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Information keeps the world spinning as more people continue to spend their time online. By buzzing in the digital world, we keep generating more useful information, which can be collected and analyzed.
Different informational units and formats are spawned every second, so the data analysis becomes more rock’n’roll, making the collection process less determined. In fact, everything can be gathered and analyzed, from the strictly formatted spreadsheets to the trendy TikTok videos.
Such a variety of information can be categorized into two groups: structured and unstructured. Sit back – we are about to tell you the main differences between them.
Structured and Unstructured Data: The Main Differences
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.
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:
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.
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.
Unstructured data, by its nature, includes many types of formats, like:
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 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.
Analysis process is less complex.
More tools available in data processing.
Can be analyzed by a less data savvy audience.
Provides more freedom in terms of format.
Data gathering process is faster and simpler.
Variety of use cases.
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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As the title suggests, the biggest difference is in the format structure. Structured datasets have predefined parameters impacting the gathering process as well as the analysis. Unstructured data is less formal in terms of structure and can support various types of data. At the same time this feature makes unstructured datasets harder to analyze.
Data lake acts like a centralized archive for both structured and unstructured raw data hosting. Data warehouse is adapted specifically for structured data storage.
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