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Data modelling isn’t dead: Data Modelling in Modern Data Analytics

Data Modelling

Data modelling often gets treated like a relic of the past. Data technologies and methodologies seem to change at the speed of light, so it’s easy to overlook traditional data modelling in favour of something new.

As we strive for more sophisticated insights and faster decision-making, the importance of a solid foundation becomes critical. Skipping data modelling comes at the cost of unclear results and untrustworthy systems that compromise insights and decision-making.

Data Modelling

So, what is data modelling? And why is it important?

At its core, traditional data modelling is the process of defining the structure of data to ensure its accuracy, consistency, and quality. It involves creating diagrams of data structures and the relationships between them.

For example, let’s consider a scenario where data modelling is applied to the Mining industry to track ore extraction. Entities such as ‘Mine,’ ‘Geological Formation,’ and ‘Extraction Equipment’ are identified, each with specific attributes like mine location, geological composition, and equipment capacity. Relationships are established, indicating that a geological formation can be present in multiple mines, and extraction equipment is associated with a specific mine site. Normalisation ensures data efficiency by preventing duplication, such as storing geological details separately to avoid redundancy in each extraction record.

This data model aids in streamlining mining processes, enabling efficient resource allocation, predicting extraction yields, and analysing geological patterns for informed decision-making in the dynamic mining environment.

The Relevance of Traditional Data Modelling in Modern Data Analytics

In the era of advanced data analytics tools, where does data modelling fit in? The answer lies in its seamless integration.

Modern analytics tools benefit immensely from the structured and organised data that traditional modelling provides. It acts as a bridge between raw data and actionable insights, ensuring that data is not just a collection of bits and bytes but a strategic asset.

Companies have also started to realise the true value of owning their data stack from end to end. Traditionally, tools like Google Analytics modelled data on your behalf. The issue with automated modelling is that data isn’t aggregated in a useful way to your specific business needs.

Owning your own data stack means that data is more relevant and therefore more useful to internal data consumers, but it also means that data modelling is more important than ever.

Data modelling can support your data stack through the following:

Data Modelling

Streamlined reporting:

Reporting is the linchpin of data-driven decision-making. Data modelling simplifies reporting by providing a structured framework for organising and interpreting data. This structured approach ensures that data is easily accessible, interpretable, and relevant to the specific needs of the reporting process.

Creating a Structured Framework:

Imagine trying to make sense of a vast library without a cataloguing system. This can be a critical pain point for most companies when moving to their own data stack. Data modelling acts as the cataloguing system for data, allowing you to create a structured framework that facilitates efficient reporting. Each piece of data is assigned its place, making it easier for analysts and decision-makers to locate and utilise information.

Improving Data Visualization for Intuitive Reports:

Data visualisation is a powerful tool for conveying complex information in a digestible format. Data modelling contributes to this by creating a foundation that allows for more intuitive and effective data visualization. Well-modelled data lends itself to clearer charts, graphs, and dashboards, enabling you to grasp insights at a glance.

What are some of the benefits of data modelling?

  • Improve decision-making through specialised and relevant reports,
  • Reduce the time it takes to transform raw data into actionable insights,
  • Maximise the value of your data by ensuring that the data collected and stored is optimised for analysis, reducing the need for costly data clean-up,
  • Improve communication between developers and business intelligence teams,
  • Ease and speed the process of database design at the conceptual, logical and physical levels.

For all the benefits of owning your data stack, modelling your data in this way is a real skill that requires a deep understanding of the business and the data you’re working with.

Designing and building data models, especially for the unique needs of a particular team within a complex business, can be challenging.

These challenges can hinder the seamless adoption of data modelling practices, potentially leading to the loss of its numerous benefits.

At Integrove, we work to understand your business and data. Our team of skilled professionals collaborates with your company, ensuring a smooth integration of traditional data modelling practices. By addressing the challenges head-on, we empower companies to unlock the full potential of data modelling, turning obstacles into opportunities for enhanced data-driven decision-making.

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