Data science modelling techniques for organisations

data science modelling techniques

Everyday, 2.5 quintillion bytes of data are generated. With so much information at our disposal, it is becoming increasingly important for organisations and enterprises to access and analyse relevant data to predict outcomes and improve services.

However, arbitrarily organising data into random structures and relationships is not enough. In order to access the data properly and extract the most out of it, it is essential to model your data correctly.

The Big Data revolution has arguably provided a more powerful information foundation than any previous digital advancement. We can now measure and manage large volumes of information with remarkable precision. This evolutionary step allows organisations to target and provide more finely-tuned solutions and use data in areas historically reserved for the “gut and intuition” decision-making process.

Data science modelling techniques play a crucial role in the growth of any organisation that understands the importance of data-driven decisions for their success. Having your data in the right format ensures that you can get answers to your business questions easily and quickly.

What is data modelling?

In simple terms, data modelling is nothing but a process through which data is stored structurally in a specific format. Data modelling is important because it enables organisations to make data-driven decisions and meet varied business goals.

Typically, a data model can be thought of as a flowchart that illustrates the relationship between data. It enables stakeholders to identify errors and make changes before any programming code has been written. Alternatively, they can be introduced as part of reverse engineering efforts to extract other data models from existing systems.

Importance of data science modelling techniques

Data modelling represents the data properly in a model. It rules out any chances of data redundancy and omission, helping analysis and processing. Furthermore, data modelling improves data quality and enables concerned stakeholders to make data-driven decisions. This clear representation makes it easier to analyse data properly. It provides a quick overview of the data, which can then be used by the developers in different applications.

Since a lot of business processes depend on successful data modelling, it is necessary to adopt the right modelling techniques to get the best results.

Types of data models

There are three types of data modelling techniques for business intelligence: Conceptual, logical, and physical.

Conceptual data modelling examines business operations to create a model with the most important parts (such as describing a store’s order system). Essentially, this data model defines what data the system will contain.

Logical data modelling examines business functions (like manufacturing and shipping) intending to create a model describing how each operation works within the whole company. It also defines how a system should be implemented: By mapping out technical rules and data structures.

Physical data modelling examines how the database will actually be implemented, intending to model how the databases, applications, and features will interact with each other. Here, the actual database is created while the schema structure is developed, refined, and tested. Data models generated should support key business operations.

Drive key business decisions using data science modelling techniques

Clearness: How easy it is to understand the data model just by looking at it.

Flexibility/scalability: The ability of the model to evolve without making a significant impact on code.

Performance: You can attribute performance benefits based on how you model the data.

Productivity: An organisation’s model needs to be easy to work with.

Traceability: The ability to manoeuvre through historical data.

The data model of every application is the heart of it

In the end, it is all about data: Data comes flooding in from everywhere, data is processed following business rules, and finally, data is presented to the user (or external applications) in a convenient way.

With new possibilities to easily access and analyse their data to improve performance, data modelling is morphing too. More than arbitrarily organising data structures and relationships, data modelling must connect with end-user requirements and questions, as well as offer guidance to help ensure the right data is being used in the right way for the right results.

Business performance, in terms of profitability, productivity, efficiency and customer satisfaction can benefit from data modelling that helps users quickly get answers to their business questions.

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