Sit717 enterprise business intelligence 2019 t2 copy1
- 1. Running head: TECHNOLOGY 1 SIT717 Enterprise Business Intelligence 2019 T2 Student Name Institute Affiliation Date
- 2. TECHNOLOGY 2 Abstract Today’s commercial and industrial scenario is characterized by high levels of competition, resourcefulness and increasing use of technology. It is extremely necessary that the management has a firm grip over the company's data and can maintain a healthy relationship with customers. Such analytics help in smart decision making. Added, it helps in achieving goals and increase productivity. It significantly contributes to strengthening customer knowledge and increasing customer base. With an increase in the use of Artificial Intelligence, such analysis is increasingly done by all corporates around and has become an important aspect of the modern business world.
- 3. TECHNOLOGY 3 Table of Contents Abstract 2 Introduction 4 Summary - Data 4 Data Mining Techniques 5 Evaluation and Demonstration 8 Conclusion 11 References 12
- 4. TECHNOLOGY 4 SIT717 Enterprise Business Intelligence 2019 T2 Introduction Data Mining Techniques should be chosen according to the types of business and the type of problem the business faces. A generalized approach can increase the effectiveness and efficiency in business by the techniques that are used in data mining, the following are a few techniques that are used in data mining: Clustering, Statistical, Visualization, Classification, Neural Networks, Rules, and Decision Tree. Consumers are the epitome of profit and loss. If the consumers want, they can make the company the best from scratch and if they want, they can destroy the company from the top to nothing. Thus, it is important for a company to work on the consumers who will take in the products to provide the company with profits. Big Data helps in the accumulation of the consumer’s needs. It helps to create a picture of what the consumers need and how the company can play a part. If the Big Data is not involved in this then the consumer’s needs will never be out to the companies because all they can do is work on one or two consumers but cannot generalize them. If the generalization of the consumers has to be done, then it has to be kept in mind that the statistics have to be taken from a large group. It can only be achieved by the big data system. Consumers create the structure where all of the data rests and its influence on all the production is vivid yet striking. If the data about the consumers are indifferent, then the company needs to rethink their products. The sales are also affected by this. Summary - Data One of the central issues in an account is to comprehend why firms money themselves as they do. This issue has turned out to be progressively significant in light of the fact that how firms are financed impacts their exhibition and worth. Since the 1950s, the capital structure writing has tended to this major issue by concentrating on a company's blend of obligation and value. Be that as it may, firms frequently utilize more than one sort of obligation guarantee. Moreover, a few firms utilize particular sorts of obligation guarantee that others don't utilize. The financing decisions of different types of obligation guarantee and various measures of obligation issued lead to monetary information with numerous constant extents and a large number suggested by obligation structures.
- 5. TECHNOLOGY 5 The investigator ought to have a sound learning in bookkeeping, its standards, ideas, shows. Else he won't most likely examine the budget summaries in subtleties. Besides, down to earth use of bookkeeping information is completely required so as to achieve his targets. The expert must be clear about the target or reason for the investigation. As a rule, he is depended to do as such for his customers. Normally, he should know his customer and his prerequisites. As needs are, he will examine the fiscal summaries for gathering such data that are wanted by his customers (Bertoni and Larsson, 2017). The expert must choose the suitable procedures with the end goal of investigation. He may apply a specific system in one spot though an alternate method in different spots. The expert must revamp or regroup the essential information gathered by him from budget reports with the end goal of his needs and employments of fiscal summaries. For instance, in the event that he needs to know the working capital position, he should know the absolute current resources and all-out current liabilities position from the information contained in fiscal reports. The examiner must decide first the degree of his investigation which will encourage him to make arrangements for his work and furthermore to set up a calendar of work for the examination. The expert ought to be all around familiar with the outside just as the inner condition which is looked by the organizations; for example contenders, disposition of the leasers and account holders and so forth. So also, he should ponder the inward condition of the organization likewise, for example, basic changes, representative resolve and so forth which will basically assist him with studying and investigate the financial reports and to set up a report. The investigator must look to his discoveries in a clear style in a basic structure which is effectively reasonable by the regular clients of fiscal summaries. Data Mining Techniques Data Mining Statistical Technique is a technique that signifies are considered as a branch Mathematics that signifies the clustering of data and its description does not consider as a data mining technique by the data analysts. It helps in discovering the various forms of a pattern. Nowadays people actually have to deal with big data which can be further derived with important patterns. This technique definitely helps you in data mining with massive data itself.
- 6. TECHNOLOGY 6 Statistical Technique helps in identifying the data in various forms which also includes the statistical form of data. This helps the organization in saving time and many of the data more relevant. It includes a number of methods which verifies the numerical data. Statistical Technique helps in finding out the patterns in the database, probability of the event that will occur, patterns useful to the business, summary that gives you a detailed review of the database. Through these reports that are made through statistical techniques will help people can make smart decisions. There are a number of statistics but the one which is considered as useful and important in collecting and counting of data (Kahane et al., 2007). There are different ways by which data can be collected, some of them are given below: Mean, Median, Histogram, Max, Min, Linear Regression, Variance. Statistics is the said to be the component of data mining that provides the tools and the techniques which deal with a large amount of data. Statistics give a proper review and reports of the big data without wasting any time. It is the science of learning data which includes everything starting from collecting to organizing and presenting the type of data for business. There are basically two types of statistical data which are descriptive and inferential. Descriptive one organizes and summarizes the data. The descriptive data when draws to the conclusion it is said to be an inferential data. This analysis and presentation of big data, it is considered to be a core for data mining along with machine learning. It provides the analytical technique and tool which is helpful for large sets of data volume. Statistical Techniques is considered to be amongst the top technique as compared to other as many of large numbers of data are handled by it and the reviews for it are extremely good and sensible. They make the possibility of making the information easy. Big Data Intelligence helps in strengthening customer knowledge. The demands could be better understood along with a more accurate understanding of the target market. It improves a firm's capacity to reach out to prospective customers. Business Intelligence reports are fundamental to understanding or predicting consumer behavior and take decisions accordingly. The demographics of the consumer set can also be of great importance. Enterprise Intelligence helps in the evaluation of Return on Investment (Kovalerchuk and Vityaev, 2009). A higher return
- 7. TECHNOLOGY 7 is the basic goal of any organization. Examining information through Data Analytics even helps in determining return per department/divisions. This makes it easier to enhance business productivity. Data Mining Techniques help in resolving the problems with big data that are actually trouble for many organizations and businesses, there are many techniques that will help in extracting, collecting, and clustering the data. Data Mining Techniques will help you in collecting the data and making them readable in an easier way, the only thing that organizations have to do is choosing the perfect techniques which suit them better. Data Mining has many advantages which include the Analysis of Big Data, Improvised Predictions, exploring the discoveries with hidden patterns, the models that can be made available to understand the complex data easily. Implementation of a new system with existing platforms, helps in identifying the various aspects related to criminal suspects, they are actually cost-effective and efficient for data separation, it also helps the e-commerce website to cluster the data. The biggest disadvantage of data mining is the privacy issue, the companies can sell important information to different companies or customers. Data Mining tools work in different manner which is due to a different algorithm which itself are employed in the design, therefore, one should choose the correct data mining technique for the work. Some of the data mining analytics software is a bit difficult to obtain which requires knowledge and training. The information through data mining can be misused. A direct model can be utilized to check whether one classification or thing is identified with another. A case of a direct model is straight relapse: information focuses are plotted on a diagram to check whether they have a direct relationship; at the end of the day, can a straight line be utilized to speak to the information. In the event that a straight line can be drawn, this demonstrates there is a connection between the two classifications. A straight model can be utilized to discover data about how age, sexual orientation, pay, and different qualities identify with case size. A period arrangement model is a place a statistician takes a gander at how a specific thing performs after some time. For instance, they may see how policyholders' cases history changes after some time to decide the amount to charge for explicit policyholder attributes or they may think about the exhibition of ventures over some undefined time frame to decide rates to charge
- 8. TECHNOLOGY 8 for entire life coverage arrangements. Quite a long while of quickening interest in information and information examination are changing the protection business. To be precise obviously, information examination is one of the verifiable mainstays of protection. Statisticians have utilized numerical models to anticipate property misfortune and harm for quite a long time (Kovalerchuk and Vityaev, 2009). When they sell approaches, safety net providers gather enormous informational collections about their clients that are refreshed when those clients make a case. As of late, as safety net providers have tried to turn out to be progressively significant to their clients and increasingly proficient, they have understood the key significance of their information ventures. They need to tackle information investigation to improve client experience altogether while cutting cases taking care of time and costs, and disposing of extortion. Physically spotting irksome cases early is testing; working out procedures to alleviate the hazard once recognized is even harder. The data should be conveyed in an opportune manner (ideally promptly), into the characteristic work process of the agent, perhaps with a warning to the boss or huge misfortune unit. The data conveyed needs to raise an alarm, however, to clarify the traits which bolster the hazard level and propose an answer or work plan for the agent. This procedure should rehash itself continuously as basic information changes are made to the case document, especially for long-tail lines, for example, real damage. Evaluation and Demonstration If the reference work is a part of the Business Intelligence, then it is important to refer the Big Data Analytics in order to get the calculations right. In simpler words, Big Data helps in the holistic idea to be considered. The idea of Big Data is to collect the macro level of data and furnish it so that it can be supportive of business management. The complete idea of Enterprise Business management is to work on the aspect of funds, data support, and the profit section. The production and the dealing are also handled but the data analytics is one of the most important parts of the complete structure. But what if the book tells you that Big Data and Business Intelligence is not related. In the books, it says that business analytics has nothing to do with the macro-level of data that it
- 9. TECHNOLOGY 9 accumulates in the course. On the other hand, we find the complete system revolving around the data and the data analysis program. The analytical system of Big Data is one of the major influences on the Business Intelligence system (Kwak, Eldridge and Shi, n.d.). It is not limited to the data for the Sales, but it also works on inventory and consumer data. These help in the best planning and mapping of the system that makes it more proficient for the company to begin with. The idea of Big Data analytics is to take in the account of the structure in the base of the macro level. And the Business Intelligence system has to go through all the minute details inside the big level system. The extraction of the data is the primary focus of the Business Intelligence, but Big Data works on the accumulation and creation of the stats. Information is collected in Big Data whereas Business Intelligence works on the extraction of information to find out the key points of the complete structure. More importantly, it takes time for Business Intelligence to work without the idea of analytics. The analytics of the information is important for the decision-making purpose. If there is no data, stats or information to be worked on then there cannot be any decision to be made. It is one of the key features that connect the dots of these two components of the business. It is true that both of these are different ideas, but it has to be kept in mind that others are incomplete without the one. Thus, it has to be understood that you cannot neglect one for the complete focus on the other structure. Big Data helps in the improvement in the consumers whereas the Business Intelligence helps in the increment of the revenues. As the idea of operations is taken into the account of the business the operations have to be increased and the efficiency of the operation can be increased by the Business Intelligence. If the Big Data plays its role then the operations don’t come into their liability, for them, it is the strategies that count more for the company. Where all the discussion end then is the real question for many of the business companies. The discussion about the Big Data for Business Intelligence is one of the major parts of the company’s development. And to achieve the success that a company requires can only be done when both ideas work in sync. The statistics and the reports should be the best that Big Data can do and the management of the data for the use of the company is the work of Business Intelligence. Thus, in order to get the best results for the company it has to be kept in mind, that the accountability of both parties is different yet they complete the system for better efficiency (Mak,
- 10. TECHNOLOGY 10 Ho and Ting, 2011). Business Intelligence has to be very accurate with the data that it handles from the Big Data because it is based on the macro-level understanding and statistics. The big data will help in the production of the company as well as support the sales department in order to gain the best output form the company. Sales make the company’s profits and losses; thus, the company has to work on sales as a different entity. If the sales are not taken seriously then it will bring about damage to the company’s market value. The sales department is based on the inventory and its production unit. The macro idea about all the entities of the company comes through the channel of big data which in turn deals with the inventory’s analytics. There are no substitutes to the raw as well as built product, thus, the inventory has to be kept intact with all the data about it. The complete system surrounds the company’s manufacturing of the products or services. The inventory can be looked upon by the batch testing or the data based on the testing and reviewing. If Big Data loses the information on this account, then the company will be seriously affected. Business intelligence is the collection of systems and products that have been implemented in business, but not the information derived from the systems and products i.e. big data. But, both big data and Business Intelligence helps to analyze the huge data sets to expand the business and optimize the cost. This data analysis also involves an active part in the development of strategies and methods that make sure the success of organizations. Business Intelligence helps in handling a large amount of structured, unstructured or semi-structured big data to help in developing business strategies. In simpler words, big data makes Business Intelligence a more valuable and useful tool for most businesses. Previously, many decisions were based on historical data, but now big data analytics helps that happen at greater speeds. This is accomplished through the integration of open source technologies or the sources of big data platforms. In the present world, the importance of data in business is enormous as the most effective decisions can be made only by analyzing the data which helps to grow the business further (Smith, Willis and Brooks, 2000). Both big data and Business Intelligence helps in analyzing the data and to get further insights. Rapid and smart decision-making requires a holistic view of any business. Ideally, it is needed to be able to collect, analyze and act on information with rapid and smart decision-making. Therefore although big data and Business
- 11. TECHNOLOGY 11 Intelligence are not the same things, they both need to be synchronized to achieve the required goal in business. Conclusion Big data Intelligence refers to an analysis of available data of huge scale to explore hidden patterns, establish relationships, correlations and other useful insights. It is concerned with the structure of data and data processing to add value to the organization. The concept extends to the use of internal data for better workforce performance. Business Intelligence is a collection of system, software and products which can be used to re-organize data into a meaningful form for better decision making the process and improved business performance. It is all about the application of Business Intelligence tools to formulate coherent data and present it into a visually understandable form.
- 12. TECHNOLOGY 12 References Bertoni, A. and Larsson, T. (2017). Data Mining in Product Service Systems Design: Literature Review and Research Questions. Procedia CIRP, 64, pp.306-311. Kahane, Y., Levin, N., Meiri, R. and Zahavi, J. (2007). Applying Data Mining Technology for Insurance Rate Making: An Example of Automobile Insurance. Asia-Pacific Journal of Risk and Insurance, 2(1). Kovalerchuk, B. and Vityaev, E. (2009). Data Mining for Financial Applications. Data Mining and Knowledge Discovery Handbook, pp.1153-1169. Kovalerchuk, B. and Vityaev, E. (2009). Data Mining for Financial Applications. Data Mining and Knowledge Discovery Handbook, pp.1203-1224. Kwak, W., Eldridge, S. and Shi, Y. (n.d.). Data Mining Applications in Accounting and Finance. Encyclopedia of Business Analytics and Optimization, pp.609-617. Mak, M., Ho, G. and Ting, S. (2011). A Financial Data Mining Model for Extracting Customer Behavior. International Journal of Engineering Business Management, 3, p.16. Smith, K., Willis, R. and Brooks, M. (2000). An analysis of customer retention and insurance claim patterns using data mining: a case study. Journal of the Operational Research Society, 51(5), pp.532-541.