Large scale environmental problem is the difficult task to address. Business applies the use of database to address the management problem. According to Cox (2014), the data application in business impact performance hence most companies usually realize a huge profit. The type of data schema depends with the business structure and management level. And therefore, the data schema required for this business is based on the data flow in the organization. The database schema is based on the administration knowledge. It also illustrates how a real world entitles is structured into the system. The business is running a warehouse and therefore, the data schema should contain features, which would allow employees to enter data and process easily in the system.
Diagram 1: Data Schema for the business
Rational for the structure
The data schema in the diagram 1, has three sections, the calendar, fact table and bind style. The business is a warehouse and therefore, the kind of data, which will be entered into the system, includes customers, cost of goods, selling price, data of submission and the clients also come from different regions. These must be captured by the system for easy processing and therefore, the data schema reflects what the database shall contain. The business would enter information related to sales, products, supplier or customers contacts, where they can from and data and month when the product was brought to the store CITATION Lah14 \l 1033 (Laher, Surace, Grillmair, Ofek, & Levitan, 2014). This information would be captured best using the above data schema because of nature. The data nature contains four tables’ calendar, Fact table, customer and Bind Style. The calendar table would contains information related to dates especially when the product was brought to the store.
The primary key is located at the fact table in the first field of the table, which is the customer id. The customer ID is used to identify every customer in the database and it is also describe the relationship between the tables to ensure that the database could function well. When customers come to the store, each customer is assigned a unique identification number when the product is processed to the warehouse. The customer’s products, sales, cost and the amount owned by the company could be established using he customer key and therefore, the primary key is the customer ID.
The Referential Integrity is the accuracy and consistency of the data. The referential integrity would be achieved by ensuring that there is a foreign key which is associated with one of the tables CITATION Chu15 \l 1033 (Chung & Paredes, 2015). The foreign key can be established at the fact table to ensure that the four tables are connected. The parent table must have foreign key for success of complete processing of data when entered the system. Without foreign key the data can easily get lost and the incomplete information is brought back. From the database, the users would not be able to generate reports and queries cannot be sent as well.
The entity relationship is the kind of relationship where graphical representation is used to illustrate the relationship between the tables or information. It describes the kind of information, which is stored CITATION Rüe18 \l 1033 (Rüegg, et al., 2018). The relationship is how the data is stored between tables, fields or entities. The data must have a proper link for it to work well and give positive feedback when processing queries or report. The business requires the relationship of one-to- many. This means that one table is linked to several other tables of the data. It means that the data entered at the warehouse of the company is associated with customer, and other data, which are closely related to the customer CITATION Lah14 \l 1033 (Laher, Surace, Grillmair, Ofek, & Levitan, 2014). It is established this way to ensure that there is efficiently in data process across several departments. The image below shows an example of how entity relationship operates to ensure that it operates well across the company.
The entity relationship will be established by linking FACT TABLE, CUSTOMER AND DATE tables together. This will ensure that there is proficient communication within the data level and therefore, the processing would be easy and faster. It ensures that there is consistency and accuracy of the data, which is being processed. The diagram is therefore, just as example of how the entity relationship works.
Data Flow Diagram
The data flow indicates how the data flow in the organization. It begins from where the data starts and the end process, which could be report generation or queries and even storage of the data. It describes the process, which involve in the system for the transfer of data from the entry point to the exit CITATION Chu15 \l 1033 (Chung & Paredes, 2015). The exit of the data processing could be either a report being generated or the data is stored for future use. It is described based on the logical data flow, which is required to established functionality of the system. The DFD is needed for processing, storage, manipulating, and the distribution of data to the system. It communicates with the system to ensure that the data flows from the beginning to the end. It is therefore, important to have a proper flow of data for the system to work well. And for this to be obtained the structure of the system must be well thought-out and clear. The data flow diagram is therefore, every essential for the operations of the database.
BIBLIOGRAPHY Chung, K. S., & Paredes, C. W. (2015). Towards a Social Networks Model for Online Learning & Performance. International Forum of Educational Technology & Society , 2-35.
Cox, M. (2014). Understanding large social-ecological systems: introducing the SESMAD project. International Journal of the Commons , 265-276 .
Laher, R. R., Surace, J., Grillmair, C. J., Ofek, E. O., & Levitan, D. (2014). IPAC Image Processing and Data Archiving for the Palomar Transient Factory. Publications of the Astronomical Society of the Pacific , 2-35.
Rüegg, J., Gries, C., Bond-Lamberty, B., Bowen, G. J., Felzer, B. S., McIntyre, N. E., et al. (2018). Completing the data life cycle: using information management in macrosystems ecology research. JOURNAL ARTICLE , 2-35.
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