trend which states that the use of business intelligence are quiet effectively based on sales and
marketing across the companies. The latest sales and purchasing can access by the sales and
marketing professionals on the basis of relying on the technical IT expert. The improvement in
accurate forecasts and targets, several measures in latest marketing campaign and promotions,
business strategies on the basis of marketing campaign, focus on valuable consumers which are
quiet useful in generating the profits and preparing the marketing message are include in the
value and frequency of purchases.
The data discovery: The data analysis for all the business users in among the leading business
trends in 2020. The business user includes data discovery which is one of the business that aimed
to detects several patterns and deriving the insights in data on the basis of several analytical
tools. The data discovery includes data preparation in which business users quiet effectively
connected with multiple data sources. The data visualization includes easy performance and easy
target that contain informative graphs and the charts.
Machine learning in the business intelligence: 97% of the industry experts from United
Kingdom includes some major and effective role of the technologies. The machine learning in
the business intelligence can be effectively deployed in the business strategies which is quiet
related with the business intelligence and analytics.
Predictive analysis and reporting: The consumer value and sales prediction can be efficient and
prominently effective in terms of the availability of big data and tools that possess several
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capabilities which can simply establish the future business trends on the basis of current data
patterns (Firouzian and et. al., 2019).
The collaborative business intelligence: In a simple and effective language, it can be suggest
that the collaborative business intelligence is a existence between the technology and online
collaborations such as the social media as well as the web technologies. The emergence of fast
data collection and analytical abilities helps in collaborative business intelligence with a faster
decision making process. The emergence of faster data collection and the analysis ability,
collaborative business intelligence is one of the business and an industry trend that enables
several business intelligence reports in enhancing the activities among the business users.
Augmented analytics: On the basis of Garner study, Augmented analytics is one of the leading
trend in the business analytics. It can be simply suggested that for the global market in
augmented analytics is expected to reach at the value of 13 billion by the year 2020. The
technological power build several complex data analytics model and enable the users in deeper
insights. On the basis of industry, the data handling and business intelligence procedure enable
the online service employees to use some sort of multi-channel marketing in the promotion and
selling of the products (Caliendo and et. al., 2019).
Self service business intelligence: The business intelligence tools are properly structured on the
basis of central data warehouse and the data stores. The centralised infrastructure is quiet
sufficient and effective on the basis of present business enterprises which needs to have a proper
and effective data assets by the user. It has effectively led to the emergence of self services
which provided business intelligence users with more and effective flexibility and independence
in terms of accessing the data.
Data automation: It is seems out to be a disruptive technology of 2020. It helps in predicting the
40% science related task. The varieties of data sources is still a major business intelligence aim
which is quiet useful in terms of data consolidation and analysation of the data generation. The
several types of data automation solutions in the business intelligence enables the analysts in
collection and analysing of the large data volumes (Smurro, 2019).
Conclusion
The above discussion is based on data handling and business intelligence. It suggest that
the concept is quiet effective and important on the basis of systematic terms and formations. The
article that has been linked with the concept which is realizing the strategic impact of business
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intelligence utilization. Also, the discussion includes several current trends of leading business
intelligence.
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REFERENCES
Smurro, J.P., 2019. Cognitive collaboration with neurosynaptic imaging networks, augmented
medical intelligence and cybernetic workflow streams. U.S. Patent 10,332,639.
Caliendo and et. al., 2019. Intelligent closed captioning. U.S. Patent 10,321,204.
Firouzian and et. al., 2019. Investigation of the Effect of Concept Drift on Data-Aware
Remaining Time Prediction of Business Processes. International Journal of Nonlinear
Analysis and Applications, 10(2), pp.153-166.
Yassine and et. al.. 2019. IoT big data analytics for smart homes with fog and cloud computing.
Future Generation Computer Systems, 91, pp.563-573.
Lauterbach, A., 2019. Artificial intelligence and policy: quo vadis?. Digital Policy, Regulation
and Governance.
Powers and et. al., 2020. Guided page navigation. U.S. Patent 10,755,233.
Itani, S., Lecron, F. and Fortemps, P., 2019. Specifics of medical data mining for diagnosis aid:
A survey. Expert systems with applications, 18, pp.300-314.
Smurro, J.P., 2019Caliendo and et. al., 2019Firouzian and et. al., 2019Yassine and et. al..
2019Lauterbach, A., 2019Powers and et. al., 2020Itani, S., Lecron, F. and Fortemps, P.,
2019
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