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(A) Each student is expected to write a summary report on three key topics presented.

(B) Research on application in BI, DM and KDD
Each student is expected to solicit an integrated view on how BI, DM and KDD contributing to business operation. Each student is required to propose a business domain on their own, and illustrate how BI, DM and KDD can improve respective wellbeing quantitatively.

What is Business Intelligence?

(A) Business Intelligence (BI)

Business Intelligence or BI is one of the renowned operations to be used in the business considering the modern day situations of the commercial market. The other name by which Business Intelligence is known in the market of business and commerce is Descriptive Analysis. It is named so because it describes the past or the current state of the running business operations (Sauter, 2014). It is that wing of the business that is confined to describing what were the conditions of the business and what is the present conditions of the business. Technically speaking it can be said that business intelligence is a customary of some specific processes, some critical architectures and some of the profound technologies that is confined to converting raw data into meaningful information as a result of which it is used to drive some of the profitable business actions. It is a package that is formed with the combination of the software and the services targeted to transform the normal data into a directorial path of knowledge and critical insights.

As mentioned above the objectives of business intelligence in are straightforward. Besides analysing what has been done and what needs to be done, there are more areas where the grip of Business Intelligence is substantial (Abelló et al., 2013). The impact of business intelligence in an organisation is direct and to be confined to be benefitted with strategic, tactical and operational decisions of business. It hugely supports resource based decision making using with the use of historical data and not just by relying in the assumptions and the predictions (Debortoli, Müller & vom Brocke, 2014). The tools used in the business intelligence systems are used to perform certain set of operations. This set of the operations mainly includes the creation of the reports, brief ad concise summaries, dedicated dashboards, operational maps, statistical maps and insightful charts along with the amalgamation of the detailed intelligence that is based as per the nature of the business operations.

Business Intelligence is very much dutiful in leading out the key business operations. It is used for the creation of the key performance indicators based on the data collected in the past. It is used for the identification and setting up of the benchmarks of several unique processes (Chang, 2014). The business intelligence systems helps out the business organisations to detect out the trends in the market and also find out the business problems that needs the critical attention from the managers. Another functionality of the business intelligence is that it helps them on visualizing the data that helps them in enhancing the quality of the data and thereby helping them to make quality decision making. The flexibility of the business intelligence not only assists the large enterprises but also allows an assistance to the small and the medium scale enterprises. With the help of these kind of the modules, the business intelligence proves to be fruitful to the business organisations.

Objectives and impact of Business Intelligence

Data Mining is one of the many operations involved in the business processes that is similar to that of the business intelligence operations. The operations are almost similar because it is also used by the business organisations to convert raw data into valuable set of information. Dedicated software is used in data mining to drive out useful insights about the company (Lin, Yao & Zadeh, 2013). By using a special type of software to look for the patterns in some of the sections of data, business operations will be able to learn more about the insightful information about their customers involved to enhance and frame out some of the effective marketing strategies, reduce the total costs and increase the number of sales for the company. The main outlines of the Data Minig activities involves the processes of effective collection of raw data, resourceful warehousing and the processes related to that computing.

Warehousing is a process when the companies are involved towards centralizing their data into a single database or a single program. The use of the data warehouse in data mining is huge in implementing the effective processes in the organisation. It may be used by the large enterprises to chalk out the segments of the data that are required to perform critical analysis and use them accordingly in some of the specific situations (Larose & Larose, 2014). Although, in some of the other cases analysts may entail on the process of selecting data according to their choices and begin with the process of data warehousing based on those set of requirements and the specifications (Freitas, 2013). It is regardless of the fact that Data Mining is used by most of the organisations to carry out operations that are involved with the processes of tactical decision making among the management of the concerned business organisation. The programs that are based on the data mining processes are often used for analysing the relationships and the patterns that are specifically based on the user requests of a particular area.

One of the instances of the use of data mining processes in the organisations states that the data mining process can be used by the organisations to create separate classes of information. As a generic example, the online stores of the internet world can be taken up. It is these online stores that collects the information of their valuable customers online and separate them into unique classes where they are served with special deals according to their likings and the requirement pattern. The insights of the customers are analysed and stored in special directories with the involvement of the dedicated data mining software. These insights are valuable for the organisations to gain profitability as a result of which they are facilitated to foster in the commercial and global market (Braha, 2013). This approach is fast as the mining process gets completed within a very short span of time as the insights are reused over and over again to reach to specific point in the business.

Data Mining and its role in business operations

Knowledge Discovery in Databases or KDD is another business operation that is often implemented by the large organisations along with the small and medium scale organisations to facilitate their business process through which they are able to gain a huge profitability that further aides them to foster in the international market of commerce. The knowledge discovery in databases is one of such business operations that is almost similar to that of the business intelligence and the data mining (Hilderman & Hamilton, 2013). Although, it is one of the processes that somewhat stands apart from the rest of the business operations such as the process of business intelligence and the data mining process.

Technically stated, it can be said that the Knowledge Discovery in Databases is one such process used up by the business operations that is used for discovering useful information or rather useful knowledge from a cluster or a collection of data. It is the enhanced version of the data mining process and it is widely used by the business organisations. Knowledge Discovery in Databases is a data mining process that includes the involvement of preparation of data, selection of data, cleansing of the data and incorporating of the prior knowledge into the specific data sets (Ester & Sander, 2013). The next step of the very process involves the accurate interpretation of the solutions from the results that are observed from a business point of view.

One of the major applications of the Knowledge Discovery in Databases is that it is used in the application of the areas in the marketing, detection of the fraudulent activities enhancing telecommunication processes and manufacturing of the specific set of products. In the past both the processes of the data mining and the knowledge discovery was performed with the help of manual processes. The process of both of the business operations has evolved much in the modern days. In modern day, situations the amount of data has grew larger which is generally measured in the size of some terabytes and has proved to be beyond the control of the human activities (Esfandiari et al., 2014). In addition to that, for the successful processes involved in the business there is a great part played by the Knowledge Discovery in Databases to drive out valuable information about the customers and the insights related to them. It is used for the discovery of the underlying patterns in the collected data, as it is very much essential to formulate the business processes.

Applications of Data Mining

It is due to this reason, dedicated software tools were discovered and developed to  mine out effective information that is mostly hidden in the approaches. It is also used to make valuable assumption and tactful predictions that is comprised of the processes involved in artificial intelligence (Dhar, 2013). The positive impact of the process of Knowledge Data in Discovery of business operations has been huge for over the last several years. This business operation is involved in the housing of several dedicated approaches to discovery of the valuable information about the requirement of the customers.

Business intelligence focuses on certain software and services which helps in transforming data into proper action. It is provided in such a way that it can deal with the organization strategy and business decision (Witten et al., 2016). Business intelligence tools can be used for accessing and analyzing various data which are present in analytical findings in any report. It helps the user by providing user with detail intelligence regarding the business state. Business intelligence software system helps in providing current, historic view of business operation. It is mainly done by making use of data which has been gathered in data warehouse. It also worked occasionally from various operational data.

Business intelligence tools are mainly considered to be an important method for data-driven DSS. In some of the cases, BI can be used in briefing books, reporting tools and lastly executive system (Wu et al., 2014). By the help of these tools, people can easily analyze data rather than waiting for IT to run for some complex system. The gathered information can help the users to easily back up business decision with hard number. Various application of Business intelligence is considered to be important for tackling data from various domains like sales, production, finance and other sources.

Sisense is a well-known business platform which helps the user to join, analyze and picture out the information which is required for making better and intelligent decision. Apart from this, it also helps in crafting out work plan and different strategies. It is considered one of the best business intelligent application (Zheng, 2015). Business intelligence software can be considered to be suitable for creating insight with respect to business value from complex data. By the help of this application, user can unify various kind of data which is required by them. It is considered to be a dashboard which is needed for drag and drop interface.

What is Knowledge Discovery in Databases?

Data mining is considered to be an important process for sorting large set of data and identification of pattern (Torgo, 2016). It helps in overcoming large number of problems by proper analysis of data. Tools related to data mining is very much helpful in understanding future trends. Grocery stores are considered to be well-known user of data mining techniques. Most of the supermarket tend to offer loyalty card to large number of customers. It gave them access to reduce the price which is not available to users. Card makes it very much easy for various stores to easily track the buyer and when the thing is bought. After the proper analysis of data, stores can easily make use of data which is offered to customer coupons. It is mainly used for targeting the buying habits and deciding the item which needs to be put in.

Data mining can be considered to be a cause of concern when a particular firm sells out selected information. It does not represent all over the sample group which is needed for providing a proper kind of hypothesis. Data mining comes up in mainly five stages. The first stage is all about collecting and loading it into the data warehouse (Sangar et al., 2015). They can easily store and manage data either in their house server or even in cloud. A number of business analyst, management teams and lastly IT professional can easily access the required data. It mainly helps in analyzing how they are organized. This particular application software can be used for sorting data which is totally based on user result. It mainly helps in analyzing data in easy format like graph or even table.

Data mining programs are considered to be helpful in analyzing relationship and data based on user request. An organization can make use of data mining software for classifying various classes of information (Wixom et al., 2014). For example, data mining can be used by restaurant can easily make use of data mining for analyzing the things which should be offered to certain specials. It helps in analyzing information which has been collected and aims in building classes for creating a customer base and list of thing which is ordered by them. Data miners look for large of information which is based on various logical relationship or even proper association.

Knowledge Discovery in Database (KDD) can be sated a method of discovery some useful knowledge which is needed for data collection (Gamarra, Guerrero & Montero, 2016). It is mainly used for data mining technique process which includes data preparation and its selection, cleaning of data. It mainly aims in incorporation of knowledge on data sets and analyzing proper solution from the given result. KDD is used in large number of fields like marketing, manufacturing, telecommunication and last detection of any fraud activities.

Applications of Knowledge Discovery in Databases

In the earlier days, both data mining and KDD was performed in manual way. But with the passing time the amount of data in the system become large to around terabytes size. It cannot be maintained in manual way. The successful existence of any business and discovery of underlying patterns in data is considered to be important (Ristoski & Paulheim, 2016). There is large number of software tools which has been discovered for analyzing hidden data and making certain number of assumption. It has formed as an important part of artificial intelligence. It now considered many aspects of discovery of data which is based on learning and knowledge acquisition for different system. The main role is all about extracting high-level knowledge from various low-level data.

KDD encompasses certain number of data storage and access algorithm which is required for massive datasets and analyzing the output (Wixom et al., 2014). Data cleansing and its access are mainly included in data warehouse which is needed for facilitating the overall process of KDD.    

References:

Abelló, A., Darmont, J., Etcheverry, L., Golfarelli, M., Mazón, J. N., Naumann, F., ... & Vossen, G. (2013). Fusion cubes: towards self-service business intelligence. International Journal of Data Warehousing and Mining (IJDWM), 9(2), 66-88.

Braha, D. (Ed.). (2013). Data mining for design and manufacturing: methods and applications (Vol. 3). Springer Science & Business Media.

Chang, V. (2014). The business intelligence as a service in the cloud. Future Generation Computer Systems, 37, 512-534.

Debortoli, S., Müller, O., & vom Brocke, J. (2014). Comparing business intelligence and big data skills. Business & Information Systems Engineering, 6(5), 289-300.

Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.

Esfandiari, N., Babavalian, M. R., Moghadam, A. M. E., & Tabar, V. K. (2014). Knowledge discovery in medicine: Current issue and future trend. Expert Systems with Applications, 41(9), 4434-4463.

Ester, M., & Sander, J. (2013). Knowledge discovery in databases: Techniken und Anwendungen. Springer-Verlag.

Freitas, A. A. (2013). Data mining and knowledge discovery with evolutionary algorithms. Springer Science & Business Media.

Gamarra, C., Guerrero, J. M., & Montero, E. (2016). A knowledge discovery in databases approach for industrial microgrid planning. Renewable and Sustainable Energy Reviews, 60, 615-630.

Hilderman, R. J., & Hamilton, H. J. (2013). Knowledge discovery and measures of interest (Vol. 638). Springer Science & Business Media.

Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.

Lin, T. Y., Yao, Y. Y., & Zadeh, L. A. (Eds.). (2013). Data mining, rough sets and granular computing (Vol. 95). Physica.

Ristoski, P., & Paulheim, H. (2016). Semantic Web in data mining and knowledge discovery: A comprehensive survey. Web semantics: science, services and agents on the World Wide Web, 36, 1-22. Sangar, A. B., Hesar, Z. E., Asl, M. S., & Tahmores, K. (2015). Research article proposing IS success models for measuring business intelligence system (BIS) success and analytical literature review on BIS measurement. ANARE Res. Notes, 33(2), 269-283.

Sauter, V. L. (2014). Decision support systems for business intelligence. John Wiley & Sons.

Torgo, L. (2016). Data mining with R: learning with case studies. Chapman and Hall/CRC.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., ... & Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3), 29.

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