Business Intelligence summarise the word doc into power point. 2 2 Business intelligence Name Affiliation Course Professor Date Contents Abstract 2

Business Intelligence summarise the word doc into power point. 2

2

Business intelligence

Name

Affiliation

Course

Professor

Date

Contents Abstract 2 Introduction 2 Literature review 3 Purpose 3 Problems associated with business intelligence 3 During the software installation phase, there was a lack of critical communication. 4 Solutions for business intelligence issues. 4 Data set 5 Type of data in the BIRT sample dataset. 5 Data consolidation and housing 6 Data consolidation and database replication 6 Data consolidation methods 7 Hand coding and scripting are two methods for creating code. 7 (2) Free software. 7 There are other cloud-based applications. 8 Data preparation process 8 Data selection is the first step. 8 Processing data is the next step. 8 Data transformation is the third step in the process. 9 OLAP software solution we selected 9 IBM Cognos software 9 Distinct features of IBM Congos OLAP software that makes it differ from its competitors 10 Preparation of data 10 Insightful data exploration. 10 The model used in your analysis of our data (Decision tree model) 10 The methodology we used during the research project 11 Findings 11 Conclusion 12 References 13

Abstract

Business intelligence is the future of succeeding companies and organizations since it has the potential of analyzing and supplying useful data to organizations and businesses that may assist the firm convert into a progressive organization. What follows is a discussion of business intelligence in relation to our results from a study project at a Classic automobiles firm. Business intelligence is the way to go for businesses and organizations because OLAP software functions as data mining resources to the firm, and the data gathered by the software is utilized by the organization to make the necessary decisions that will lead to the organization’s long-term success. Business intelligence will give a company with information such as market trends, budgetary forecasts, customer monitoring, business risk assessment and management techniques, and sales tracking.

Introduction

We will oversee and carry out a full study project on Classic vehicle organization business intelligence in this project. In this context, we will process and comprehend what business intelligence is, the value of business intelligence, and the challenges that may be detected in the research project for the Classic vehicle company, which is our key organization under our business intelligence project. Corporate intelligence is rapidly expanding and has a favorable influence on business development and success. The main ambiguity that many firms may have while using this business intelligence strategy is how the method will affect your company and its key advantages.

Literature review

Company intelligence is a complete combination of procedures, professional architecture, relevant theories, and technology that turn substantial raw data into significant information for adequate business success and diverse business goals. Business intelligence has the greatest capacity for handling and processing massive amounts of important firm data and information, which are critical for any given business since the data from business intelligence allows the company to uncover new business prospects (Park, et al. 2017). Business intelligence processed data and information may assist a business in initiating and implementing a solid business strategy that can assist the firm in dealing with and surviving in a competitive market, as well as assisting the business in developing long-term business operation strategies.

Purpose

Every company should take major measures to integrate business intelligence properly since it is so crucial. There are many benefits to using business intelligence in a company’s day-to-day operations since it transforms raw business data into valuable and actionable information that may help the firm enhance its leadership and decision-making processes and therefore have a positive influence on its industry. Business intelligence also aids the company in increasing internal productivity, reducing the amount of time spent on data entry and manipulation, and aiding in the development of an effective strategy for competing with the company’s current business rivals. Finally, daily operations are strategically coordinated and well organized thanks to the use of business intelligence.

Problems associated with business intelligence

In spite of the many advantages of Business Intelligence, there are certain drawbacks that might have a detrimental influence on the firm that employs the strategy. There are times when there is an unexpected mismatch between the business and the software that has been implemented. (1) Insufficient assistance from the principal business intelligence source is one of the dangers of business intelligence. Because of lack of assistance from the business intelligence software vendors, a company may face a major and tough challenge. If a firm or its workers do not have a strong financial foundation, they will be unable to understand or appreciate the benefits that software may give.

During the software installation phase, there was a lack of critical communication.

Firm intelligence software suppliers may not be able to give suitable solutions if there is a lack of good communication between the business and its workers and personnel. A lack of efficient communication throughout the software implementation process might result in the firm deploying software that is not suitable for their needs.

Solutions for business intelligence issues.

Some practical actions and controls must be adopted by the company to deal with the issues related with business intelligence and the software. To begin, the company should maintain an internal business training forum to adequately teach its personnel on how to go about and utilize the program successfully and its advantages. Second, before deploying Business intelligence software, the company should make sure that efficient communication is being maintained in order to have the correct guidelines for the impact of the program and its value in addressing current business issues. The most important data a source of business knowledge, the Classic Car Club of America Incorporating business intelligence into a company’s operations may provide the organization with important and relevant information. Marketing strategy and commercial campaigns may benefit greatly from the use of business intelligence (Mariani, et al. 2018). E-commerce and online data analytics are also provided by the system, allowing the firm to track and analyze sales volumes, analyze and process consumer behavior, and increase efficiency between the company and its most important clients.

Data set

We’ve found that the BIRT example data set, with its seven tables and MS Access database integration, is the most efficient and relevant data set for our business, and it can be simply extracted and imported into the SAS EG with ease. While other data sets may give Classic Car organizations with ER diagrams and help files to assist them figure out how their company market is trending and find issues, the BIRT sample data set is superior since it can do both. As seen in the tables, the BIRT sample data set aids the firm in identifying and resolving any business risks.

Customer information, such as sales volume and debt owed by prime clients, is also provided via BIRT data set. A crucial component of the BIRT report designer is pre-installed along with the example data. For example, an organization may utilize or experiment with other accessible tools, such as creating sample data pieces for other practical tools, by accessing the sample database.

Type of data in the BIRT sample dataset.

The BIRT example data set has eight tables in a database.

A BIRT data type is a database that has been arranged according to a predetermined information structure (2019). There are unique and correct data columns in the data collection, such as automobile sales volumes, which have been given a different data name.

There is a total of eight BIRT sample data sets in use by the Classic Cars group, each with a distinct data type. The following is a description of the BIRT sample data’s data type in the form of a well-structured table.

DATA TYPE

Data type specification.

FRONT OFFICES

Sales office

CUSTOMERS

Prime customers available in the Classic car organization

ORDERS

Several orders placed and ordered by customers

CUSTOMERS ORDERS DETAILS

Sort items according to the orders placed by individual customers.

CUSTOMERS PAYMENT METHODS

Describes the payment means to be used by customers to pay for their placed orders

PRODUCTS LISTS

Shows the list of all available Classic cars available in the organization with their specifications.

EMPLOYEES

Lists all the available employees, including the Salespersons and sales representatives.

Data consolidation and housing

Since the company organization may utilize this information to convert and lead the particular premises to high-level success, all raw data obtained from organization databases, legitimate papers, and other dependable sources must be preserved properly. All of an organization’s databases and other dependable external sources are combined in a process known as Data Consolidation. Redundancies in the raw data are eliminated and raw data mistakes are filtered before the useful data can be stored in the organization’s data warehouses.

Data consolidation and database replication

As another data consolidation technique, this one address and updates different database changes, including data rows and records that have been deleted or that have been modified, and ensures that these updates have been properly applied across the organization’s data lakes and warehouses as well. To guarantee that the newest data version is represented in the organization’s data warehouses and data lakes, frequent database replication is conducted (Cheng, et al. 2020).

A company or organization that successfully executes the plan will benefit from data consolidation in numerous ways, as data consolidation will assist the company or organization deal with the present business climate. Data analysis and manipulation in business are made easier and more comfortable via the use of this method since it aids the company in achieving high product quality while also saving time and enhancing the accuracy and quality of data.

Data consolidation methods

There is no accepted standard for data consolidation. The data consolidation techniques used by the Classic vehicle association, as well as those used by other organizations, are discussed here.

Hand coding and scripting are two methods for creating code.

Data scientists are engaged to manually code the Classic vehicle in this form of data aggregation.

Consolidate and script the data from several organizational databases and other external sources into a single database for future reference and data analysis.

(2) Free software.

High-level knowledge may be achieved by using the open-source strategy, which is used by the Classic vehicle associations Data aggregation and consolidation software that is free to use and accessible to the public. Open-source software is quick, inexpensive, and very customizable.

There are other cloud-based applications.

This is a current data consolidation approach that Classic Cars has employed, where cloud solutions tools have been created with fast speeds, upgraded with active security measures, and are extremely versatile.. As a result of the simplicity with which cloud data consolidation may be implemented, it is the most effective solution for integrating the company’s many data repositories into a single database.

Data preparation process

When it comes to Classic automobiles businesses, the data preparation procedure has to be quite accurate, since data management determines whether or not their plan succeeds. The major approach used by the business to prepare the data for Machine Learning Algorithms is outlined in the following phases.

Data selection is the first step.

Data and subsets that are easily accessible, effective, and hold up inside the company are selected in this initial phase of the process.

Processing data is the next step.

After selecting the suitable data type and subset to work with, the organization has another responsibility in assessing the selected data and devising a realistic plan on how you are going to utilize the chosen data (Xu, et al. 2017).

As a general rule, preprocessing involves preparing data in a way that makes it easier for the organization to utilize and work with the data There are three processes to preprocessing data; first is to format the data, second is to remove unnecessary data, and third is data sampling.

Data transformation is the third step in the process.

Finally, in the data preparation phase, the organization needs to test various and particular data methods that the business intends to use by doing numerous separate data revisits and transformations. Scaling, decomposition, and aggregation of data are all components of data transformation.

OLAP software solution we selected

By studying Classic Cars, we discovered that using OLAP software will improve their market target precision and give them with online data analytics for customers, organization vehicle sales, and sales volumes. Online Analytic Processing (OLAP) software is simply a tool that gives an organization or a company with relevant data analyses from the organization’s databases and aids the organization in analyzing multidimensional data. For the Classic Cars, we are able to recommend IBM Cognos as their OLAP software for implementation.

IBM Cognos software

It was our recommendation to use IBM Cognos as an OLAP software for the business operation and monitoring of Classic Cars. There is a “proprietary license” for IBM Cognos software. Cognos has been incorporated into a web-based analytic processing system by IBM. IBM’s online performance analysis, rating, and reporting toolset is paired with the software to provide the company with metrics for monitoring. To satisfy the needs of a specific company, IBM Cognos was created and integrated with critical components already in place (Liang, et al. 2018). Windows, the IBM Cognos framework, manager, cube designer, IBM Cognos transform tool, maps manager, and the IBM Cognos connection toolkit are all built-in components of IBM Cognos. It is a software program that develops and prepares business reports, which are provided to the knowledge department for efficient processing. Charts, maps, lists, and loop functions may all be created using Cognos studio. Cognos studio is a toolbox for developing and searching for background and preformation of an event captured by the program, and then producing substantial data volume assessments.

Distinct features of IBM Congos OLAP software that makes it differ from its competitors

If you want to stand out in the market and beat the competition, IBM Congos software has a unique set of capabilities that make it stand out. IBM Congos has a unique set of characteristics that make it stand out in the markets listed below.

Preparation of data

A key aspect of IBM Congos is its ability to aggregate and consolidate data from databases and other dependable and accessible external sources using its integrated artificial intelligence formulation, cleaning and leveraging sophisticated technologies. Since the program offers unique and enhanced features that help it stand out in the market today, IBM Congos was our answer for the firm. Implementing the software in the field of business intelligence is necessary since it is both beneficial and cost-effective for any corporation.

Insightful data exploration.

It allows IBM Congos OLAP program to discover answers and solutions to difficult queries by revealing hidden data patterns. Visualize data using automated methods. With the help of this function, you may monitor and process and evaluate your web data analytics in a formal end-user natural and understanding manner.

The model used in your analysis of our data (Decision tree model)

After collecting raw data on the Business intelligence in Classic Cars company, we employed the decision tree to develop and evaluate our data. As a result, we went with the Decision Tree Model, which makes use of the Cascade Correlation Network and Quick Propagations to ensure that the model can make quick and effective judgments. Our study endeavor necessitates the usage of a decision tree model since it is very accurate and uses the smallest amount of data feasible, while still being able to evaluate data and make decisions on its own.

The methodology we used during the research project

We had to first establish a methodological approach that would be suited for our research study in the organization of the Classic automobile before we could begin collecting data. Data collecting, data processing and analysis, warehouse data design, efficient business intelligence design and lastly data raw data assessment and explanation procedure are the processes we followed throughout our study. The interview method of data gathering was employed throughout the data collecting procedure. As we gathered data and information from the personnel of the company, we asked them legitimate questions.

We used the business intelligence analysis and assessment approach to analyze and evaluate data. We studied and assessed the organization’s data lakes and warehouses by completing the proper examination of multidimensional data and facts contained in the organization’s data lakes and warehouses (Božič, et al. 2019). Due to its effectiveness, we used the Decision Tree model in our business intelligence design research because it provides us with the most customized solutions to our company’s business intelligence approach.

Findings

We were able to come up with a thorough set of conclusions after conducting a study project in the organizing of classic cars. Some of the outcomes were due to a lack of implementation of the company’s Business Intelligence system, as well as a lack of software that met the organization’s needs. With our findings, we were able to offer better software for the organization to utilize, and we also performed some in-house seminars where workers and organization staffs were educated on how a beneficial Business Intelligence program may assist them and their company.

Conclusion

We were able to draw the conclusion from our business intelligence study project that businesses and organizations would profit greatly from using Business intelligence facilities. Since the software is efficient, flexible, quick, and better tools for business analytics monitoring, we may see that better and more sophisticated OLAP software can lead to great commercial success. Therefore, we can say with confidence that OLAP and BI software is the future of corporate success since it is automated and can manage the online business platform without the need for essential oversight. It is thus possible to advocate for the use of these tools by enterprises or organizations, which will help them to improve their overall efficiency and performance.

References

Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International journal of information management, 46, 93-103.

Cheng, C., Zhong, H., & Cao, L. (2020). Facilitating speed of internationalization: The roles of business intelligence and organizational agility. Journal of Business Research, 110, 95-103.

Liang, T. P., & Liu, Y. H. (2018). Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2-10.

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.

Park, Y., El Sawy, O. A., & Fiss, P. (2017). The role of business intelligence and communication technologies in organizational agility: a configurational approach. Journal of the association for information systems, 18(9), 1.

Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of information management, 37(6), 673-683.

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