Data archiving is the systematic process of securely storing and preserving electronic data, including documents, images, videos, and other digital content, for long-term retention and easy retrieval. This essential practice involves the transfer of data from active storage systems, where it is frequently accessed and used, to secondary storage systems specifically designed for extended preservation and infrequent access. But why do businesses need it exactly?
While we were talking about a data-driven future about 10 years ago, today we are perhaps laying the foundations of this future. Almost everyone in or around the business world is now aware of the importance of the correct use of data.
Social media applications have been able to personalize their ads, chatbots have been able to answer complex questions, and e-commerce sites have been able to personalize their product recommendations thanks to the data they collect from users.
But this data sometimes needs to be archived. So; Why, how, and when do you archive data? Let us explain.
What is data archiving?
Data archiving refers to the process of storing and preserving electronic data, such as documents, images, videos, and other digital content, for long-term preservation and retrieval. It involves transferring data from active storage systems, where it is regularly accessed and used, to secondary storage systems that are designed specifically for long-term storage and infrequent access.
The purpose of data archiving is to ensure that important information is not lost or corrupted over time and to reduce the cost and complexity of managing large amounts of data on primary storage systems.
The data archiving process involves several key steps to ensure that important information is properly stored and preserved for long-term retrieval. First, the data must be identified and evaluated based on its importance, relevance, format, and size. Once identified, the data is classified into categories to ensure it’s stored in a way that makes it easy to retrieve and manage.
After classification, the data is transferred to a secondary storage system, such as a tape library, optical disk, or cloud storage service. This system provides long-term storage at a lower cost than primary storage systems. To ensure the data can be easily found and retrieved, an index is created that includes metadata about each file, such as its name, location, and contents.
Regular backups of the archived data are made to protect against loss or corruption. The archive system is monitored regularly to ensure it’s functioning properly and that data is being retrieved and restored successfully. Data retention policies are put in place to determine how long the data will be kept in the archive before it’s deleted or migrated to another storage tier.
When data is needed again, it can be retrieved from the archive using the index. It may need to be converted or migrated to a different format to make it compatible with current technology. Finally, the data is disposed of when it’s no longer needed, either by deleting it or transferring it to another storage tier.
Why archive data?
There are several reasons why data archiving is important for your personal use and your business. Firstly, it helps organizations reduce their overall storage costs. By moving infrequently accessed data to cheaper storage media, such as tape libraries or cloud storage services, organizations can free up space on primary storage systems and reduce their storage expenses.
Secondly, data archiving helps organizations comply with regulatory requirements. Many regulations, such as HIPAA, SOX, and GDPR, require organizations to retain certain types of data for specific periods of time. Data archiving helps organizations meet these requirements while minimizing the impact on primary storage systems.
Archiving data also helps protect against data loss due to hardware failures, software corruption, or user error. By creating backups of the archived data, organizations can ensure that their data is safe and recoverable in case of a disaster or data breach.
Databases are the unsung heroes of AI
Furthermore, data archiving improves the performance of applications and databases. By removing infrequently accessed data from primary storage systems, organizations can improve the performance of their applications and databases, which can lead to increased productivity and efficiency.
Lastly, data archiving allows organizations to preserve historical records and documents for future reference. This is especially important for industries such as healthcare, finance, and government, where data must be retained for long periods of time for legal or compliance reasons.
How can AI help with data archiving?
Artificial intelligence (AI) can be used to automate and optimize the data archiving process. There are several ways to use AI for data archiving.
Intelligent data classification
Intelligent data classification is a process where artificial intelligence (AI) algorithms are used to automatically categorize and classify data based on its content, relevance, and importance; getting data ready for archiving. This process can help organizations identify which data should be archived and how it should be categorized, making it easier to search, retrieve, and manage the data.
There are several techniques used in intelligent data classification, including:
- Machine learning: Machine learning algorithms can be trained on large datasets to recognize patterns and categories within the data. The algorithms can then use this knowledge to classify new, unseen data into predefined categories
- Natural language processing (NLP): NLP is a subset of machine learning that focuses on the interaction between computers and human language. NLP can be used to analyze text data and extract relevant information, such as keywords, sentiment, and topics
- Image recognition: Image recognition algorithms can be used to classify images and other visual data based on their content. For example, an image recognition algorithm could be trained to recognize different types of documents, such as receipts, invoices, or contracts
- Predictive modeling: Predictive modeling algorithms can be used to predict the likelihood that a piece of data will be relevant or important in the future. This can help organizations identify which data should be archived and prioritize its storage
- Hybrid approaches: Many organizations use a combination of these techniques to create a hybrid approach to data classification. For example, an organization might use machine learning to identify broad categories of data and then use NLP to extract more specific information within those categories
In short, intelligent data classification can help organizations optimize their data storage and management strategies by identifying which data is most important and should be retained long-term.
Data discovery
Data discovery helps businesses by identifying and locating data that is not easily searchable or accessible, often referred to as “dark data“. This type of data may be scattered across different systems, stored in obscure formats, or buried deep within large datasets. AI-powered tools can help organizations uncover and identify dark data, making it easier to archive and manage.
AI algorithms can automatically detect and identify data sources within an organization’s systems, including files, emails, databases, and other data repositories. Also, data profiling tools can analyze data samples from various sources and create detailed descriptions of the data, including its format, structure, and content. This information helps organizations understand what data they have, where it’s located, and how it can be used.
Data compression
Data compression reduces the size of a data set by removing redundant or unnecessary information, which helps save storage space and improve data transfer times, making data archiving cost-efficient. Traditional data compression methods often rely on rules-based algorithms that identify and remove obvious duplicates or redundancies. However, these methods can be limited in their effectiveness, especially when dealing with large datasets.
AI-powered data compression, on the other hand, uses machine learning algorithms to identify more nuanced patterns and relationships within the data, allowing for more effective compression rates. These algorithms can learn from the data itself, adapting and improving over time as they analyze more data.
Data indexing
Data indexing is another important step in data archiving and it is the process of creating a database or catalog of archived data, allowing users to quickly search and retrieve specific files or information. Traditional data indexing methods often rely on manual tagging or basic keyword searches, which can be time-consuming and prone to errors.
AI-powered data indexing utilizes machine learning algorithms to meticulously analyze the contents of archived data, generating comprehensive indexes for efficient search and retrieval. These advanced algorithms excel at recognizing patterns, establishing relationships, and uncovering valuable insights hidden within the data. Consequently, this technology significantly simplifies the process of pinpointing specific files or information, saving time in finding the relevant information after data archiving.
Clustering
Clustering is a technique used in machine learning and data mining to group similar data points together based on their characteristics. AI-powered clustering algorithms can analyze large datasets and identify patterns and relationships within the data that may indicate dark data.
Clustering algorithms work by assigning data points to clusters based on their similarity. The algorithm iteratively assigns each data point to the cluster with which it is most similar until all data points have been assigned to a cluster. The number of clusters is determined by the user, and the algorithm will automatically adjust the size and shape of the clusters based on the data.
Anomaly detection
Anomaly detection is a crucial process aimed at pinpointing data points that deviate from the anticipated or typical value ranges. This technique harnesses the power of AI algorithms to detect unconventional or aberrant patterns within datasets, signifying the presence of potential hidden insights that demand further scrutiny.
The core mechanism of anomaly detection algorithms involves a comprehensive analysis of data distribution, with the primary objective of identifying data points that diverge from this distribution. These algorithms come in two primary categories: supervised and unsupervised. The choice between them hinges on the specific nature of the anomalies under scrutiny.
- Supervised anomaly detection: This approach relies on labeled data to train a model for anomaly recognition. By leveraging the known anomalies in the training data, supervised algorithms develop the capacity to discern irregularities effectively
- Unsupervised anomaly detection: In contrast, unsupervised algorithms employ statistical methodologies to uncover anomalies without the need for prior knowledge or labeled data. This versatility makes them particularly valuable for scenarios where anomalies are unpredictable or scarce
What are the best data archiving tools of 2023?
Now that we have emphasized the importance of data archiving, it is time to talk about the commercial tools that offer this service. As you know, many big technology companies offer such services. So which one should be your best choice for data archiving? Let’s take a look together.
Bloomberg Vault
Bloomberg Vault is a comprehensive platform designed to help global financial services organizations meet their regulatory obligations and business standards. Provided by Bloomberg Professional Services, this integrated compliance and surveillance solution simplifies data archiving, collection, and aggregation.
One of the key features of Bloomberg Vault is its ability to collect and aggregate primary sources of Bloomberg-originated data and corporate data required for regulatory compliance and surveillance purposes. This includes data needed for supervision and surveillance programs within the financial industry.
Bloomberg Vault also offers real-time compliance monitoring. This allows organizations to track and manage their compliance with regulatory requirements efficiently. The platform provides users with the capability to retrieve stored data securely, ensuring accessibility for audit and regulatory reporting needs.
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Microsoft Exchange Online Archiving
Microsoft Exchange Online Archiving is a cloud-based, enterprise-class archiving solution provided by Microsoft 365. It is designed to address various data archiving needs for organizations. The solution is used for data archiving, compliance, regulatory, and eDiscovery challenges associated with email management within organizations.
Exchange Online Archiving provides several features that make it an attractive option for organizations looking to improve their email management strategies. One of its key benefits is its cloud-based nature, which makes it accessible and reliable. Additionally, the solution offers mailbox quota management capabilities, which help alleviate mailbox size issues by automatically moving mailbox items to personal or cloud-based archives when they approach their allocated quota.
Another advantage of Exchange Online Archiving is its ability to configure archive policies and settings. This allows organizations to tailor the solution to meet their specific needs. For example, organizations can set up archiving policies that determine how and when mailbox items are archived. This level of control ensures that organizations can comply with regulatory requirements and internal policies regarding data retention and security.
Google Vault
Google Vault is a powerful information governance and eDiscovery tool designed specifically for Google Workspace. At its core, Google Vault helps organizations manage data within Google Workspace by providing features such as data archiving, legal holds, searching, and exporting user data from Google Workspace applications like Gmail and Google Drive.
One of the primary purposes of Google Vault is to preserve user data from specific Google Workspace apps by placing them on legal holds. This ensures that important data is not deleted prematurely and can be retrieved when needed. In addition to data preservation, Google Vault also facilitates eDiscovery by enabling users to search for specific information across Google Workspace applications. This feature is particularly useful for legal and compliance purposes.
Another significant advantage of Google Vault is its API integration. The tool offers an API that allows organizations to integrate it with their systems and automate eDiscovery processes, including managing legal matters, placing holds, and data archiving. This streamlines the process of managing data and makes it more efficient for organizations.
Proofpoint Archive
Proofpoint Archive is a cloud-based archiving solution that aims to simplify legal discovery, regulatory compliance, and data archiving for organizations. This solution provides secure storage and easy access to archived data, making it easier for organizations to manage their data and respond to legal and regulatory requests.
One of the key benefits of Proofpoint Archive is its ability to simplify legal discovery. When organizations need to retrieve data for legal purposes, Proofpoint Archive enables them to quickly and efficiently search and retrieve archived data. This saves time and resources compared to traditional data retrieval methods, which can be manual and time-consuming.
In addition to legal discovery, Proofpoint Archive also helps organizations stay compliant with regulatory requirements. The solution securely archives data and provides tools for compliance monitoring, ensuring that organizations are meeting the necessary standards for data retention and security.
Another advantage of Proofpoint Archive is its ability to leverage cloud intelligence to gain insights into archived data. With this next-generation archiving solution, organizations can gain deeper insights into their data, enabling them to make more accurate decisions and improve their overall data management strategies.
Data archiving stands as a crucial practice in the modern era of data-driven business models. It encompasses the systematic preservation of electronic data, ensuring its long-term retention and accessibility while addressing various business needs.
Featured image credit: DCStudio/Freepik.
