Understanding Structured, Semi-Structured, and Unstructured Data

What is Structured Data?

Structured data is information that has been formatted and transformed into a well-defined data model. The raw data is mapped into predesigned fields that can then be extracted and read through SQL easily. SQL relational databases, consisting of tables with rows and columns, are the perfect example of structured data.

The relational model of this data format utilizes memory since it minimizes data redundancy. However, this also means that structured data is more inter-dependent and less flexible.

Examples of Structured Data

This type of data is generated by both humans and machines. There are numerous examples of structured data from machines, such as POS data like quantity, barcodes, and weblog statistics. Similarly, anyone who works on data would have used spreadsheets once in their lifetime, which is a classic case of structured data generated by humans. Due to the organization of structured data, it is easier to analyze than both semi-structured and unstructured data.

What is Semi-Structured Data?

You may not always find your data sets to be structured or unstructured. Semi-structured data or partially structured data is another category between structured and unstructured data. Semi-structured data is a type of data that has some consistent and definite characteristics.

It does not confine into a rigid structure such as that needed for relational databases. Businesses use organizational properties like metadata or semantics tags with semi-structured data to make it more manageable. However, it still contains some variability and inconsistency.

Examples of Semi-Structured Data

An example of data in a semi-structured format is delimited files. It contains elements that can break down the data into separate hierarchies. Similarly, in digital photographs, the image does not have a pre-defined structure itself but has certain structural attributes making them semi-structured.

For instance, if you take a photo from a smartphone, it would have some structured attributes like geotag, device ID, and DateTime stamp. After you save them, you can assign tags to images such as ‘pet’ or ‘dog’ to provide a structure.

On some occasions, unstructured data is classified as semi-structured data because it has one or more classifying attributes.

What is Unstructured Data?

Unstructured data exists in its raw, native format without predefined organization. According to Gartner, this represents 80–90% of all new enterprise data and is growing three times faster than structured data.

This data is challenging to process with traditional tools, but contains rich contextual insights that structured data cannot capture: customer sentiment, visual patterns, conversational nuance, and emerging trends.

Unstructured data includes social media posts, chats, satellite imagery, IoT sensor data, emails, and presentations. Unstructured data management takes this data to organize it in a logical, predefined manner in data storage. Natural language processing (NLP) tools help understand unstructured data that exists in a written format.

In contrast, the meaning of structured data is data that follows predefined data models and is easy to analyze. Structured data examples would include alphabetically arranged names of customers and properly organized credit card numbers.

Examples of Unstructured Data

Unstructured data can be anything that’s not in a specific format. This can be a paragraph from a book with relevant information or a web page. An example of unstructured data could also be Log files that are not easy to separate. Social media comments and posts are also unstructured.

Here is an example of unstructured data from a log file:

38,P-R-38636-6-45,P-R-39105-1-11,P-R-38036-1-5,P-R-35697-1-13,P-R-35087-1-27,P-R-34341-1-9,P-R-33341-1-15,P-R-33110-1-29,P-R-31345-1-693,P-R-29076-1-6,P-R-28767-1-8,P-R-28540-2-8,P-R-28312-1-10,P-R-28069-1-27,P-R-28032-1-9,P-R-26562-1-12,P-R-26527-5-20,P-R-26164-1-11,P-R-25785-1-30,P-R-25095-9-70,P-R-23504-1-15,P-R-19719-5-41203

Wed Sep 23 2020 05:21:01 GMT+0500

Unstructured data is qualitative, not quantitative, so it is mostly categorical and characteristic in nature.

Why This Matters for Business

Unstructured data reveals insights impossible to capture in structured formats. Social media sentiment predicts market trends before they appear in sales data. Support ticket patterns identify product issues before they escalate. Customer call recordings capture objections that surveys miss.

Organizations with data lakes report:

• 41% gains in competitive advantage
• 37% cost reduction
• 35% improved customer experiences
• 33% better response to opportunities and threats

The challenge? More than 95% of companies acknowledge that managing unstructured data is difficult, and many spend over 30% of their IT budget on storage and management.

Data from social media or websites can help predict future buying trends or determine the effectiveness of a marketing campaign. Another unstructured data analytics example is detecting patterns in scam emails and chat, which can be useful for enterprises in monitoring policy compliance. Businesses extract and store unstructured data in data warehouses (also called data lakes) for analysis.

The Difference Between Structured, Semi-Structured, And Unstructured Data

Consider three types of job interviews: unstructured, semi-structured, and structured.

In an unstructured format interview, the questions asked are completely the interviewer’s choice. He can decide the questions he wants to ask and the order in which he will ask them. Popular examples of unstructured questions include “Tell me about yourself” and “Describe your ideal role.”

Another type is a structured interview. In this case, the interviewer will strictly follow a script created by the HR department and will use the same script for all applicants. Likewise, structured vs. unstructured data follows an organized format with a less flexible schema.

The third type is semi-structured data. In a semi-structured interview, the interviewer will combine the elements of both unstructured and structured interviews. It would include the quantitative and consistency elements, similar to a structured interview.

However, at the same time, like semi-structured data, structured interviews will have the flexibility of customizing questions according to the situation. To reiterate, the main difference between unstructured and semi-structured data is that unstructured data follows no pre-defined format, while semi-structured data is only partly unstructured.

The following points highlight the differences between structured data vs. unstructured data vs. semi-structured data:

• Organization: Structured data is well organized. Therefore, it has the highest level of organization. Semi-structured data is partially organized; hence the level of organizing is lesser than structured data but higher than that of unstructured data. Lastly, the latter category is not organized at all.
• Flexibility and Scalability: Structured data is relational database or schema dependent, therefore less flexible and difficult to scale, while semi-structured data is more flexible and simpler to scale than structured data. However, unstructured data doesn’t have a schema that makes it the most flexible and scalable out of the other two.
• Versioning: Since structured data is based on a relational database, versioning is performed over tuples, rows, and tables. On the other hand, in semi-structured data, tuples or graphs are possible as only a partial database is supported. Lastly, in unstructured data, versioning is likely as a whole data as there’s no database support.

Historically, businesses have only focused on extracting and analyzing information from structured data. However, with the growth of semi-structured and unstructured data, businesses now need to look for a solution that can help them analyze all three types of data.