Managed data
What is Data as a Service (DaaS) and why it matters
A clear definition of Data as a Service (DaaS): what it includes, how it differs from building your own infrastructure and why more companies adopt it.
Read articleKey takeaways
- Data modelling defines how data is structured and related.
- A good structure eases queries, reporting and consistency.
- Concepts like facts and dimensions organise analytical data.
- A bad model means slow reports and inconsistent figures.
- It combines technical and business knowledge.
Behind a good dashboard there is almost always a good data model. Modelling is one of those invisible disciplines whose absence shows immediately: slow reports, figures that do not match and questions impossible to answer.
What it is
Data modelling is the process of defining how data is structured, named and related so it is coherent and useful. It is the blueprint on which all analytics is built.
Essential concepts
- Entities: the business objects (customer, product, order).
- Relationships: how they connect.
- Facts and dimensions: facts are measurable (sales), dimensions the context (time, region).
- Granularity: the level of detail of each data point.
Entities
CustomerProduct
Facts + dimensions
Measure+ context
Reporting
FastConsistent
Why it matters to the business
A well-designed model answers questions quickly and consistently; a poorly designed one forces convoluted calculations, produces figures that depend on who pulls them, and makes every new question a project.
Who handles it
Modelling combines technical and business knowledge: you must understand both the data and the questions it will answer. In a managed service, the provider designs and maintains the model from the business needs, so leadership gets reliable reports without worrying about the underlying structure.
A good model answers questions quickly and consistently; a bad one makes every question a project.
In summary
Data modelling is the blueprint of analytics: it defines entities, relationships, facts, dimensions and granularity so reports are fast and consistent. A bad model produces slow, inconsistent reporting. It combines technical and business knowledge — handled for you in a managed service.
Sources & further reading
Frequently asked questions
What is data modelling?
The process of defining how data is structured and related so it is coherent and useful. It is the blueprint for analytics.
Why does it matter for reporting?
A good model enables fast, consistent reports; a bad one produces inconsistent figures and slow queries.
Do I have to design it myself?
Not in a managed service: the provider designs and maintains the model based on business needs.
What are facts and dimensions?
Facts are the measurable values (sales, costs); dimensions are the context (time, region, product) you analyse them by.
What is granularity?
The level of detail of each data point — e.g. per transaction vs. per day — which shapes what questions the model can answer.
What skills does modelling need?
Both technical knowledge of the data and business understanding of the questions it must answer.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.