Structured and Unstructured Data: The Main Differences

What is structured data?

Structured data is a standardized format of quantitative datasets. Structured datasets, in turn, are formatted by predefined data management parameters. Such a format is usually set up in Structured Query Language (SQL), determining the format of fields and the general data model. 

Structured datasets that are based on the connection of stored items are called relational databases. The fields of structured datasets have strict formatting restrictions, which later aid in data searching and filtering.

What’s cool about structured data

1. Easier to analyze. The standardized predefined parameters of structured datasets makes the analysis easier to manipulate by machine learning. Needless to say, it’s applicable not for all cases; but easy readable content format enables robots to find logical connections and analyze the results. 
2. Easier to understand by average business users. Don’t raise your eyebrows yet – it’s applicable not for all cases. But in general, analyzing structured datasets doesn't require having deep insights about the connection between various types of data. Therefore structured datasets open up opportunities for a wider group of specialists to analyze the data.
3. Compatible with more tools. Structured data started to be collected way before the unstructured data caught analysts’ attention. Therefore there are more tools designed specifically for structured data analysis.

Things to consider about structured data models

1. Predefined file formats limit possibilities. Structure is an immense benefit when analyzing big datasets. Still, it also limits the number of use cases for which the data model could be used. 
2. Format issues. The format options of such datasets are limited. Structured datasets have strict formats, and schemes, leaving no space for freedom of storage choices. Besides, in case of changes in data model, all the datasets have to be restructured to meet the new requirements. That can raise some unpleasant issues, especially when talking about changing big data storage parameters.

Structured data examples and use cases

Are those heavy, structured Excel sheets familiar? They’re a great example of how human-generated structured datasets look like. Here’s a few examples on what they could include:

• Addresses;
• Phone numbers;
• Credit card info;
• Names;
• Dates;
• Stats results;
• Collected output of web and sales data;
• etc. 

Customer relationship management. Suppose you are willing to analyze your customers’ behavior patterns and triggers. You could level up your CRM by using analytical tools to create structured data models listing all the necessary parameters about your customers. These structured information lists in your CRM could include the lead source, call tracking data, contact information, dedicated support representative, the type of product purchased, subscription status of newsletters, etc. This structured data could aid you in building your ideal customer profile and identifying some repetitive characteristics.

Financial records management. Many companies operating in the finance industry deal with a plethora of information. Storing various records in structured databases could greatly ease the filtering and data management process. Financial data is well-structured, therefore there’re more chances for an average Joe to use such a database for the employees to analyze the collected information. Not for all the cases, of course, but still.

Inventory management. Inventory control requires structured databases because of the same reason as financial records. Such datasets should be organized neatly to empower more employees to work with the data.

What is unstructured data?

Unstructured data is any dataset in unique content formats with no predefined storage parameters. Such datasets contain informational units in their native formats, making the research more complex.  

Unstructured data gathering is like the new kid in the data industry – the process hasn’t matured yet, leaving a lot of space for development. However, such a data model provides businesses with more context without leaving data behind the parameters’ frame. Therefore companies tend to invest in unstructured data collection.

What makes unstructured data models lit

1. Limitless types of formats. Unstructured datasets are stowed in their natural shape until it’s framed in a more structured layout for analysis. Videos, audio, and images – everything falls under the unstructured data umbrella.
2. Variety of possible insights. Since the types of unstructured data forms aren’t limited, businesses can take a chance to analyze the data from different perspectives. Since no strict pre-definitions are required for data preparation, the possibilities of data gathering are limited only by your imagination. 
3. Data collection speed. Unstructured data models don't have a predefined format and are faster to collect in bulk. If you have enough capacity to analyze unstructured datasets, the collection speed could strengthen your analysis muscles.

Issues using unstructured data

1. More complex analysis process. Data gathering may be easier, but analyzing unstructured content formats can be challenging. It would likely require you to rely on analytics engineers and industry experts to gather something meaningful from the collected raw unstructured data. After this step, the structured formats can be passed on to data analysts for further investigation.
2. Specialized tools are needed. Finding the right tools for unstructured data analysis could be a tough nut to crack. Simply because you wouldn’t be able to use Excel for unstructured databases. To better analyze the collected assets, you would need specific analytical software, which usually requires more technical skills.

Unstructured data examples and use cases

Unstructured data, by its nature, includes many types of formats, like:

• Social media comments;
• Emails; 
• Media (photos, video, etc.);
• Customer support tickets;
• Documents in various formats.

The variety of content formats opens up the possibility for different use cases, like:

Track customers' activity in social media/forums. Reviewing your audience's positive and negative comments on social media sites can provide great insights for improvement opportunities or warn about possible issues. Simple structured data gathering, like the numbers of comments or likes, won't provide you with an understanding of the overall context. Analyzing context is key to gathering good insights from such findings. 

Level up chatbots. Chatbots aren't a new thing, but their development hasn't stopped since the first one showed up on the market. The most developed ones are AI chatbots that can keep up a human-like conversation flow using natural language processing (NLP). For example, such chatbots help businesses design more personalized shopping processes. Companies need to invest in NLP-based unstructured data research to make all of this great stuff happen.

Structured vs. unstructured data

Structured and unstructured data models aren't in opposition with each other – you don't need to pick your sides. You can use both for your analysis, just make sure to understand which type of data analysis would benefit your project more. 

Where unstructured data model meets structured

There are some cases when neither structured nor unstructured is the perfect word to describe the format and complexity of datasets. We call such data models semi-structured as they contain an unstructured format with metadata characteristics. 

Such databases could be easily analyzed by grouping and filtering the metadata. However, it still has a bit of messiness in terms of data formats, therefore it cannot be fully considered structured. A great example would be a list of Youtube comments with the publishing time information as metadata.

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