Enhancing unstructured data protection efforts. Learn how to secure and manage your organization’s valuable data assets. Unstructured data, comprising 80%-90% of the modern enterprise, is extremely valuable. But if it gets into the wrong hands, that value could be lost.
This information should be properly classified, and a security policy put into place. This is where a Panzura solution can make all the difference. We help organizations get granular protection and visibility into their most valuable files.
1. Storage
Unstructured data is information that does not conform to a predetermined data model or schema, and thus cannot be stored using traditional relational database management systems (RDBMS). This type of information includes email messages, documents, social media posts, text files, video and photos, and many more. It’s estimated that up to 80% of all the data we have today is unstructured, and that number is growing at an incredible rate.
Unstructured information requires specialized tools and software to process, mine, integrate, track, index and report the business insights that are contained within. It takes time to work with this data manually, which is why it’s so important to leverage artificial intelligence and machine learning algorithms to automate the process. When the right technology is in place, this type of data becomes a treasure trove of customer analytics. It helps businesses determine what customers are saying about them via social media, reviews of products or chatbot conversations and enables organizations to identify patterns in purchase behavior that lead to better marketing strategies.
With so much data at the fingertips, organizations need a platform that can house and manage all this information without sacrificing performance, security or cost. That’s why it’s critical to choose the best storage option for unstructured data, which can include NAS, object-based or SAN storage. According to TechTarget, NAS and object storage are well-suited for unstructured data use cases because they deliver the performance and capacity required to support these applications.
Another consideration is that the unstructured data you’re working with may be very large in volume, which could require massive amounts of storage. TechTarget suggests that the best solution for this is to use cloud storage, which provides scalable object and file storage that can accommodate these massive volumes and deliver high performance.
One way to streamline the data workflow process is to connect a storage system with a business intelligence or data analytics platform. This will enable you to automatically route unstructured data into the platform for processing, and then move the insights back out to endpoints or into other analytics platforms as needed.
2. Backups
Backups are a key component of any enterprise data management strategy, and protecting unstructured data is no different. An effective backup policy includes routinely making extra copies of files that can be restored in the event of an unplanned or catastrophic data loss event.
Structured data has a defined format that makes it relatively easy to manage and protect. Unstructured data, on the other hand, requires additional capabilities to gain business value from its contents. Data mining, which involves examining large volumes of unstructured data to find patterns that are useful to the organization, is one way to do this. Specialized software programs can also dissect the information in these files and organize it into usable form.
Then there’s archiving, which involves moving files to a secondary storage tier after a certain amount of time has passed. This can be done manually, or via an automated system that moves the files based on their age. While this helps free up space on primary storage, it can cause problems for those who need to recover a specific file from an earlier point in time.
This is why it’s important for businesses to have an overarching data protection, disaster recovery and business continuity strategy that includes both the centralized storage of structured data and the offsite backup and recovery of unstructured data. This will help ensure that the organization can always get back to where it was before a disruption occurred.
It’s also critical that backups are reliably tested. If backups can’t be recovered in the event of a disaster, they are of little use to anyone. This is especially true when a backup contains sensitive or confidential information, which can be lost to ransomware attacks or deleted by disgruntled employees.
Many organizations have an existing backup and recovery plan in place, but it’s important to review and update it periodically to include new sensitive or unstructured data. It’s also a good idea to have at least two copies of the backup data, one on-premises and one in the cloud for fast recovery, along with a robust test and restore process that is regularly conducted.
3. Security
Data comes in all different forms, from structured spreadsheets and SQL databases to text and video documents, IoT sensor data, emails and even social media posts. It’s no surprise that unstructured data accounts for 80-90% of the information in our world today and is growing faster than structured data.
But unstructured data is also a lot more challenging to manage than structured data. This is because unstructured data doesn’t have the same pre-defined structure as structured data, such as the rows and columns of a database table. Rather, unstructured data can feature any number of elements such as text, numbers and images. This makes it more difficult to organize and categorize unstructured data, resulting in a vast amount of content that is harder to locate, maneuver and protect.
This visibility challenge is compounded by the fact that unstructured data tends to be much less secure than structured data sources. The reason is that traditional security tools and strategies are often unable to effectively protect unstructured data. That’s because these tools rely on a security team’s policy changes to label unstructured data as valuable and therefore worthy of protection. Unfortunately, that process is often too time consuming and doesn’t fit the dynamic nature of unstructured data.
To combat these challenges, it’s critical that organizations invest in unstructured data management solutions that provide a comprehensive set of controls to help protect sensitive data from malicious actors. This includes centralized access management, data encryption and activity logging. This approach helps reduce the risk of unauthorized or accidental disclosures while ensuring that only authorized individuals have access to critical business data.
With unstructured data comprising 80-90% of the information in our world and growing at a faster rate, it’s imperative that enterprises take steps to protect this content. Unstructured data can contain sensitive, business-critical information such as customer contact center records (phone, chat, email and more), R&D project documents and notes, and more. However, without the proper security measures in place, this information is at an increased risk of attack by both state-sponsored and hacktivist hackers who are constantly probing enterprise networks looking for the next target.
4. Analytics
A company’s data comes in many formats and is stored in a wide range of repositories. Some are structured, containing numbers and other pre-formatted information, while others are unstructured, such as data from IoT sensors or text files that hold social media posts. Structured data typically lives in relational databases, while unstructured data tends to live in storage repositories called data lakes and other non-relational database systems.
This massive amount of data presents companies with the opportunity to gain valuable insights and support business intelligence. But to take advantage of this wealth of knowledge, businesses need to be able to process and analyze it. Unstructured data makes up 80-90% of the world’s data and is expanding at a much faster rate than structured data. But the majority of this data is not being analyzed or protected.
To make sense of the large amounts of unstructured data, it is necessary to utilize analytics, a set of tools that allow users to examine and interpret information from various sources. These tools can include search, visualization and data processing algorithms to extract and organize information for analysis. They may also provide users with alerts if they notice suspicious behavior or patterns in the data.
As an added benefit, they may help users find and identify the information that is most relevant to their specific needs or projects. Analytics tools also help companies find ways to save time, reduce costs and improve efficiency through data analysis.
The use cases for unstructured data are vast and varied. They can include anything from social media posts to scientific data, but are primarily defined by the fact that they don’t conform to a structured schema and are not formatted in advance of its use.
Typical examples of unstructured data include email and other business documents, social media content, images, video and sensor and IoT data. This type of data is often difficult to protect with traditional policy-based data protection tools because it is dynamic and constantly changing. It requires a new approach that can recognize unstructured data on its own, and provide users with the flexibility they need in collaboration-powered work cultures without stifling productivity or introducing excessive false positives.
