Data Modelling is a process of structuring data collected from disparate sources to allow decision-makers to make informed decisions with analytics. With Data Modelling, organizations illustrate the types of data used, relationships among information, and organization of data. In other words, Data Modelling is a technique to optimize data for streamlining information flow within organizations for various business requirements.
Built for enhancing analytics, Data Modelling includes formatting of data and its attributes, building relationships among information, and grouping data. This not only assists companies in maintaining consistency but also enhancing the predictability of use cases they can carry out. Without proper Data Modelling, organizations fail to accomplish their business goals due to the absence of a well-defined roadmap for Data Analytics.
Data Modelling Process
As a discipline, data modelling invites stakeholders to evaluate data processing and storage in painstaking detail. Data modelling techniques have different conventions that dictate which symbols are used to represent the data, how models are laid out, and how business requirements are conveyed.
All approaches provide formalized workflows that include a sequence of tasks to be performed in an iterative manner. Those workflows generally look like this:
- Identify the entities The process of data modelling begins with the identification of the things, events or concepts that are represented in the data set that is to be modeled. Each entity should be cohesive and logically discrete from all others.
- Identify key properties of each entity Each entity type can be differentiated from all others because it has one or more unique properties, called attributes. For instance, an entity called “customer” might possess such attributes as a first name, last name, telephone number and salutation, while an entity called “address” might include a street name and number, a city, state, country and zip code.
- Identify relationships among entities The earliest draft of a data model will specify the nature of the relationships each entity has with the others. In the above example, each customer “lives at” an address. If that model were expanded to include an entity called “orders,” each order would be shipped to and billed to an address as well. These relationships are usually documented via unified modelling language (UML).
- Map attributes to entities completely This will ensure the model reflects how the business will use the data. Several formal data modelling patterns are in widespread use. Object-oriented developers often apply analysis patterns or design patterns, while stakeholders from other business domains may turn to other patterns.
- Assign keys as needed, and decide on a degree of normalization Normalization is a technique for organizing data models (and the databases they represent) in which numerical identifiers, called keys, are assigned to groups of data to represent relationships between them without repeating the data.For instance, if customers are each assigned a key, that key can be linked to both their address and their order history without having to repeat this information in the table of customer names. Normalization tends to reduce the amount of storage space a database will require, but it can at cost to query performance.
- Finalize and validate the data model Data modelling is an iterative process that should be repeated and refined as business needs change.
Understanding the Types of Data Models
As Data Modelling techniques are incorporated within organizations based on the business requirements, it is essential to align them with database design schemas. Consequently, it is vital to ensure all the three aspects — Data Modelling, business requirements, and database design schema — are taken into account while devising a strategy for superior data management and analytics workflows.
However, before embracing Data Modelling techniques, the below methodologies are incorporated for a successful implementation:
1. Conceptual Data Models
In conceptual data models, business requirements are assimilated to define the types of data needs, collection procedures, and security demands.
2. Logical Data Models
This model is highly prominent with companies that are heavily involved in data warehousing. Logical data models help organizations formulate data consolidation and segregation for simplifying Data Analytics.
3. Physical Data Models
With physical data models, companies finalize the relation among tables and deploy the right databases.
Types of Data Modelling Techniques
Hierarchical Data Modelling
Developed by IBM in 1960, hierarchical Data Modelling is a tree-like structure, which has one root or parent connecting to different children. The parent data is in direct association with child data points, making it a one-to-many relationship.
Although simple, hierarchical Data Modelling is not suitable for complex structures. As a result, hierarchical Data Modelling is not widely used in the data-driven world. Today, data analyses are performed by evaluating relationships among different data points, thereby requiring a many-to-many relationship structure. However, with a one-to-many relationships model, it becomes strenuous for companies to gain an in-depth understanding of collected information.
Relational Data Modelling
Relationship Data Modelling is the most well-known technique used in databases to support analytics initiatives. Data in relational Data Modelling is organized in tables that are in relation to each other.
Proposed in 1970 by Edgar F. Codd, relational databases are still the go-to Data Modelling for complex data analysis. Organizations use structured query language (SQL) to obtain and record data in the form of tables while maintaining the relationship intact for better consistency and data integrity.
Entity-Relationship (ER) Data Modelling
Entity-relationship Data Modelling was introduced by Peter Chen in 1976 that revolutionized the computer science industry.
Entity-relationship models are a logical structure where the relationship among data points is created based on specific software development requirements. Unlike relational Data Modelling techniques, entity-relationship Data Modelling is designed to support business processes in a particular order.
Even if two datasets can have numerous relationships, entity-relationship is only created based on the data points needed for accomplishing a task while minimizing data privacy risks.
Object-Oriented Data Modelling
Object-Oriented is used to represent the real world by grouping objects into classes hierarchy. This structure has been used with several object-oriented programming languages that allow foundational features like encapsulation, abstraction, and inheritance.
Object-oriented Data Modelling techniques are used for representing and working with complex analyses.
Dimensional Data Modelling
Introduced by Ralph Kimball in 1996, dimensional Data Modelling is leveraged to optimize data retrieval from data warehouses.
In dimensional Data Modelling, data are represented in cubes or sets of tables to allow slicing and dicing for better visualization or analysis. With dimensional Data Modelling, users can carry out in-depth analysis by accessing data based on different viewpoints.
Organizations implement two types of dimensional Data Modelling techniques — star schema and snowflake schema.
Benefits of Data Modelling Techniques
1. Data Quality
For any data science project, almost 80 percent of the time is lost in data wrangling. However, with Data Modelling, you define business problems and then plan the data collection process accordingly.
This not only streamlines the entire data flow but also enhances the data quality. Companies obtain blueprint by planning to implement Data Modelling techniques, which empowers the data analysts in extracting data without worrying about the data quality. Desired Data Modelling has the potential to expedites data analysis by creating relationships among data points.
2. Reduced Cost
By implementing Data Modelling according to the business requirements, you are more likely to follow the defined roadmap for data collection and analysis.
This will reduce the cost since the needs of businesses are taken into account while deploying the Data Modelling techniques.
Often companies with poor Data Modelling techniques have to revamp their data collection process, thereby increasing the operational costs. However, if an organization has the right Data Modelling strategy from the very beginning, it not only reduces costs but also expedites analytics.
3. Quicker Time to Market
By deploying the perfect Data Modelling techniques according to the needs within departments, companies can reduce the time for bringing products and services.
A perfect Data Modelling technique can eliminate several bottlenecks that companies witness while deploying data strategies.
