Data is everywhere and in everything businesses do. Old project files, compliance reports, historical transactions, backups of backups, you name it, there’s data. Whether you’re running a startup or managing decades-old IT services, you probably have loads of data already, regardless of the industry you’re in.
Now, storing all of that in your production systems isn’t just expensive, it’s inefficient. It slows things down, increases risk, and in some cases, even gets in the way of compliance.
Which is why businesses side towards digital archiving. Each type of data requires a different digital archiving solution. The format, usage patterns, retention needs, and regulations all shape how you store and manage your archives.
Let’s take a closer look at the different types of data archives
- Structured Data Archiving
Structured data is neat! Like the rows and columns in a database, SQL Server, Oracle, PostgreSQL, or even older systems like AS/400. This type of data adheres to strict rules and schemas. It’s where you’ll typically find financial records, inventory systems, employee databases, and other data sets with clear relationships and dependencies. The important part here is to logically maintain it: table relationships, constraints, and dependencies. If these aren’t maintained, you’re not really archiving; you’re just dumping data into a digital attic where it’s no longer usable. Effective structured digital data archiving involves careful planning to retain indexability, ensuring that even if the data is years old, it can still be searched and trusted.
- Unstructured Data Archiving
Unstructured data, on the other hand, doesn’t live in defined rows or columns, and it includes formats like documents, PDFs, spreadsheets, audio files, video recordings, emails, and scanned images. These don’t follow any rigid structure, and without proper organization, this type of data quickly becomes digital junk. Without metadata or some kind of context, searching through unstructured archives becomes time-consuming and ineffective. The right approach to unstructured data archiving involves using systems like document management software or object storage platforms. Compression and deduplication help reduce storage size, and encryption ensures compliance, especially when dealing with sensitive information. Without a system in place, unstructured archives lose their value quickly, and what could be useful data turns into a file pile.
- Semi-Structured Data Archiving
Semi-structured data falls somewhere in the middle. It has some form and consistency but lacks the strict table-based structure of relational databases. Common examples include XML, JSON, CSV files, and EDI records. These formats are often used in APIs, web applications, and integration platforms. While semi-structured data can be both human- and machine-readable, archiving it effectively requires an understanding of both the data and the schema behind it. Schema-aware archiving tools can help preserve the structure, while semantic tagging improves search accuracy later on. Since these files often carry configurations, instructions, or data exchange logs between systems, losing their structure can render them meaningless.
- Hybrid Data Archiving
Hybrid data archiving is a combo of all three. A single record may combine structured, unstructured, and semi-structured elements. For example, a customer support ticket might consist of a database entry, a PDF document, a chat transcript, and metadata in JSON format. Archiving each component separately can strip away context and make the information harder to understand or use in the future. The challenge lies in archiving all these components as a cohesive unit. This requires tools and systems that can manage multiple data types while keeping the relationships between them intact. In many cases, different retention rules must also be applied to different parts of the same record based on business or legal requirements.
- Live Data Archival
In many industries, data that is no longer used daily is still relevant to business operations. That’s where the live data archival method involves moving older but still-accessible data out of expensive “hot” storage and into more economical “warm” storage tiers. It’s a common practice in sectors like retail, logistics, and manufacturing, where historical data is often referenced to make current decisions. The key to successful live archiving is maintaining quick access while cutting storage costs. Data automation helps by shifting data based on usage patterns, allowing businesses to strike the right balance between performance and efficiency.
- Legacy Data Archiving
Legacy data refers to information stored in outdated systems that may no longer be supported or secure. Keeping these systems running just to access the data can be expensive and introduces risk. Legacy data archiving is the process of extracting valuable information from these aging systems and storing it in modern formats. This not only helps with regulatory compliance and business continuity but also makes it possible to shut down obsolete infrastructure. By removing old data from production systems, organizations can also improve performance and reduce maintenance costs. Legacy data archiving is as much about reducing risk as it is about preserving history. Top companies that offer document data extraction services use AI to make the process seamless. Rannsolve’s AI-powered Cognitive Data Extactor (CDE) transforms unstructured data from legacy systems into actionable insights.
- Historical Data Archives
Historical archives are designed for preserving the data for the long term that holds research, cultural, institutional, or historical value. These archives are typically not accessed frequently, but their integrity and authenticity must be maintained over decades or even centuries.
For example, government documents, scientific research data, legacy corporate records, or digitized microfilm collections.
Historical archives often involve strict preservation standards, including format stability (e.g., PDF/A, TIFF), metadata tagging, and secure storage environments—both physical and digital.
- Backup Data Archives
Backup archives are copies of active data created to ensure data recovery in the event of accidents or data loss, such as hardware failure, cybersecurity incidents, or disasters. Unlike other archive types, backup archives are not primarily intended for long-term retention, but rather for short to medium-term recovery of recent data states.
They may be stored on local servers, external drives, or cloud-based platforms and are often part of a broader disaster recovery or business continuity plan.
While these represent key types of data archiving, the landscape is much broader, with many more variations tailored to specific industries, use cases, and evolving technologies. Many companies, like Rannsolve, offer digital archive management services and digital archiving solutions to take over your data overload.
Partner With Rannsolve for End-To-End Digital Archiving Solutions
With 25+ years of professional data digitization services, Rannsolve offers end-to-end digital archiving solutions tailored to meet the unique needs of organizations and regulatory standards across all industries. We are HIPAA, SOC 2, GDPR, and HITRUST Compliant, which means your data is handled with the highest standards of security, privacy, and regulatory compliance.
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