What is data modeling? (GoSmarter Documentation)

Table of Contents Show

Definition of data modeling

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.

Data modeling is the process of structuring and organising data as a map that clearly visualises data sources, transfers and relationships. It is applied during the design stage of an application or system to understand how data will be used to support it.

Executive view

The quality of a business data model reflects the organisation of the whole business. A messy data model with inefficient storage and access solutions can reflect dysfunctional business processes, and lead to slow, inefficient decision making. A data modeling initiative can sit as part of an initiative for cultural and process changes.

Data modeling helps businesses:

build products that use data efficiently and meet business goals.
empower teams to make data-driven decisions efficiently.
manage large volumes of data.

Business function leader view

Data modeling provides a clear, big-picture view of your data sources, transfers and relationships. Developing a data model is the first step towards ensuring that your products use data efficiently and reducing unnecessary server costs, and enables you to make decisions more quickly and efficiently.

You may need this service if:

you are building an application that will access multiple data sources.
you manage a large volume of data and want to store and access it efficiently.
it takes a long time to make data-driven decisions because your data is difficult to find and analyse.

KPIs you should consider measuring for this are:

reduced costs of data storage and querying
improved efficiency of project delivery
improved efficiency of decision making when a big picture view is quickly and easily available

Technical view

Data modeling helps teams to design systems and software that utilise data, and to design effective database structures. Your application will need to access and store data efficiently to reduce server costs. Developing a clear view of your data sources and relationships will guide your development to avoid repetitive queries and manage data effectively.

Data modeling helps deliver:

products that can integrate data from several sources and process it effectively.
products that store and process data efficiently to reduce load.

Get this service if you encounter:

high expenses from repeated, unnecessary server requests.
high expenses from data storage.
lags and bugs in your product due to inefficient data processing.

Key criteria to consider are:

Are you able to identify all of your data sources and how data is queried by your application?
Do you have the time and resources available to audit your data storage and processing solutions?

Data modeling in metals manufacturing

Manufacturing data models tend to be more relationally complex than they first appear, because the physical world they represent has real dependencies that the data has to preserve. A single stock length of bar has a heat number, which links to a mill certificate, which certifies a chemical composition and set of mechanical properties. When that bar is cut, the resulting pieces need to inherit the heat number and certificate link of the parent — and any remnant that goes back into stock needs to carry the same traceability forward. Get that relationship wrong in the data model and the traceability chain breaks silently: the system looks fine, the certificate still exists somewhere, but it’s no longer linked to the material it’s supposed to certify.

The same is true for orders and inventory. An order line item needs to reserve against specific, traceable stock — not just decrement a generic quantity — if a customer ever needs to know exactly which heat number went into their delivery. A well-designed data model makes that kind of query trivial; a poorly designed one makes it impossible to answer without manually cross-referencing paperwork.

This is why systems purpose-built for metals manufacturing — as opposed to a generic ERP module adapted for the industry — tend to model heat numbers, certificates, and cutting plans as first-class, connected entities rather than free-text fields bolted on afterwards. The data model is effectively the traceability guarantee: if the relationships aren’t captured correctly at the schema level, no amount of reporting on top of it can recover the missing link.