Unstructured vs. Structured Data: Understanding the Differences

Unstructured vs. Structured Data

The data spectrum is constantly evolving, encompassing various data types. New data types are emerging every day. However, structured and unstructured data represent two distinct areas of such a spectrum. Data volume and complexity are rising exponentially, with more users and businesses embarking on digital platforms. 

Online businesses rely on data volumes to facilitate their operations. Informed decision-making through automation can be processed by reading input data. But It’s getting challenging for online firms to handle, store, and manage large data volumes, either structured or unstructured. 

Not only is this process costly, but it requires technical expertise If no proper tools are employed. To effectively manage data, ShareArchiver advances its affordable service of data storage management and data security software for better administration. 

The way or format in which data is available also makes a huge difference in how fast it can be stored or processed. Within structured and unstructured data, there are distinct differences and we have created an in-depth article about them below. 

Structured Data

A form of highly formatted data that has been pre-defined according to a pre-established data model is known as structured data. It has a set structure before it is placed in a data storage, known as schema-on-write. This is also considered quantitative data used by businesses to forecast business trends. 

Precisely defined fields are set in this, and business algorithms can easily do it. Spreadsheets, tables, rows, and columns are the format this data is available in. Each field within this relates to a specific category of data or value. 

Data analysis is easy in this type of data, and it can be easily stored, searched, processed, and analyzed. Dates, addresses, customer information in CRM systems, credit card numbers, stock information, financial records, and inventory databases are all examples of structured data. 

With ShareArchive’s E-discovery Tools, structured data is easily accessed even if it is in large data volumes Its full-text search allows easily locating data. With the help of a relational database management system (RDBMS), database administrators and users may easily add, search, and modify structured data. This is structured data’s best quality.

Pros 

  • A predefined structure enables easy storage of structured data, which reduces storage and archival costs as well.
  • There are no requirements to be an expert in ML or AI to process structured data. Basic data science knowledge can help work with data management tools for structured data. 
  • These are available in pre-established data sets, which results in accurate data processing results with no errors. 
  • There is consistency in data interpretation of structured data. 
  • This data is compatible with conventional data analysis tools, which is ideal for processing it for insights.
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Cons

  • The scalability of this data is hard, as it is predefined into data sets and is not adaptable to fit into some data requirements. 
  • No context or details are available in structured data, which makes it harder to draw deep insights and conclusions at times. 
  • As it is schema-dependent, minor changes require altering the whole structure, making the process time-consuming. It can be a big challenge during data migration. 

Unstructured Data

Unstructured Data

Unstructured data is qualitative and has no predefined format or schema. This is before being placed in a data storage, referred to as schema-on-read. There is no inherent structure of this data and no record keys to identify it. Due to its lack of organization, it can be not easy to process and makes up 80-90% of the entire data available. 

This is also challenging to analyze and is available in the form of different formats. These include texts, images, audio and video recordings, sensor data, emails, social media posts, HTML content, etc. This type of data arises from a variety of sources and is present in a freeform. 

Unstructured data can sometimes be a part of structured data, as this is in the form of individual data files. This is in abundance, and online organizations find it hard to store for which purpose ShareArchiver offers Unstructured Data Management to help businesses excel. Unstructured data management categorizes and organizes it to integrate and archive in one unified space. 

Now, let’s look at some advantages and disadvantages of unstructured data. 

Pros 

  • Unstructured data is adaptable, allowing a specific amount of data to be analyzed. 
  • Diverse and valuable information is present in unstructured data, which is beneficial for businesses. 
  • Data accumulation rates for this type of data are high. Its data can be easily collected. 
  • Real-time information is available through this data type, which helps gather insights about customer opinions and preferences. 

Cons

  • Advanced tools are required to define and categorize this data type, making it highly convenient for SMEs. 
  • Every file has to be individually categorized, labeled, and archived, enhancing storage costs. 
  • The difference in formats is a big disadvantage of unstructured data, therefore data integration can be a demanding ordeal in this. 
  • Due to being uncategorized, there can be inconsistencies in data processing of unstructured data.
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Examples and Use Cases

To better comprehend the different use cases and examples of both structured and unstructured data, we have individually mentioned them here. 

Structured Data Examples

Structured Data Examples

Financial Databases

It is possible to monitor financial activities, account balances, and investment portfolios via the use of structured data and is a great example. The data structure in financial transactions makes it easy to detect fraud, which is a big advantage.

Targeted Marketing Campaigns

Segment customers, A/B testing of content sent to a customer group, and customizing campaigns through pre-categorized data is a usage case that is widely employed. 

Product Catalogs

Detailed product information, including names, descriptions, pricing, and specifications, may be found through structured data. This aids business intelligence. 

Software for Managing Inventory

Maintaining an accurate record of stock movements, inventory levels, and reorder points through structure data is another usage example. Supply chain optimization is the potential benefit of this. 

Scientific Records and Research

Documentation of this includes the recording of experimental evaluations, observations, and results from studies through structured data. To advance scientific studies, it is easy to analyze experimental data If it is in a structured form, which is a great use. 

Unstructured Data Examples

Social Media Analysis

Unstructured data contains user opinions, sentiment, and social trends. Market perceptions through gained insights is a great usage of this type of data. 

Text Content Analysis

Emails, reports, contracts, and other written content are available for textual data analysis through unstructured data. It helps categorize feedback from customers and enhances product quality and services by online businesses. 

Image Recognition

Product photos, medical scans, and satellite imagery are examples of unstructured data. Image recognition is a usage case for this data to identify visual content. This aids in strengthening security and helps moderate content. 

A combination of unstructured and structured data also provides potential advantages, such as boosting customer recommendations and identifying fraud. 

Data Management Challenges: Unstructured vs. Structured Data

Along with offering valuable insights,  there are a few obstacles these two types of data face, which are enhanced below. 

Data Silos

Siloed information is the result of data stored within structured data. It does not let integration be streamlined with other data sets, which can be a big task to handle. 

Data Compliance

Due to the sensitivity of data being archived, it requires compliance with privacy regulations for regular audits. Premium security measures and tools are required to manage structured data. The Data Compliance Software certifies businesses’ data retention efficiency with data security through SSL technology.

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Data Governance 

For unstructured data establishing clear data ownership is not easy. There is complexity in managing access controls along with data policies, which are strict for this sort of data. 

Data Organizing Tools for Structured and Unstructured Data

To eliminate the challenges of managing structured and unstructured data, there are a few tools and techniques that must be employed with careful strategizing. These are mentioned as follows. 

Tools for Structured Data 

Relational Databases

Well-established technologies like MySQL, Oracle, and Microsoft SQL Server with strong relational model support can effectively manage structured data. 

Spreadsheet Tools

For this, Microsoft Excel and Google Sheets are tools that are ideal for small data sets and offer an easy accessibility and engagement approach. 

Data Catalogs 

A great technique applied across different systems is the use of  Alation and  Share Archiver for easy data discovery and easy data management and organizing. 

Tools for Unstructured Data 

Document Management Systems (DMS)

For unstructured data, ShareArchiver, DocuWare, and M-Files are effectively used for secure storage adoption. This also provides different control versions for the data sets. ShareArchiver’s data archiving software allows storing data in different tiers for user-focused accessibility and stores data according to preference and usage frequency. This effectively manages storage costs as well. 

Digital Asset Management (DAM)

Multimedia content is a big part of unstructured data, and organizing it requires Adobe Experience Manager and ShareArchiver, which also provides metadata tagging. 

Machine Learning Tools

To extract insights from unstructured data, tools like TensorFlow and PyTorch are employed, which facilitate data analysis. Automation of these tasks is also assisted through these. 

Conclusion

Data has taken over the world with the expansion of the virtual world. With more customers joining the digital marketplace, data is rising, which is then modified to a structured approach or stays in an unstructured form. Both of these have their differences. Unstructured data is diverse and easily quantifiable, while structured data is clear and organized but can be hard to scale. 

Both can be managed with the help of advanced tools to create easy access and manage them to retain required information. This can enhance the business’s marketing practices and boost growth. We hope this article helped you understand the differences and management of unstructured and structured data effectively.