Database Landscape: The Basics and Practical Examples

Database landscaping is the process of analyzing a database and identifying areas for improvement. This analysis results in a documented set of recommendations for improving performance, security, and user experience. A well-executed database landscape will also uncover potential problems before they impact your business operations. A detailed database landscape document lists all of the data elements in your databases along with their type, length, format, source, and other properties relevant to their effectiveness. It also details recommendations for making each data element more effective by improving its storage location or other properties. This article covers the basics of database landscaping and practical examples you can use to implement these concepts in your business.

What is Database Landscaping?

As the name implies, database landscaping is the process of looking at your database landscape and identifying areas for improvement. This analysis results in a documented set of recommendations for improving performance, security, and user experience. A well-executed database landscape will also uncover potential problems before they impact your business operations. A detailed database landscape document lists all of the data elements in your databases along with their type, length, format, source, and other properties relevant to their effectiveness. It also details recommendations for making each data element more effective by improving its storage location or other properties. This article covers the basics of database landscaping and practical examples you can use to implement these concepts in your business.

Key Value of Database Landscape

● Faster retrieval - When you know how your data is organized, it is easier to retrieve. Appropriately structuring your data will help you retrieve it faster. This means you save time and money by reducing the amount of time you spend on your queries.
● More accurate information - If you store your data in the appropriate location, you can be more confident that it is accurate. The data may also be easier for your users to find if it is in an easily identifiable location.
● Reduced costs - It is much easier to manage a smaller database than a larger one. This means you may be able to reduce your storage costs.
● More secure data - If you learn how to properly secure your data, you will have less chance of a security breach. Efficiently storing your data in a smaller database makes protecting it easier.
● Improved user experience - Data is more accessible when it is stored in an appropriate location. Your users will be able to find the information they need more easily.

Identify Data Elements for Improvement

The first step in database landscaping is identifying the data elements that need improvement. During this process, you will want to look for data elements that are either inefficient or unnecessary. You will also want to identify data elements that are difficult to find or use. Examples of inefficient data elements include large text fields, date/time fields with unnecessary precision, and binary data that does not need to be stored. Examples of unnecessary data elements include fields that contain null values and fields that only apply to a particular subset of your users. Examples of data elements that are difficult to find include fields buried in a specific table and fields that are not labeled appropriately.

Move Data from One Location to Another

If you discover that some data is in the wrong location, you may be able to move it to a new location. In some cases, this may require you to use a different data type, which is discussed below. If the data in a specific field applies to many other places in your database, you may want to consider moving it to a central location. Doing so will help improve the overall efficiency of your database by reducing the amount of data stored in each location. Suppose you discover that certain data only applies to a subset of your users. In that case, you may consider moving it to a specific location where those users will only access it.

Change the Data Type of a Specific Element

If you identify an inefficient data type, you may be able to change it to a more efficient data type. When you change the data type, you need to be careful that you don’t lose any valuable information. When changing a data type, you need to be careful to avoid truncating the data. Data truncation occurs when you change the data type of a field and the new data type is too small to hold all of the data. In this situation, some of the data is lost. You also need to be careful when changing the data type of a field because certain data types cannot be used in certain places. For example, a field that holds integers can be used in a calculation, but it cannot be used in a date/time field.

Establish a Review Process and Determine Frequency

Now that you’ve identified your data elements for improvement and made recommendations for improvement, you need to decide how often you will review these elements. The ideal frequency for database landscaping will depend on your organization. You will want to establish a review process that works for your business. You may decide to review your data elements annually, quarterly, or even more frequently. You may also want to review only certain data elements each time. This will depend on your particular situation. Whatever process you decide on, make sure it is documented so your team members know how they should proceed.

Summary

As the name implies, database landscaping is the process of looking at your database landscape and identifying areas for improvement. This analysis results in a documented set of recommendations for improving performance, security, and user experience. A well-executed database landscape will also uncover potential problems before they impact your business operations. A detailed database landscape document lists all of the data elements in your databases along with their type, length, format, source, and other properties relevant to their effectiveness. It also details recommendations for making each data element more effective by improving its storage location or other properties.