Unstructured VS Structured Data
Today data is everywhere – and data is growing. In fact, Gartner analysts assess that about 80% of all enterprise data is unstructured data. Considering most enterprises manage about 347 TB of data, that’s roughly on average 277 TB of just unstructured data per enterprise. And don’t forget there’s also semi-structured data to consider in the equation. Additionally, in the near future, these numbers will only increase; it’s estimated that enterprises will accumulate more data at a 42% AGR by 2022. With so much data and only more coming, it’s difficult to maneuver the nuances between unstructured vs structured data, and how to handle each type.
Upgrade your data management skills by learning the differences between unstructured vs structured data (including semi-structured data), and four key management differences between them.
Unstructured VS Structured Data: Definitions
What’s the Main Difference Between Structured and Unstructured Data?
In short, structured data has a formal structure in place and is therefore easy to search for due to its patterns. Whereas unstructured data is, simply put, not; unstructured data has no pre-defined data model and is generally unorganized.
What is Structured Data?
In other words, structured data is likely the type of data most are used to encountering on a regular basis. As mentioned before, structured data is highly-organized and requires a pre-determined data model, allowing machine language to understand them well. Additionally, structured data is often classified as quantitative data, and is typically created by systems.
Examples of Structured Data or Content:
While there are many types of structured data or content, some common examples include:
- Numbers (Phone, Credit Card, Zip Codes, etc.)
- Most CRM Data
What Is Unstructured Data?
Sometimes called unstructured information and classified as qualitative data, most simply, unstructured data is everything that structured data is not. Unstructured data is data that has no pre-defined data model or pattern and is, therefore, unorganized and not easily searchable. To add, unstructured data is most often created by people, rather than by systems.
Examples of Unstructured Data or Content:
Keep in mind, these are only several sources of unstructured data and content of all of the possible examples – Otherwise, the list would be quite long!
- Text files
- PowerPoint presentations
- Social Media Data
- Log Data
- Mobile Activity
What Is Unstructured Data Used For?
There’s power in numbers, and since unstructured data makes up the vast majority of enterprise data – it’s important! Organizations that sort and analyze their unstructured data can leverage it to make better business decisions and sharpen their competitive edge. And organizations that don’t utilize their unstructured data at all are missing out on potential opportunities for success.
While the definition of semi-structured data can be blurry, it is categorized as a form of structured data that does not follow a pattern or pre-defined data model (typical for unstructured data), but still contains some tags to sort fields within that data (metadata).
Examples of Semi-Structured Data or Content:
Unstructured VS Structured Data: 4 Key Management Differences
Now that you can identify structured data and unstructured data in your content landscape, learn about these four key management differences so you know how to apply them when the time inevitably arrives.
When it comes to structured data vs unstructured data, analysis is likely the most important difference. Because machines can easily search for structured data, it is, as a result, easy for those machines to analyze that data. On the other hand, unstructured data requires additional processing, since it is inherently difficult to find, even by machines.
Today’s global corporations have minimal insight into and control over one of their most valuable assets – their enterprise content. Content is everywhere: dispersed across cloud services, networks, local and remote offices, ECM platforms, and within business systems and applications.
Understanding the full scale of the content, its location, its value, and the business risk is an immense, difficult, and growing challenge, leaving organizations vulnerable to significant risk or lost opportunity.
Unstructured Data Analysis
If you can’t easily organize it, then how do organizations analyze unstructured data? The search-difficulty of unstructured data naturally makes its content analysis challenging. While there are tools available, unstructured data is still an unsolved problem still looking for its best solution. Developers are currently still working on unstructured data analytics tools and creating best practices for their management and governance.
DryvIQ empowers organizations to identify, organize, analyze, and safeguard their enterprise content by providing a holistic platform that enables the automated remediation and orchestration of content across information silos to support regulatory compliance, enhance business productivity, and mitigate corporate risk.
Intrinsically, structured data is much easier to manage than unstructured data due to its organized nature.
Validating the accuracy of sensitivity labels while also identifying unlabeled content is a manually intensive process. For various reasons, users may not apply the correct sensitivity label to content. Even automated solutions need to be “re-checked” to ensure labeling accuracy. Dated content or backfile data may also be missing required sensitivity labels.
How Do You Manage Unstructured Data?
The most common way to manage unstructured data is by way of an ECM (enterprise content management) system. This way, unstructured data is available in a centralized location and organizations can store them in the same storage space as their structured content.
The Dryv platform enables organizations to validate the accuracy of file sensitivity labels while also identifying unlabeled content—and applying accurate labels.
Unstructured Data Storage
Since most data is unstructured, enterprises will therefore require more storage space for unstructured data than structured data. Additionally, because there is usually more unstructured data within a file than just its organized structured data (address, date, number, etc.) unstructured data also requires more storage and processing. As a result, it can sometimes be challenging to find a strong unstructured data storage solution.
Best Storage For Unstructured Data
Structured data is usually stored in data warehouses, while unstructured data is most typically stored in data lakes. As for where to actually store all of that unstructured data, there are a variety of options. It can be stored in cloud storage, non-relational databases, cloud data lakes, and data warehouses. NoSQL approached databases have proven useful for storing unstructured data, as they do not rely on structures and leverage more flexible data models.
With unstructured data growing at a rate of more than 50% per year, safeguarding that content can feel impossible. Relying on users and manual processes to ensure your content is properly managed is an impossible task, rife with opportunities for mistakes. This lack of control opens organizations up to large fines, loss of sensitive data or intellectual property, operational inefficiency, or a negative impact on market and brand value.
You have all of your unstructured data and content landscape finally under control. But now, it’s time to move it. But because it is difficult for machines to read, unstructured data is also difficult to migrate. Whereas – you guessed it – structured data is more straightforward to migrate.
Unintentionally, your organization could migrate sensitive and risky content to the new system—and features designed to enhance collaboration and productivity may increase vulnerability and risk of data loss. Migration without knowing what content you’re moving can be risky.
Migrating Unstructured Data
There are a number of migration tools available that can help minimize some of the many issues unstructured data migrations create. By doing a content analysis before migration, organizations can prioritize which content needs to be migrated first, last, or not at all.
However, some organizations may need most or all of their unstructured data, depending on their business needs. Companies needing to preserve their content fidelity should look for a compatible migration tool. (Like this global travel agency when they achieved a 99.999% successful file migration)
An upgrade to your enterprise storage provides a real opportunity to fully understand your content landscape. The Dryv platform will proactively discover and classify sensitive content before your migration enabling informed decision-making on content location and permissions—thus ultimately reducing corporate risk and exposure.
By leveraging pre-migration investments in entities and policies, organizations can continually scan and safeguard their content in new environments.
Structured or unstructured, organizations that manage their data most effectively will have the edge over those that neglect to. While both types provide business value, organizations must stay mindful of their differences if they want that value to be of any practical use.