Data needs to be visually represented and presented to facilitate understanding. There are two types of data, which are structured and unstructured. Structured is a quantiative data while unstructured is qualitative, which is why structured is more easily understood by machine language. Relational database can easily input, search, and manipulate structured data using relational database management system. Programming language for managing structured data is called structured query language (SQL).
Structured data includes things like numbers, dates, and strings while unstructured includes audio, visuals (video/images), and emails. This means that structured can be organized while unstructured can’t, this is why unstructured results in more difficulty security and more storage requirements. Structured data also can be displayed in relational databases while unstructured data can’t. Financial transactions, medial information, CRM data, and dates/times are examples of structured data. Unstructured data is much more complex and unorganized, which is why it’s also referred to as the big data and machine learning is required to understand it better. Unstructured data includes social media data and multimedia and can’t be stored in relational database since strings have mixed datatypes.
Regular data structure will have columns/variables/fields/features on horizontal portion of a table with rows/records/instances on the vertical portion of a table. As you can see from the table below, (state, city, and median income is on the horizontal portion while actual records/instances are on the vertical portion). You can take columns as a way to break down data while rows includes actual data sets. Structured data are much easier to organize and understand and don’t need heavy machine learning but does come with more strict policies.
| State | City | Median Income |
| GA | Atlanta | 70000 |
| MA | Boston | 90000 |
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