A data dictionary provides a tool that allows us to communicate business stakeholder needs in such a way that your team can more quickly and easily design a data structure or relational database to meet those needs. Data dictionary defines the data objects in the database of each user, and you can also use schema to er diagram to make your work better. Basically, it provides complete details about the business data, such as standard definitions of data elements, allowable values, and their meanings. Typically, it is organized in a spreadsheet format. It is a tool specialized for IT system analysts and anyone who wants to modify those systems.
A data dictionary provides an attractive and quick report on the data and the resources that the objects are using and hence creating the data management easily. It provides more detail in the best way about each attribute of a business concept. Let’s have a look at the most common elements used in a data dictionary:
While we’re going to discuss the core elements of a data dictionary, it’s not uncommon to document additional details about each element, which includes the physical database field name, the source of the details, the field length, the concept or table in which the attribute is contained, and any default values.
Basically, it is a unique identifier that is expressed in business language, that labels each attribute.
It defines what type of data is allowable in a place. Common attribute types include numeric, text, look-ups, date/time, enumerated list, unique identifiers, and booleans.
It indicates whether the information is needed in an attribute before a record can be saved or not.
Well, these are the key features and core elements of the data dictionary, now we’ll talk about the key benefits of creating a data dictionary. Hence, there are lots of advantages of the data dictionary but we’ll talk about some main benefits, let’s have a look at them:
- More efficient programming
- Better data consistency
- Superior categorization of incoming data
- Compliance with all legal data regulation requirements
- Higher quality data
- Elimination of data redundancies
- Stronger standard enforcement across datasets
- Established data ownership
- Improved utilization of existing data
- Faster and more accurate data analysis
Since databases are very huge and have many views, tables, indexes, constraints, etc, it will be difficult for anyone to remember. It helps the data dictionary creator by providing all the details in it. Since it provides great documentation on each object, it helps to understand the needs and design to the perfect extent. It gives clear and well-structured details about the database.
One can analyze the needs, any redundancy like duplicate tables, views, columns, etc. and even During implementation, it provides a base against which creators compare their data description. But without a perfect Data Dictionary, you will need to depend on the people’s knowledge who know the sentence or databases yourself to do a lot of digging through the code of reports, applications, errors, queries, and many hours of guesswork.