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A database consists of mass information stored in a framework, which makes it easier to locate and discover relevant information. A well-designed database contains accurate and up-to-date information for analysis and reporting. Database design can play an important role in the efficient execution of queries and ensuring consistency of information.
What is Database Design?
Database design is a set of steps that help create, implement, and maintain business data management systems. The primary goal of database design is to generate physical and logical models of designs for the proposed database system.
What Does Good Database Design Look Like?
A good database design process is governed by certain rules. The first rule in creating a database design is to avoid data redundancy. This wastes space and increases the possibility of errors and inconsistencies in the database.
The second rule is that the accuracy and completeness of the information are essential. A database containing wrong information leads to incorrect analysis and reporting. As a result, it can mislead decision-makers and negatively affect company performance. Therefore, it is important to keep some rules in mind when designing a database for your organization.
So, how can you make sure your database design is good? A well-designed database is one that:
- Distribute your data into tables based on specific subject areas to reduce data redundancy
- Provide the information needed to link the data in the tables to the database
- Support and guarantee the accuracy and reliability of the data
- Meet your data processing and reporting needs
- Operate interactively with database operators
Importance Of Database Design
Database design defines the database structure used to program, store, and manage information. In order to ensure data integrity, you should design a database that stores only relevant and valuable information.
A well-designed database is essential to ensure data consistency, eliminate redundant data, execute queries efficiently, and improve database performance.
Data reliability depends on the structure of the table, while the creation of primary and unique keys ensures uniformity in the stored information. You can avoid repeating data by creating a table of possible values and using a key to represent the value. So, whenever the value changes, the change happens only once in the main table.
Since the overall performance of a database depends on its design, good database design uses simple queries and faster execution. It is also easy to maintain and update. On the other hand, when the database is poorly designed, even trivial outages can damage stored events, views, and applications.
Database Design Techniques
Two common methods used for database design are:
Data Normalization: Tables are organized in a way that reduces data redundancy and dependency. Larger tables are divided into smaller tables and linked to each other using relationships.
Entity-Relationship (ER) Modeling: A graphical database design approach that models entities and attributes and defines the relationships between them to represent real objects. An entity is any real item that is different or unique from its surroundings.
Database Design Steps
The first question to ask when designing a database is how to define the database structure.
Database design generally starts with identifying your goal. Then the relevant data are collected and organized in tables. Next, you define primary keys and analyze the relationships between different tables for efficient data design. After modifying the tables, the last step is to apply the normalization rules to standardize the tables.
Let’s explore these database design steps in detail:
Specify the Database Target
The first step is to determine the purpose of the database. For example, if you run a small home business, you can design a customer database that maintains a list of consumer information to generate emails and reports. Hence, it is vital to understand the importance of a database.
At the end of this step, you will have a strong mission statement that you can refer to throughout the database design process. This will help you focus on your goals when making important decisions.
Find And Integrate the Necessary Data
The next step is to gather the types of information you might want to store in the database. Start with existing data and consider the questions you want your database to answer. This will help you decide which data should be recorded.
Distribute Data into Tables
Once you have gathered all the necessary data items, the next step is to divide them into major entities or subject areas. For example, if you are a retailer, some of your main entities could be products, customers, suppliers, and orders. Then each entity becomes a separate table.
Change Data Items to Columns
The data is separated into tables so that each data item is converted into a field and represented as a column. For example, the customer table might contain fields such as name, address, email address, and city.
After determining the initial set of columns for each table, you can modify them. For example, you can record customer names in two separate columns: first name and last name. Likewise, you can store the address in five separate columns by address, city, state, zip code, and region. This will make it easier for you to filter information.
Identify the Primary Keys
The next step to improving database design is to choose a primary key for each table. This is the primary key of a column or set of columns that is used to uniquely identify each row. For example, in your customer’s table, the primary key could be the customer ID. This allows you to identify unique rows by customer ID.
There can also be more than one primary key, which is called a composite key and contains multiple columns. For example, in your order details table, the primary keys could be order id and product id. A composite key can be constructed using fields with the same or different data types.
Similarly, if you want to know about the sales of your product, you can identify the product ID from the Products table and the order number or ID from the orders table.
Determine How the Tables are Related
After dividing the data into tables, the information must come together in a meaningful way. Therefore, you can explore each table and identify the relationship between the tables. If needed, you can add fields or create new tables to simplify the relationship based on data types.
In this step, you will create one-to-one, one-to-many, and/or many-to-many relationships between different table entries. When an item from one table is related to an item from another table, it is called a one-to-one (1:1) relationship.
In a one-to-many (1:M) relationship, an item in one table is related to many items in another table, for example, a customer submits multiple orders. A many-to-many (M: N) relationship occurs if more than one table item is related to many other table items.
Improve your Database Design
Now that you have all the tables, fields, and relationships you need, the next step is to refine the database design by creating and populating the tables with information. Test sample data by running queries or adding new ones. This will help you analyze your design, identify its weaknesses, and correct possible errors. If necessary, make changes to reduce these problems.
Run the Normalization Rules
The final step is to implement normalization rules for your database design. A systematic approach eliminates redundancy and unwanted features, such as insertion, update, etc. anomalies. The multi-step process stores data in tabular form, which helps eliminate redundant data from relational tables.
Database Design Example
Tips for Database Management
To help increase database productivity, you can follow these database management practices:
Determine Business Goals
A targeted and actionable database management strategy should reflect your business needs and define the metrics you use to track your success. If you don’t spend enough time deciding what data to collect and how to use it effectively, you risk wasting internal resources collecting the wrong data, accumulating too much data, or missing important opportunities.
Establish Policies and Procedures, including Backup and Recovery Procedures
Establishing specific backup and recovery policies and procedures will prepare your team to be more effective should the worst happen. Determine smart actions that you can plan in advance. This exercise keeps the team focused and gives you a chance to handle worst-case scenarios well.
As you plan your disaster response, you can use flowcharts and process mapping to visualize everything and give your team a useful overview.
Although not every disaster is completely predictable or preventable, you can improve data security and manage the risks associated with worst-case scenarios for your database. Maintenance planning, backup, and recovery are the best options to protect important things.
Database administrators who understand industry best practices for database security are valuable allies in the fight against data loss, security breaches, and database compromise.
Focus on Data Quality
Your database manager should work to maintain a high standard of data quality, remove data that does not meet standards, and adapt quality standards to your changing strategy.
Reduce Duplicate Data
Duplicate data slows down database performance and can cause your efforts to fail. Many times, duplicate data leads to wasted internal resources and duplicated team effort. For example, if a customer record is repeated in a CRM, the service team may literally spend twice as much time fixing the same problem again.
Make Data Easily Available
You need to make sure that users can benefit from the data. Internal users, end-users, and other stakeholders who have access to your database should know how to use it and be able to easily benefit from it.
Let’s briefly say what database design is. The database design process helps you simplify the design, development, implementation, and maintenance of your enterprise data management system.
A good database design can help save disk storage space by reducing data redundancy. While maintaining data accuracy and reliability, it allows you to access data in a variety of ways. In addition, a well-designed database is easier to use and maintain and makes integration easier.