The first step of every database design project is data modelling. This process entails the analysis and definition of all the different data within a business as well as the development of relationships, semantics and constraints between these data points. In other words, during the data modelling process, the data team access all the business data, and select the appropriate dataset that defines the business process and how best we can organize the selected dataset. The action usually produces a data diagram that can be used to represent a business at a glance. Data modelling plays a key role in transforming the organisational data into quality data hence optimising the analytics of the business which positions the executive users to make data-driven decisions.
Why it is important to model business data?
Data modelling usually comes with a cost (time, money, human resources) however for effective data governance every organization should take this action. Below are some reasons why data modelling is key for every organization:
- Data modelling provides a platform for every organisation member to communicate their data needs and how it fits into the business requirement effectively.
- It saves resources on IT and processes investments through timely planning schemes.
- Organisations can easily make data-driven business decisions due to the increased performance of data retrieval and analytics.
- Data redundancy can be curbed effectively to reduce errors as well as storage resources.
- Missing data can be identified easily with the use of a data model.
- Every organisation can achieve the “one-truth” policy through effective data modelling.
- While it takes time to set up a robust data model for a business, it can be maintained in a cheaper and faster manner since it provides a common, consistent and predictable way of defining and managing data resources across an organization.
For a data developer to effectively build a robust data model for any business, he must take three approaches to deploy.
They are conceptual, logical and physical approaches.
Data Modelling Overview and their data experts
This approach is the first phase of the data modelling exercise. In this phase, the full structure of the business data is accessed to determine what the model will contain based on the business activities, how it will be arranged and which business rules/requirements are involved. The key business stakeholders and data team are fully involved in this phase.
The logical approach of the data modelling exercise focuses on the business information and defining the rules that will best suit the business information. In this approach, business data unit definitions, describing and identifying their attributes (indexing, primary keys) and the relationship between these units (foreign keys) are addressed. The data team (data architects) and business analysts are responsible for the development of a technical road map for rules and data structures which will be the building block for the physical approach.
As the name goes, the main objective of this approach is the implementation of the actual database. While most of the design has been mapped out in the logical approach, the database administrators and developers own this phase by transforming the business data units and their attributes into tables, columns, views, partitioning, access profiles and security (authorization). They usually consider the database management system (DBMS), location, storage or technology in this approach.
The role of data modelling in the data management of any organisation. With the full understanding and application of this subject matter, data experts within an organization can properly organize and provide intelligence from the datasets (small or large) effectively to other stakeholders while meeting the business requirements. While there are three approaches to modelling data in any organization, it is imperative that the business have a full grasp of these concepts so they can know when best to apply any or all of these approaches. All these approaches require different levels of technicality and data experts to execute these approaches carefully
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