Structured vs. Unstructured Data

The rise of the Internet of Things (IoT), e-commerce, the consumer Internet, social media, and big data have made an enormous amount of data available to businesses and organizations. Making sense of this information is a challenge that needs to be addressed if businesses want to stay relevant in the hyper-competitive world marketplace of the 21st Century. It demands tools and methods that go beyond traditional data and business analysis.

Data analytics is a critically important tool used by large and small businesses in virtually every market sector. When the technique is used effectively, it can give a company an edge through a better understanding of their customers, products, and other competitors. Ignoring the potential business intelligence hidden inside a company’s data resources can spell disaster for the organization.

The wealth of data generated and available for analytics comes in two main forms. Most electronically generated information can be classified as either structured or unstructured data, though some does straddle the line between the two. Let’s take a look at what distinguishes the different types of data and how they can be used to provide the insights that businesses need to survive and thrive.

Structured Data

Structured data is the type of data that has been used in most information technology processing systems since the advent of computers. It is the fuel that powers relational databases. Structured data is stored in fields of specific length containing designated data types such as integers or text characters. The rows and tables that contain structured data are easily searchable through SQL queries and automated tools.

Examples of structured data include the names, addresses, and credit card numbers gathered by e-commerce sites. When you take advantage of online registration for college courses or make airline reservations, you are creating structured data that will be used by whatever entity is running the website. The data is required to be entered in a specific format, and a good data collection page will force you to use the correct type. You should get an error message if entering your name into the credit card field. Give it a try next time you’re online.

The structured form of information makes it easy for database professionals to query it for various business reasons. Generating a report of all customers in alphabetical order who live in a specific city is a trivial feat with well-structured data. The ease at which this type of data can be handled is one of its most attractive aspects. It can be created by humans or machines as long as it conforms to the defined structure. 

Unstructured Data

As its name implies, unstructured data is, well, not structured. It essentially encompasses any information that cannot be formatted in a structured manner. Many recently introduced information streams are composed primarily of unstructured data generated by machines or humans. These include social media communication and mobile data which may contain a combination of freeform text and images. Sensor data collected from IoT devices may contain unstructured data as does the output of digital surveillance systems.

Unstructured data is not kept in a rational database and poses problems when analysis on it is attempted. New techniques are required to fully process the information and return useful results. It makes up a large percentage of enterprise data, so using it productively is important when considering the data resources at an enterprise’s disposal. Making the best use of unstructured data is one of the greatest challenges facing the proponents of data analytics.

Semi-Structured Data

This type of information possesses some of the qualities of both structured and unstructured data. Email is a prime example of semi-structured data. Its metadata provides structure but it is not searchable by traditional data mining techniques. Open standard JSON and XML markup language are other instances of semi-structured data. They pose many of the same difficulties to viable analytics as that of fully unstructured data.

Using Aqua Data Studio for Analytics on All Your Data Sources

Different databases are required to handle structured and unstructured data. Businesses that have both kinds of data have to manage the complexity involved in performing analytics against the backdrop of widely diverse data sources. Some data may live in SQL Server databases while other information that is just as important might be in a non-relational database such as NoSQL. This complicates the already challenging task of using their assets in the most effective way possible.

Aqua Data Studio offers database professionals a versatile tool for creating visualizations across many diverse data repositories. Using the same interface to interact with their SQL Server and MongoDB systems results in productivity gains for all team members. The tool enables you to develop custom dashboards and visual presentations that drive home the important points hidden in an enterprise’s data resources. It also provides a database team with a single platform from which to manage and access all of the systems encountered in a modern multi-platform environment. It is a valuable tool designed to get the most out of your valuable data in any form.

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