Data is either structured or unstructured. It is not monolithic.
And as businesses become more data-driven and are leveraging analytics and AI, the ability to harness these two distinct types of data together is essential.
“Structured data” is data that fits neatly into tables and includes discrete data types such as numbers, text strings, and dates. Common examples of structured data include sales forecasts, airline reservations, and CRM systems. Structured data is often stored in Excel files or relational databases (SQL Server, Oracle, MySQL, etc.)
On the other hand, “unstructured data” doesn’t fit neatly into a data table because of its size or nature. For example, audio recordings, video clips, chats, legal documents, and newspaper articles are largely freeform and unpredictable and do not fit neatly into tables. Nevertheless, analysis and searchability of unstructured data is critically important in order to detect keywords, customer sentiment, discussion topics, and many more usages. Unstructured data is often stored in a “NO-SQL” database like MongoDB, AWS DynamoDB, and Azure Cosmos DB.
Going a little deeper, here are some other ways you can distinguish structured from unstructured data.
Defined vs. Undefined Data
Structured data is clearly defined data in a structure, while unstructured data is usually stored in its native format. It lives in rows and columns and can be mapped into predefined fields.
Unlike structured data, which you can organize and access in relational databases, unstructured data does not have a predefined data model and is undefined.
Qualitative vs. Quantitative Data
Qualitative data is generally unstructured data. Examples include customer surveys, interviews, and social media interactions. It cannot be processed and analyzed using conventional tools and methods; more advanced analytics techniques like data mining and data stacking are necessary to unearth what the data is telling you.
Quantitative data is often structured data, meaning it usually consists of hard numbers or things that can be counted, like product information in a CRM.) Methods for analysis include regression (to predict relationships between variables), classification (to estimate probability), and clustering of data (based on different attributes). Data scientists and other data analysts can use these methods to generate business insights for your organization.
That’s it for now. Part 2 will discuss differences in formats, data storage, and ease of analytics.
