Data Warehousing
The field of information system has evolved at a rapid pace because of which data warehousing has emerged. The main function of data warehousing is analysis and dealing with a query. It is a time-variant, subject oriented, integrated, and un-updatable collection of a company’s data. It is mainly used for the decision-making process, management, and business intelligence. The data warehouse of the company is the place where its data can be stored physically. Some of the components of the data warehouse are software, interactive, and directory. Software is an important element where the original data is stored. It allows copying the data from the software and transferring it to the warehouse. Such a system is required by AnalyzeThis as it will help the company to make its process more effective. The company has an interactive software which allows the company to process the data (Adamson, 2012).
Need for Data Warehousing
AnalyzeThis requires data warehousing because of its expansion. As the company is expanding along with its number of the customer, it is necessary for the company to opt method to control, monitor, and analyze the data. In addition to it, there are two more important factors that influence the company to use data warehousing which is integration and extraction of information. Integration allows the business to provide information company-wide. It also maintains a standard of quality. It is necessary for the company to extract useful information from the operational system and separate it as it helps in improving the performance of the company and manages its data. The extraction and separation of data are also necessary because one file can be opened through different servers and are running in a different form. It reduces the speed of the system as data is unevenly spread in different parts of the system. Data warehousing helps in adjusting the data evenly and allows it to run on a particular server. There are numerous challenges that can be faced while implementing a proper data warehousing system as gathering all the data in a single file will give rise to inconsistent key structures. Synonyms, missing data, inconsistent data values, etc. are also some issues that can be faced while adopting the data warehouse system. If these issues are saved then data warehousing can be implemented efficiently (Ponniah, 2011).
Best Practices
The best practices of data warehousing are completing the requirements and design, prototyping, utilize aggregations, training, and data checking. The adoption of all these practices allows the company to implement data warehousing in a professional manner. It is very necessary that all the requirement of the data warehouse is completed, and the design of the program is in accordance with the need of the company. Before completing the entire system, it is necessary to build a prototype as it helps in understanding the major areas of business. At the time of developing the software, it is a best practice to use correct aggregations and detail of the data to ensure that all the requirements are perfectly infused into the system. In addition to it, training for the new system is necessary as it will help the users to use the system in an efficient manner (Adamson, 2012).
Explanation of Database Schema
Schema is a graphical representation of data which makes it easier to understand the process on which it is based. From the above schema, it is noticed that the all the data collected from the web analytics is stored in the data warehouse. Web analytic receives data from data mining. Other factors like content analytics, business aptitude, and data apparition are the sources from where the entire data of the company is filtered and made available in a format that is understandable and easy to use. As the entire data is stored in the data warehouse which is the main hub of storing the entire data of the company, the users have easy access to the data warehouse. The users of the system have several options to search the data in a proper manner. The system has provided various options of data warehousing like tables, fields, procedures, relationships, views, and indexes. All these options are beneficial for the users as it directs them to the useful information that is required for the project. All the irrelevant information is easily separated with the help of fields. From the schema, it is noted that the system is accessible to the users as well as the CEO of the company. CEO also has a direct control of the users and can view what is being done by the users in the system. The schema of the business and its processes provided in the database schema is deemed to be correct as it uses simple steps to cover the flow of data. The data with the provided schema can control and filter the data to make it easier for the customers to use it. The graphical representation of the database schema is in Appendix 1.
Entity Relationship Diagram
The entity relationship diagram explains the entire procedure that how web analytics and databases work. There are two different types of user accounts. The first one is confined for the admin or the employers who own the company. It is critical for the company to make separate database accounts for employers and the staff members. The employer acts as an admin and inspects and examines the entire data analysis and processes.
The employee as an administrator will log into the database, will enter its username or will sign up to continue as an admin. After logging in, the admin will be directed to the homepage where he will see different features that are being designed to advance the data analytics system. The features will include data inspection, details of the employees, and details of the progression of work, email, important documentations, and reports.
According to Grust (2006), the role of an administrator is played by the employer or executives of the company, and their primary responsibility is to scrutinize and examine that staff members in the company are working according to the requirement of the company’s policy or not. The ERD diagram is demonstrating that the employer as an admin is the one who is capable of posting the details of the project, and he is legally responsible for verifying that all the employees in the company are doing their work in a right way or not. The employer as an admin enters the data in the database that is being reviewed by the employees. The admin also has its contact detail mentioned on the web analytic database so anyone from the team can easily contact him and then they can even explain the issues they are facing in the company or progression of work. The admin then examines the dispute and takes action according to the requirements of the circumstances, the employee who is inefficient is fired, or the employer gives him a warning.
The web analytics and database provides a different interface for employees, the features they see are different, as the employees use data analytics and database to check the requirements of products, eligibility, and requirements. The database helps an employee in becoming more efficient and successful. The web analytics and database acts as a platform for the employees through which employees are capable of understanding the right procedure of work. The Entity relationship diagram illustrates that an employee in a database acts like a user, where he will sign in to his account and then will be able to view different features such as search, feedback, a collection of data, and submission of data. The employers as a user can obtain the data that has been added by the administrator. The database helps the employee to examine the suitable course of work. Web analytics and database make work easier for the employees as it ensures that the data they are storing is going to be saved in a safe place. The database and web analytics also increase the speed of the work and they do not need to save anything on the secondary storage, and it also allows them to view and obtain the data and then they can even report their issues to the administrator.
Data Flow Diagram
The data flow diagram exemplifies that how data is executed in a company by a structure concerning inputs and outputs. The DFD diagram is illustrating the entire process of work in the company. It shows how there are two different type of arrangements for the employer and an employee. The role of an employer has been shown in the data flow diagram, and it exhibits that the employer is legally responsible for designing an effectual database that will help the company in storing its important information. Data flow diagram is designed in numerous nested layers. A single procedural node on a diagram is expanded to give the readers an idea about the role of employer and employees in a company. The data flow diagram shows the step-by-step procedure of how employer proceeds in a company. After designing the appropriate database, the employer implements it in the company and then he is also responsible for ensuring that the web analytics and database are working according to the requirement of the company or not. If the database is not proving to be successful for the company then employers hire another designer who can repair the database.
Similarly, the data flow diagram also demonstrates the workflow of employees of the company. According to Howe (2001), the employees of the company are equally responsible for the growth or failure of the company. The employees will also first sign into the database and will upload the required data that has to be stored, inspected, or examined. The web analytics and database ensures that their data is not going to get lost. The data flow diagram shows that employees are allowed to report it to the admin if they are facing any issues in the company. The data flow diagram illustrates the main developments within the system.
Company’s Business and Processes (Schema)
Entity Relationship Diagram
Data Flow Diagram
References
Adamson, C. (2012). Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance. Hoboken: John Wiley & Sons.
Grust, T. (2006). Current Trends in Database Technology - EDBT 2006. New York: Springer Publishers.
Howe, D. (2001). Data Analysis for Database Design. Oxford: Butterworth-Heinemann.
Kitagawa, H., Ishikawa, Y., & Qing Li, C. W. (2010). Database Systems for Advanced Applications. New York: Springer Publishers.
Ponniah, P. (2011). Data Warehousing Fundamentals for IT Professionals. Hoboken: John Wiley & Sons.
