Why you need data modelling in your business
Collecting large amounts of data supports organisational goals and helps businesses of all kinds improve their competitive positioning.
But before you can put that data to work for your company, you must first learn how to visualise the various ways it’s connected. Doing so allows you to organise, store and access it better.
To really bring your data to life, you’ll need to understand data modelling.
In this guide:
- What is data modelling?
- Why is data modelling important?
- Data modelling versus data analysis
- Types of data models
- Data modelling examples
- The data modelling process
- Evaluate a platform to actualise your data model
What is data modelling?
Data modelling is the process of mapping and visualising different data sources into models. These models are then put together in a way that makes the information easier to understand, manipulate and analyse.
The goal of data modelling is to produce a clear picture of a company’s data through high-quality, consistent and structured models. Using those models, business teams can clearly define and communicate the requirements for a computer system or database design and the technical teams can build the design in response to those requirements.
Data modelling also helps businesses identify missing or redundant information. For example, a business can use data modelling to locate inefficiencies that may be leading to unnecessary spending. By modelling data from various departments, the business can analyse how resources are used across the organisation, discover which areas have too many or too few and reallocate them accordingly.
How are data models built?
Data models are created by IT in collaboration with commercial teams. They are centred on business requirements and incorporate feedback from business stakeholders on what is needed from the information system or database design. These are known as business rules. Once the data requirements are clearly defined, the data architects, data engineers and other technical experts will build it.
Why is data modelling important?
When you empower your team members to apply their skills in data modelling by investing in the right technologies, you can more efficiently organise the information your company collects. In turn, you’ll get the most out of your data and leverage it to support critical business processes.
Data consistency
Data modelling tears down silos and ensures information consistency across all departments, systems and databases.
Improved data quality
When your team can easily share and access information, overall data quality and usability increase significantly.
More efficient database design
Data modelling reveals potential information logjams, meaning you can eliminate these bottlenecks and create a more efficient data management system.
Reduced errors and redundancies
Through data modelling, you can optimise workflows, reduce the need for redundant manual input and decrease the likelihood of costly data entry errors.
Improved transparency throughout the company
By using data modelling, you can improve information transparency across the entire company and provide decision-makers with the information they need to capitalise on emerging opportunities.
Data modelling vs. data analysis
Data modelling and data analysis are often thought to be the same. But they’re different concepts that require entirely different skill sets.
Data analysis
Data analysis is what you do with the data you have access to. It’s about filtering data to find the most important insights in the form of reports, predictions, graphs or other visualisations. Data analysis helps determine why aspects of a business are working the way they are.
Data modelling
Data modelling is about creating the right conditions to make that analysis possible. Creating and fitting together a company’s data models needs to happen before teams can perform data analysis. It’s about determining which types of data to bring together — and in which ways — to get the desired results.
Types of data models
There are three main types of data model — conceptual, logical and physical. Which of these you choose depends on:
- how complex your model is
- your business requirements for the model
- how you will use it
Each of the three options has its own way of storing and organising data as well as handling data retrieval:
Conceptual data modelling
Also called enterprise data modelling. This type follows a big-picture approach that shapes the data around the main business requirements and needs of your stakeholders such as business analysts. A conceptual model identifies your different data entities and shows how they are related.
Logical data modelling
Logical data modelling identifies the technical details and how they will support business goals. It works by exploring in detail how each set of data relates to the others. A logical data model expands on a conceptual one by factoring in attributes within data entities and how those attributes interact.
Physical data modelling
This model is the blueprint for a business’s database design — generally built for a particular data storage system. It lays out precisely how each database will be built and how all databases, applications and features will interact.
Data modelling examples
Relational
Relational data modelling is a technique that IBM developed in the 1970s and it maps the relationship between data elements stored in different tables. The relational data modelling method is popular among many modellers because it doesn’t require a deep understanding of physical information storage.
Entity-relationship
Entity-relationship (ER) data modelling is a more complex variant of the relational data model, mapping entities and identifying specific attributes as they relate to these entities. An entity could be customer data, employee data, product information or an invoice, while an attribute might be a customer email address, employee surname, product price or the date that an invoice was created.
Hierarchical
Hierarchical data models typically feature a tree-like visualisation of parent and child relationships. IBM’s Information Management System (IMS) is the most well-known example of a hierarchical data model and is still used by many businesses, though relational data models have otherwise largely replaced hierarchical ones. Extensible Mark-up Language (XML, as it’s more commonly known) is an alternative to IMS that is also still in use.
Dimensional
Dimensional data modelling is primarily used in data marts and warehouses for business intelligence (BI) applications. The technique is designed to optimise data retrieval speeds so organisations can efficiently tap into the information stored within their data warehouses. A dimensional data model consists of fact tables, each of which contains information about events or transactions, such as product purchases.
Object-orientated
Object-orientated data modelling is similar to ER modelling, but in this framework, entities are abstracted into objects. Objects that share similar attributes can be moved into classes and grouped hierarchically. Object-orientated databases can incorporate tables, but they also support more complex data relationships. Many hypertext and multimedia databases use object-orientated modelling.
The data modelling process
Data modelling is a fluid process that can be shaped to the needs of your organisation. However, most teams start by creating a conceptual model, followed by a logical model and finally a physical data model. This enables your organisation to progress from a high-level visualisation of your data to a more technical representation of the database itself.
Following the established data modelling process will support stakeholders as they meet to discuss the company’s data processing and storage needs in extreme detail. During these discussions, decision-makers will generally progress through a workflow that goes as follows:
- Identify the entities. Organisational leaders will identify and list the various entities and departments that exist within the business.
- Identify any key properties for each entity. Decision-makers will identify attributes that differentiate each of the company’s entities.
- Identify entity relationships. Business leaders will develop a model that outlines interconnected entities and shows how they relate to each other visually.
- Identify the data attributes that need to be in the model. Company leaders will list the specific attributes that need to be included in the model, such as customer email addresses and customer names.
- Map the attributes. Once data attributes are created, they will then be mapped to each entity.
- Finalise and validate its accuracy. Before finalising the data model, decision-makers will validate its accuracy and ensure that all relevant entities and attributes are included in the initial framework.
By following this reproducible process, organisational leaders can effectively develop increasingly technical data models and clearly identify the information needs of the business.
Evaluate a platform to actualise your data model
Having a visual representation of how your data is connected will help you to organise your data so you can meet your company’s need for information. However, before you can start designing your data model, you must first implement a robust data collection and storage platform.
Adobe Real-Time CDP collects B2C and B2B data from across systems and immediately unifies it into profiles ready for activation across any channel.
To learn more about Adobe Real-Time CDP, watch the overview video or take an interactive tour.
