Definition
Structured data means data that is organized and formatted consistently, making it easily searchable and analyzable. It is usually stored in databases or spreadsheets, with each piece of information assigned to a specific field or category. This form of structure allows AI systems to be more efficient in processing and interpreting data, resulting to more accurate predictions and recommendations [1].
A good example
of a structured data is a Microsoft Office Excel file of a departmental students’
course result. This data can comprise both textual elements and numbers, such
as students’ registration number, names, lab score, test score, grade, etc.
A fictional students’ result in a structured form.
Origin
Origin of Structured
data in AI can be traced back to the 1960s-70s with early database systems and
knowledge representation work by researchers like Marvin Minsky. The concept
formalized in the 1980s-90s through relational databases and knowledge
engineering. A crucial moment arrived in the early 2000s, with Tim
Berners-Lee's Semantic Web proposal, leading to Schema.org and Google's
Knowledge Graph. Presently, structured data provides the organized knowledge
foundation that improves modern AI systems and allows advanced reasoning
capabilities.
Context and
Usage
There are
several use cases of Structured data such as in AI model training, customer
relationship management (CRM), and in financial reporting and analysis.
In AI model
training, it enables AI systems learn to recognize patterns and make accurate
predictions or recommendations by feeding it.
In Customer
relationship management (CRM), CRM software creates datasets that show patterns
and trends in customer behavior by using analytical tools to process structured
data.
When it comes to Financial Analysis and Reporting, Financial institutions and accounting departments analyze transactions, balance sheets, income statements, and other financial records using structured data [2].
Why it Matters
In Machine learning, structured data provides models high-quality data needed for effective training. The clean and organized nature of structured data allows for more efficient training processes, resulting to more accurate and reliable models. Machine Learning models trained with structured data can easily recognize patterns and relationships, leading to a better performance. This is particularly important in applications like predictive analytics, where the accuracy of the model is a key factor [3].
In Practice
A real-life case study of a company that utilizes structured data for AI can be seen in the case of Mayo Clinic. They Implement structured medical data for healthcare AI applications. Mayo Clinic Platform Discover offers high quality data of 10 million patients of complex and rare diseases de-identified, and in one comprehensive location for AI innovators. It includes data from multiple clinical specialties, structured data such as lab tests, diagnoses, and medications, as well as unstructured data like clinical notes and pathology and radiology reports [4].
See Also
Training Data: Data used to teach the model patterns and relationships
Unstructured Data: Data lacking predefined organization or format
Validation Data: Data used for hyperparameter tuning
References
- Bickham, B. (2023). The Power of Structured Data in AI: A Comprehensive Guide.
- Needl. (2021). Structured Vs Unstructured Data: Role Of ML/AI In Deriving Insight.
- Newton, G. (2025). Importance of Structured Data in AI.
- Mayo clinic. (2022). Mayo Clinic Platform Discover: actionable insights from clinical data.