Introduction
For data engineers, data modeling is one of the crucial steps that should always be followed when planning to move for analysis or machine learning purposes. Data modeling is often described as a structured representation of the data of any organization. This often a visual representation often taking the form of an ER diagram(s). This representation helps us in understanding the relationship, constraints and patterns that exist within our data. It is often useful in gaining business value as one can see all the available data and the gaps. The data model can also be used to trace the data lineage and see the flow of the data from source to destination, such as data warehouse, data lake or any other analytics solution.
During data modeling, one can also come across the gaps in data, look at the quality of the data and take appropriate steps to overcome these difficulties.
Ideally, data models are documents with long lives and they evolve with the changing business needs. They play an important role in supporting business processes and planning IT architecture and strategy. They are easily shared and help in communicating the ideas and the requirements. Data models can be used as tools to validate the data needs of a business and the model can be utilized further to build AI or machine learning strategies for the organization.
Why is data modeling important?
Although in the world where working quickly is considered a plus, data modeling might take a backseat as it is time consuming and doesn’t have any immediate benefits, but in the long run a good data model can benefit the teams working with data or consuming the data. By providing the single source of the truth, a data model makes it easier to communicate with other teams. Every data point means the same across the organization and follows the same standard. It is easier to distribute a data model to different teams in the organization, making collaboration easier.
A good data model organizes the data in a structured way making its understanding and management easier. It also helps in identifying and rectifying the data quality issues and leads to better data quality. A good data model reduces redundancy thus allowing a single source of truth and making data management easier. It also simplifies data retrieval, thus improving the system performance. Data modelling also promotes the standardization and consistency of the data across the organization, thus minimizing the data quality issues. Data modeling also helps in facilitating the data governance initiatives making it easier to see where the compliance needs to be enforced and where it is working well. It also helps in maintaining the data lifecycle. We could easily conclude that a well-maintained data model is a long-term investment that provides value throughout the lifecycle of the data.
Data Model Types
Popularly there are three types of data models:
1.Conceptual Data Models: Conceptual data Models are the simplest and most abstract type. It is an overall layout of the data and the set of relationships and the rules within that data. Here data is divided into granular subjects and the business rules, entity classes, constraints, objects, and other limitations are found here.
2.Logical Data Models: Logical models are the are the expansion of the conceptual data model. They are more detailed, as in, they have clear data types, cardinalities, keys, constraints, and validations.
3.Physical Data Models: They are the lowest level of data modeling, where the logical data models are translations into the specific process system, like a data warehouse. Physical data models help in optimizing the system for the performance, scalability, security, and availability of the data. It also considers the limitations of the platform that we are choosing for our data and analytics needs.
Data Modeling Steps
There are many different steps that the literature identifies in creating a data model, the following are some steps that are part of the modeling process.
Sugandh Wafai
Consultant Data Analytics