The volume of data created and stored around the world each year continues to explode. One estimate predicts total global data volume to reach 161 zettabytes (that’s 161 trillion gigabytes!) by 2025. Businesses today innovate and grow using the data they have at their disposal.
Technological advancements in the form of distributed processing and neural networks now make it possible to analyze unstructured data, which differs from the type of data usually found in standard databases. Multiple estimates put the percentage of data that is unstructured somewhere between 80 and 90% of all data. This article clarifies the differences between structured and unstructured data and discusses some particular privacy and security concerns of unstructured data.
Structured data is information that is organized in a consistent way, which makes it trivial to query, search, manipulate, and analyze. Typically, businesses store this type of data in a database comprised of tables with rows and columns.
An example of structured data is a table containing the home address, credit card number, and product ID of each customer placing an order with an online business. Other examples are found in reservation systems, CRM software, and inventory management systems.
Some of the following characteristics further define structured data:
While the percentage of overall business data that is structured continues to decline, this type of data remains vital for helping to guide business decisions.
Unstructured data is information that doesn’t fit into a predefined data model or have any easily identifiable structure. The lack of structure or data model makes it difficult to query, analyze, and search through this information using the conventional tools that work so well for structured data. Much of the data that businesses generate and collect today is unstructured; some examples include PDF files, images, emails, and audio from sales calls.
Here are some additional characteristics of unstructured data:
Unstructured data often contains a treasure trove of intelligence that businesses can uncover with sophisticated machine learning algorithms and the power of Big Data distributed computing. Use-cases include predictive analytics, improved customer understanding, and driving new marketing initiatives.
To fully understand the differences between structured and unstructured data, it’s helpful to compare the types under the following five headings:
Businesses store their structured data in relational database systems, such as Oracle, MySQL, and PostgreSQL. When an organization has large amounts of structured data from multiple sources, data warehouses typically serve as centralized repositories for all this information. Data flows into data warehouse servers from multiple relational databases.
Unstructured data does not live in a database system, so businesses store it in its native raw format (e.g. text file, image file, video file). Since most organizations have enormous volumes of unstructured data, they often store all of it in a large repository known as a data lake.
Due to its predefined organization (or schema), structured data lacks flexibility. You can only use structured data for its intended purpose. Data warehouses, which serve as central repositories for many sources of structured data, are also inflexible. Simple data model changes to meet evolving business requirements cost a lot of time and resources in a standard data warehouse. Unstructured data is not constrained by any schema so it doesn’t need to be configured or stored in a specific way or format.
Manipulating data includes performing actions that make the information easier to read and more organized or transforming it. These actions include erasing, merging, or sorting data. Structured data stored in relational databases has properties, such as consistency and durability, that make it much easier to manipulate than unstructured data.
Structured data has a pre-defined data model that describes how the data elements are represented and interrelated. For structured data, the data model is relational, which means each table contains a finite set of attributes for each row. Unstructured data doesn’t have a pre-defined data model, but it may well have an intrinsic structure that can be uncovered by advanced analytics.
Structured data has robust security and access restriction features. Administrative controls in database systems help to restrict who can access particular tables of information and what people can do with their access levels.
Unstructured data has less robust levels of protection because it may be generated and found anywhere within your organization. Without the ability to easily identify and classify unstructured data, sensitive information is often more at risk than structured data sources.
A large portion of the explosive growth in data comes from drastically increased volumes of unstructured data. Digital transformation initiatives continually increase the sources of unstructured data available to businesses. These sources include IoT sensors, web pages, reports, memos, social media, and team collaboration tools.
For many years, analyzing and extracting insights from unstructured data was challenging, but machine learning advancements helped to mitigate this challenge. The key advancement came from deep learning algorithms that are able to uncover data features, patterns, and insights from unstructured data. Unstructured data will continue to grow as machine learning tools enhance analytics capabilities for businesses.
In this structured data landscape, secure information management becomes more complex. The use of cloud computing infrastructure to store much of this data further complicates data management. Businesses need the right tools to maintain visibility over their data, identify their sensitive data assets, and ensure compliance with relevant regulations governing these assets. The potential regulatory and reputational impacts of mismanaged sensitive data make effective data security posture management a pressing concern for every business.
Regardless of whether data is structured or unstructured, many businesses struggle to maintain sufficient data visibility in today’s complex IT ecosystems. If you can’t identify all your data residing in on-premise and cloud systems and know what data is sensitive, you can’t expect to maintain compliance.
Polar is an agentless data security posture management solution that identifies all data stores, classifies what data assets are sensitive, and maps data flows to prevent leaks or compliance violations. You can also enforce automated data security and compliance controls. Book a demo to see the platform in action.