Structured vs. Unstructured Data

In this article, we review the two types of data and their different uses.

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Defining Structured vs. Unstructured Data Main Image Defining Structured vs. Unstructured Data Main Image Defining Structured vs. Unstructured Data Main Image

Defining Structured vs. Unstructured Data

There are two ways in which data is classified for the purposes of storage, analysis, and business decision-making: structured and unstructured. The difference between structured and unstructured depends on whether or not the information is organized for the purposes of data usage and analysis.

Structured data typically consists of clearly defined information (like hard text and numbers) that is easily searchable and maintained in or trackable via a highly organized table or database. Meanwhile, unstructured data comes in a variety of file or media formats and isn't intrinsically neatly grouped or classified.

But the differences between structured and unstructured data extend beyond how the information is collated. For the purposes of analysis, each requires a different set of technology tools and analytical methodologies deployed by data professionals with varied knowledge and skill sets.

Organizations tend to utilize structured data more than they do unstructured. About 43% of all data that organizations capture goes unutilized, representing enormous untapped value in regard to unstructured data. But both data types are valuable and can be exploited as long as organizations understand how they differ, and the capabilities required to make use of them.

What is Unstructured Data?

Unstructured data is information in its raw format; it often lives in or near the original location in which it was collected, or in data lakes — relatively undifferentiated pools of data. Because it represents all types of raw data that’s collected, even that which hasn’t been catalogued or analyzed, it represents massive quantities of potential value and thus requires robust data center and cloud architectures deploying very high-capacity data storage systems.

Thus, unstructured data is hard-drive intensive. The need to uncover greater value by retaining vast quantities of unstructured data in an economical way means there is higher-than-ever demand for mass-capacity storage systems centered around hard drives — which continue to provide significant TCO advantages, as advances in HDD technology continue to make ever-higher capacities possible. The need to access unstructured data near its source and to move it, as needed, to a variety of private and public cloud data centers to be used for different purposes, is also driving the shift from closed, proprietary, and siloed IT architectures to open, composable, hybrid architectures where data moves freely and efficiently across the distributed enterprise.

Unstructured information is also referred to as qualitative data, meaning that it simply information that is observed or recorded. Internet of Things (IoT) sensors in a factory, for instance, might collect data about the ongoing performance of equipment. The information is then sent to servers to be stored in an unstructured format, such as a PDF and video files.

Other examples of unstructured data include satellite photos, weather reports, patients’ biosignal data in a hospital, and digital camera imagery that have not yet been tagged or catalogued in an organized way. The common denominator is that data is passively gathered and transmitted without any pre-defined organizational formatting. While unstructured data has the opportunity to be extremely useful in spotting larger trends and constructing predictive models when it has been reviewed and understood as part of a massive dataset, it's difficult to readily search and analyze for the purposes of business analytics.

What is Structured Data?

Structured data is organized, quantitative data — most commonly numerical or text-based data — that exists in some kind of standard formatting in a fixed field within a file or record. Information that exists in spreadsheets or relational databases are common examples of structured data. This organization makes it simple to query the data when looking for specific pieces of data or groups of information.

For example, agricultural sensors on a farm might collect raw weather data to determine when crops should be watered and how much water they need. In order for the data to be structured, it needs to be categorized and formatted. This type of data in a structured format might look like a table with columns entitled “time of day," “temperature," and “humidity." The structure facilitates searching, sorting, and analyzing.

Structured vs. Unstructured Data

The main difference between structured and unstructured data is the formatting. Unstructured data is stored in its native formats, such as a PDF, video, or sensor output. Structured data is presented strictly in a predefined form or with predefined signifiers that describe it, in a standardized format so that it can be easily placed into a table, spreadsheet, or relational database.

Unstructured data is often housed in what's called a data lake, which is essentially a repository that stores raw data in various formats. Structured data resides in data warehouses, repositories that only accept data formatted to pre-defined specifications. A data lake is like a reservoir that stores unstructured data and may also store structured data, while a data warehouse houses only organized and formatted structured data.

Whether data is in a lake or a warehouse, the information is stored in some form of a database. The main difference is that structured data is stored in a relational database, stored in rows and columns using organized formats like Structured Query Language (SQL), PostgreSQL, or MongoDB. These formats make structured data far easier for users — or machines — to search, sort, and work with. Unstructured data, by contrast, is stored in a non-relational database such as NoSQL.

The two types of data also differ in how they may be analyzed, as well as the tools and personnel needed for working with and manipulating them. Unstructured data is typically analyzed by using techniques such as data stacking and data mining, which have been developed to work with metadata and come to more general conclusions. When it comes to structured data, more mathematical forms of analysis — such as data classification, clustering, and regression analysis — can be used. In terms of tools and technologies, structured data facilitates the use of management and analytics tools. Examples of tools used to work with structured data are:

  • Relational Database Management Systems (RDBMS)
  • Customer Relationship Management (CRM)
  • Online Analytical Processing (OLAP)
  • Online Transactional Processing (OLTP)

Software that can work with large datasets existing in multiple formats are typically used for managing and analyzing unstructured data. Examples of tools for managing unstructured data include:

  • NoSQL Database Management Systems (DBMS)
  • AI-Driven Data Analysis Tools
  • Data Visualization Tools

Unstructured data often requires management by a well-trained expert, and software tools that have more advanced AI and predictive modeling capabilities, than those used for structured data. Machine learning is one of the strategies used for the analysis of unstructured data.

Because structured data is already sorted and organized, the software tools used to work with these datasets are more accessible for non-expert business users. For example, inputs, searches, queries, and manipulation of data are often done in a self-service fashion via a highly organized user interface.

Use Cases

One illustration of how unstructured data can be employed is in the way sensor data from IoT devices may be used for predictive modeling. Sensors on a farm, for example, are constantly collecting and disseminating data about the climate, health of crops, and functionality of agricultural equipment. AI tools can then analyze the data and build predictive models for better management and decision-making. AI with machine learning capabilities can learn from these patterns over time, producing more accurate models with each subsequent analysis.

Unstructured data in the form of weather and crop growth patterns can be analyzed to predict how much water or nutrients the automated machinery should deliver in the future. Then, the AI software conducts an automated analysis and constructs a predictive model to inform better farm management going forward. This analysis is based on patterns the AI recognizes emerging as it sifts through unstructured data in multiple formats, like crop growth and soil nutrient patterns collected from sensors.

Structured data is used in scenarios that involve quantitative analysis. Logistics and inventory management are areas in which structured data is useful in improving efficiency and decision-making. Warehouse inventory is typically housed in the form of structured data with columns and rows in a relational database. This data can then interface with inventory management or business analytics systems to inform both business and data science users. Users, and their software tools, can place hard values on metrics like the profitability of certain product lines and the overhead associated with procurement and shipping. Companies can then make decisions based on quantifiable outputs.

Today, the two types of data have different uses. Unstructured data is the raw output of devices or software that collect information which is moved into data lakes in its original format. Structured data is organized in numerical or text format, and can be catalogued, organized, reorganized and analyzed within pre-defined parameters. As AI and ML continue to advance, new capabilities to mine, analyze, learn from and make immediate use of unstructured data are likely to emerge.