Data Handling and Business Intelligence: Current Trends Analysis

This report provides an overview of data warehousing, business intelligence, and data mining, emphasizing their current trends and applications in clinical data mining. It highlights the importance of business intelligence tools in transforming raw data into meaningful insights, aiding in decision-making within organizations. The report delves into the significance of artificial intelligence and cloud computing as key trends, particularly in healthcare, where they assist in reducing workload, improving service quality, and enabling faster and more accurate diagnoses. The study also covers the applications of machine learning, such as predicting pharmaceutical properties and recognizing medical images. The report concludes by underscoring the vital role of data mining in improving healthcare processes and efficiencies, emphasizing the use of business intelligence trends to improve patient outcomes. The report also highlights the impact of AI and machine learning on healthcare, including the use of chatbots to improve the relationship between companies and customers.

Data Handling and
Business Intelligence

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
Current trends in data warehousing, business intelligence and data mining..........................1
CONCLUSION................................................................................................................................3
REFERENCES................................................................................................................................2

INTRODUCTION
Data warehousing is the process of transforming and storing data in an effective manner in
data warehouse (Laursen and Thorlund, 2016). This study is going to show importance of
business intelligence trend in clinical data mining. With the help of business intelligence tools,
companies can transform all raw materials in important data which help them out in making
decision. All transformed and analysed data can be stored safe in data warehousing, data bases
and data files in specific data structure. It will also show importance of artificial intelligence and
cloud computing which is one of the main and effective trend of business intelligence in clinical
data mining and process of analysing data.
MAIN BODY
Current trends in data warehousing, business intelligence and data mining
Data warehousing and business intelligence are main and important terms as well as
process which are being used for storing all important informations or data of company in both
internal and external data bases from several sources (Peng, Tuan and Liu, 2017). In the context
of business intelligence, it can be said that it is one of the best tool which companies use for
collection, integration, analysis as well as visualization of company’s data. For understanding it
in more detail, it can be said that companies get raw data from different sources which needed to
be transformed in an effective manner. One of the main reasons of completing this process and
transforming data is to give and have meaningful insight into data for further queries. An
effective data mining by using business intelligence can also allow companies in detecting fraud.
Clinical data mining can be defined as an effective research strategy which is being used
by both researcher and practitioners to analyse and interpret both qualitative and quantitative
informations from available medical records. The main aim of clinical data mining is to make
practitioners able to make an effective decision regarding healthcare (Arunachalam, Page and
Thorsteinsson, 2016). It is not possible for professional of clinics or medical environment to
analyse large volume of patients and other data and identifying that which are important and
what to do with it. But with the help of business intelligence, professionals have become able to
focus on projects of specific areas which in turn make their strategies successful.
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In the context of current trends of business intelligence and data mining, it can be said that
it is important for organizations and healthcare departments to satisfy needs of customers and for
this they require to identify their needs. Customers expectations are increasing day by day and in
this current changing environment, key of the success of organizations are all about satisfying
customers changing needs (Karami, Rahimi and Shahmirzadi, 2017). It allows them to make an
effective decision and accomplishing their goals as well.
Artificial intelligence and cloud computing in clinical data mining: In the context of
current trends of business intelligence or process of storing and transforming data in clinical data
mining, it can be said that artificial intelligence is playing a vital role. Artificial intelligence and
machine learning are supporting all staff members of clinic or healthcare departments in
reducing their work load, managing time; delivering qualitative healthcare services and reduces
cost of clinical radiologist (Ramachandran, 2017). Artificial intelligence allows them to analyse
vast amount of image data automatically along with providing meaningful insight in order to
make effective decisions. Machine learning and artificial intelligence have features of learning
diagnostic complexity images which save their time and give them accurate and effective
outcomes. The main aim of cloud computing is to transform traditional approach of computing
by delivering service of both hardware and software application resources.
There are several uses of artificial intelligence and machine learning in clinics and in
clinical data mining as well which includes: Machine based learning and artificial intelligence
are being mainly used to predict pharmaceutical properties of molecular compounds for drug
discovery. Helps in learning difficult diagnostic codes as well as recognizing medical images
related to pathology slides, retinal scan and internal organs. This feature of artificial intelligence
and machine learning makes them able to diagnose and trace disease progression faster. Rather,
all these it also allows them in developing deep-learning techniques on multimodal data sources
like combining genomic and clinical data in order to detect new predictive model (Ahmed, El
Seddawy and Nasr, 2019). So, it can be said that business intelligence trend or artificial
intelligence tools have abilities to deal with uncertain as well as incomplete data sets.
In addition, in the context of importance of business intelligence trends it can be said that it
plays an important role in all industries but one of the main important industries where it should
be developed and implemented to the great extent is medical. One of the main reasons is this
department mainly work for improving health of patients. Promoting qualitative health is
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important for this and it is also known to us that it is the industry that has workloads and heavy
labour turnover. It is becoming difficult for them to focus on each patient individually and
analyse all their data or informations. It can affect patients’ health (Georga and et.al., 2019). But
thanks to business intelligence trends or artificial intelligence which is solving all these problems
by allowing them to understand human body as what is actually happening and what is the main
problem. After identifying problems, they can give appropriate treatments to them. It also allows
them in better understanding of processes and prediction of future of complex system with
analysing their efficiency (Clinical Data Mining, 2015). For an effective clinical data mining by
making an effective use of business trend, one of the models has been identified which is known
as: approximate temporal functional dependencies in which functional dependencies are
associated over facts stored by database. One of the best examples of artificial intelligence which
is being used to the great extent in companies is chat bots. It helps all sectors of industries in
relieving pain which they are suffering today. One of the main uses of this is to improve relation
between companies and customers and make them influence to buy products and services. So, it
can be said that by applying and making an effective use of ATFD mining to different clinical
data sets related to pharmacovigilance and psychiatric patient management, they can improve
healthcare services and analyse all data in an efficient manner (Kausar and et.al., 2016).
Importance of clinical data mining: In the context of importance of data mining it can be
said that it can help companies in the future by making them able to improve processes and their
efficiencies without having a target in mind. In the healthcare, it will allow professionals in
analysing and determining useful patterns by analysing large sets of data in an efficient manner.
One of the main aims of using these data patterns is to predict informations trends and
determining that now what to do with them. So, overall it can be said that data mining can help
them out in measuring effectiveness of treatment given by them and improving processes by
using effective business intelligence trends in it.
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CONCLUSION
From the above study it has been summarized that business intelligence or process of storing
data, transforming raw data into effective informations played a vital role. Data mining in
clinical help professionals in making the best decision regarding healthcare and improve their
services as well. Artificial intelligence as business intelligence trend also played a vital role in
clinical data mining as it allows professional and all staff in reducing work load. It has also
discussed all features of artificial intelligence and machine learning which are important business
intelligence trends, in clinics and its data mining.
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REFERENCES
Books & Journal
Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business
intelligence beyond reporting. John Wiley & Sons.
Arunachalam, S., Page, T. and Thorsteinsson, G., 2016. Healthcare Data Warehousing. i-
Manager's Journal on Computer Science. 4(4). p.1.
Karami, M., Rahimi, A. and Shahmirzadi, A.H., 2017. Clinical data warehouse: an effective tool
to create intelligence in disease management. The health care manager. 36(4). pp.380-
384.
Ramachandran, M., 2017. Service-oriented architecture for big data and business intelligence
analytics in the cloud. In Computational Intelligence Applications in Business
Intelligence and Big Data Analytics (pp. 237-258). Auerbach Publications.
Ahmed, S., El Seddawy, A.I. and Nasr, M., 2019. A Proposed Framework for Detecting and
Predicting Diseases through Business Intelligence Applications. International Journal of
Advanced Networking and Applications. 10(4). pp.3951-3957.
Georga, E.I. and et.al., 2019. Artificial Intelligence and Data Mining Methods for Cardiovascular
Risk Prediction. In Cardiovascular Computing—Methodologies and Clinical
Applications (pp. 279-301). Springer, Singapore.
Kausar, N. and et.al., 2016. Systematic analysis of applied data mining based optimization
algorithms in clinical attribute extraction and classification for diagnosis of cardiac
patients. In Applications of intelligent optimization in biology and medicine (pp. 217-
231). Springer, Cham.
Peng, M.Y.P., Tuan, S.H. and Liu, F.C., 2017, July. Establishment of business intelligence and
big data analysis for higher education. In Proceedings of the International Conference on
Business and Information Management (pp. 121-125).
[Online].
Clinical Data Mining. 2015. Available through: <
http://202.129.210.58/projectfiles/internal_cust_document/Clinical_data_mining2_1587474931.p
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