Structured Vs Unstructured Data: How They Differ

In the GenAI era, unstructured data is becoming increasingly important, with one IDC report estimating it to be 90% of all data generated today. This data holds vast untapped potential for extracting business insights. Managing both structured and unstructured data is essential for delivering comprehensive analysis, driving innovation, and fueling growth. Leveraging both these data types ensures a more robust and holistic approach to solving complex business problems and making informed decisions.

Structured data has a pre-defined model and is presented in a neat format that is easy to analyze. Unstructured data doesn’t have any pre-defined format. It is available in its raw form, requiring complex tools for management and analysis. However, this isn’t the only difference between structured and unstructured data. In fact, each data type has its unique characteristics, use cases, benefits, challenges, and significance.

Learning more about these two categories of data enables businesses to optimize data management, improve data strategy, and streamline other critical business operations.

Key Differences Between Structured and Unstructured Data

What Is Structured Data?

Data that is meticulously organized in a specific predefined format is called structured data. This type of data is often referred to as business data or quantitative data. Structured data can best be understood with the example of a spreadsheet. A document with rows, columns, and tables having predefined fields and labeling, such as customer name, address, credit information, patient data, financial transactions, etc.

Structured data requires preformatting or organization before it is stored in a relational database management system (RDBMS), which is why it is also called schema-on-write. Since the data is present in a simplified format, it is easier for users to search for specific datasets across the database, modify the data, or leverage it for relevant business needs. Structured query language (SQL), developed by IBM, is a computer language built specifically for working with structured data.

Structured Data Sources

This type of data can originate from a wide number of sources, such as enterprise resource planning (ERP) software, customer relationship management (CRM) tools, master data management (MDM) platforms, etc. Similarly, structured data can come from social media platforms and other online sources, such as online customer surveys, to name a few. In fact, structured data can further be extracted from unstructured data using specialized applications.

Examples of Structured Data

Structured data examples may include:

• Customer Database: It has customers’ information in tabular format, including but not limited to contact address, purchase history, demographic information, etc.
• Sales Data: Most of this data, such as sales volume and customer acquisition cost, comes from CRM.
• Ecommerce Data: This type of structured data includes customer information, product catalogs, purchase history, etc.
• Financial Records: This data includes information such as transaction logs, ledgers, balance sheets, etc.

Pros and Cons of Structured Data

Pros of Structured Data

There are a number of benefits businesses can gain from using structured data, such as:

• Ease of use: Structured data is relatively easier to use for both regular users and power users due to its organized and streamlined formatting.
• ML algorithm-friendly: This type of data benefits both business users and machine learning (ML) algorithms, as it can efficiently parse structured data.
• Tools accessibility: Data teams have a wide range of tools available to manage and analyze structured data, which makes it easier to work with it.

Cons of structured data

Lack of flexibility is one of the major challenges or cons of structured data.

• No flexibility: Structured data leverages a schema-on-write approach, and since it has a predefined structure, changing it for varying purposes can be a significant challenge.
• Data preparation difficulty: As mentioned above, structured data demands complex data transformations before it is ready to be stored in databases.
• Overhead cost: Structured data is stored in databases because they can handle large-scale storage and enable easy access to queries. However, running, maintaining, or operating a database requires excessive resources.

Use Cases For Structured Data

Structured data plays a significant role in data analytics, allowing businesses to extract critical insights. Let’s examine what other use cases structured data serves.

Web and Business Analytics

Structured data is pivotal in web and business analytics, providing marketing and business intelligence teams with the essential tools to analyze and interpret market trends, customer behaviors, and usage patterns. This analysis helps identify opportunities for growth and areas requiring enhancement, driving strategic business decisions.

Inventory Management

Structured data optimizes inventory management by organizing asset information in a way that enhances searchability and accessibility. Businesses can efficiently track asset movements, monitor stock levels, and predict inventory needs, reducing overstock and outages and ensuring operational continuity.

Health Data Management

In healthcare, structured data is utilized within Electronic Health Records (EHR) to manage and store patients' clinical histories and records systematically. This organization aids in improving patient care accuracy, streamlining workflows, and facilitating easier data access for healthcare providers.

Financial Forecasting and Risk Management

Financial institutions leverage structured data to perform robust financial forecasting and risk management. By analyzing historical data, market trends, and economic indicators, they can predict future market behaviors, assess investment risks, and optimize financial strategies, thus safeguarding and enhancing financial performance.

Customer Relationship Management (CRM)

CRM systems use structured data to maintain detailed records of customer interactions, purchases, and personal information. This data helps businesses enhance customer relationships through targeted marketing efforts, personalized services, and efficient communication, ultimately boosting customer satisfaction and loyalty.

What Is Unstructured Data?

Unstructured data is any data that doesn’t have any organized or pre-defined format. This type of data comes in a variety of formats, including but not limited to HTML, doc files, image files, audio and video files, source codes, email content, etc. Since the data isn’t available in a structured format, it is generally treated and stored as “objects”. These objects are usually stored in either NoSQL databases or data lakes. To make these objects searchable and accessible to teams, data teams label the objects with “tags” or other identifiers.

Unstructured Data Sources

The volume of unstructured data available in organizations globally is much larger than its counterpart. In fact, statistics reveal that up to 90% of an organization’s data is unstructured. The reason behind the massive volume of unstructured data is its diverse sources. This data may come from emails, interactive design applications, presentations, videos, application source codes, database files, word processing tools, medical devices, etc.

Examples of Unstructured Data

The following formats are among the many examples of unstructured data.

• Computer-Aided Designs: stl, iges, art, 3dxml, and psmodel.
• Mails: eml, msg, emlx, dbx, and wab.
• Crypto Keys And Certificates: crt, pem, pkipath, etc.
• Videos: mpeg, mpg, h263, h264, 3gp, wmv, etc.
• Spreadsheets: xls, xlsx, numbers, cal, and ots.
• Presentations: ppt, keynote, gslides, or ppz.
• Binary Files: gsf, hex, exe, or bpk.
• Source Codes: a2w, amw, androidproj, awd, axb, bufferedimage, or buildpath.
• Markup Texts: HTML, XHTML, and markdown.
• Desktop Publishing: PDF, pub, xfdf, and ave.
• Images: jpeg, png, bmp, tiff, etc.
• Audios: mp3, mp4a, wma, ram, aac, etc.
• Database Files: 4db, adt, box, kexic, contact, pdb, and more.

Pros and Cons of Unstructured Data

Pros of Unstructured Data

There are a number of benefits that unstructured data serves.

• Use case diversity: Unstructured data isn’t limited to any specific use case. In fact, its qualitative and diverse nature makes it a valuable resource for a wide range of use cases.
• Strategic decision-making: Marketing teams can evaluate customer sentiments through surveys, analyze marketing trends via online comments, or understand market demands through support tickets.
• Simple to store: Unstructured data is more prevalent in a business environment than structured data due to its convenience of being stored in its raw format.
• Enhance operational efficiency: Businesses can leverage this type of data to improve their operational excellence, reduce cost, and improve performance.
• Fuels GenAI applications: One of the current most significant benefits of unstructured data is its ability to drive GenAI initiatives.

Cons of Unstructured Data

There are a number of challenges and cons associated with unstructured data.

• Lack of visibility: Unstructured data is spread across numerous silos and varying formats. Hence, unifying such a high volume of disparate data can be challenging.
• Access governance: Traditional access control frameworks cannot address unstructured data access risks.
• Data quality issues: Unstructured data consists of duplicated, outdated, and often trivial data. This can significantly hinder data teams from making the most out of their data or GenAI initiatives.
• Lack of data lineage: Without clear insights into the source, movement, and transformation of unstructured data, it is challenging to find vulnerabilities and verify the authenticity and reliability of data across its lifecycle.
• Compliance risks: Unstructured data often contains sensitive information. Without proper privacy and compliance controls, sensitive data can lead to compliance risks.

Use Cases For Unstructured Data

Unstructured is typically seen as a source for qualitative data analysis, although this isn’t always the case. Let’s take a quick look at some of the productive ways unstructured data is used.

Training & Fine-Tuning LLMs

Generative AI, large language models, or multimodal systems are adept at leveraging unstructured data. These datasets enable GenAI models to create realistic content or hyper-realistic images, enhance machine learning, and even produce real-world simulations. These amazing capabilities can only be achieved through the profound richness and depth found in unstructured data. Another critical use case of unstructured data is the domain-specific knowledge it offers, enabling teams to improve the reliability and accuracy of AI applications.

Enterprise AI Search

In the realm of enterprise AI search, enhancing knowledge management involves deploying AI-driven systems that can intelligently index, search, and retrieve vast amounts of unstructured data from diverse corporate documents. These systems leverage natural language processing to understand and process human language queries, enabling employees to access precise information swiftly. This not only boosts productivity but also fosters innovation by making previously siloed knowledge readily available across the organization, enhancing decision-making and strategic planning.

Enabling Market Research

As mentioned earlier, unstructured data is considered chiefly qualitative data, as opposed to quantitative, structured data. The diversity of information, the varying sentiments, and the implicit relationship between datasets enable teams to gather insights valuable for marketing intelligence. By leveraging unstructured data for marketing research, businesses can better evaluate market trends, customer sentiments, or consumer behavior to drive their marketing strategies.

Improving Legal Processes

Legal documents, case histories, or contracts and agreements are all available as unstructured data. These types of information are necessary for court proceedings, legal procedures, and other legal decision-making purposes. When managed efficiently, this information can provide relevant insights that can help legal teams streamline their processes when it comes to improving legal research, agreement reviews, and compliance risks.

Patient Outcome Analysis

Leveraging unstructured data from patient records, doctor's notes, and medical transcripts to identify patterns and correlations between treatments and patient outcomes. This analysis can inform more effective drug development strategies, personalize treatment plans, and improve the understanding of drug efficacy and safety across different demographics.

When to Use Structured and Unstructured Data?

The choice between using structured or unstructured data depends on business objectives and specific use case requirements. For accurate quantitative reporting, such as calculating inventory costs or summarizing financial insights, structured data is ideal. It is organized, easily searchable, and ready for analytical tools.

Unstructured data is more suitable for qualitative analysis, such as detecting trends or assessing customer sentiment. Machine learning algorithms or generative AI applications can process social media posts, emails, videos, and images to deliver the desired outcomes.

In practice, businesses collect, store, manage, and use both data types. They leverage quantitative reporting and qualitative analysis to support their growth strategies and improve their bottom line.

Govern Unstructured Data with Securiti

Traditional governance tools aren’t built to handle the complexities required in governing unstructured data, such as inline discovery and classification, data lineage tracking, sanitization, etc. Securiti Data Command Graph, one of the core capabilities of our Data+AI Command Center, enables businesses to discover and catalog all important metadata and relationships between them, offering valuable contextual intelligence about your unstructured and structured data.

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