Unstructured Data Storage Solutions
Leveraging business insights to grow your company or improve a product or service is easy when you have access to structured data. This data has already been labeled, categorized, and essentially optimized for analysis.
In comparison, unstructured data remains difficult to analyze. This is partly due to the sheer variety of file types and the unsorted content they contain.
To give you the edge in fast-paced markets and industries, the right data storage solution will not only store your unstructured data but also automatically sift and analyze your data to glean actionable insights.
With the advent of artificial intelligence and machine learning, more data storage solutions and platforms are gaining this ability. Once the treasure trove of unstructured data is unlocked, there is boundless potential for optimization of business processes as well as targeted improvement of products and services.
What is Unstructured Data?
To understand unstructured data, it helps to first define structured data.
Defined as data that incorporates relations between entities and variables, structured data is typically quantitative. This type of information can be easily rendered in spreadsheets since the categories of data are predefined.
Some common examples of structured data include names, dates, and addresses, credit card numbers and other Personally Identifiable Information (PII), stock information and financial trends, and geolocation coordinates, among others. Structured data has formed the backbone of business intelligence because it is easily read, filtered, and analyzed by machine and coding languages.
Structured data is stored specifically in Relational Database Management Systems (RDBMS), which enable users to add, search, or manipulate data using a specific programming language called Structured Query Language (SQL). This language was developed in the 1970s by Donald D. Chamberlin and Raymond F. Boyceat, programmers at IBM, and officially adopted by ANSI and ISO standard groups in 1986.
Now that we’ve explored what structured data is, we can better understand the role unstructured data plays in business operations and growth opportunities.
Unstructured data is essentially remaining types of data or qualitative data. Text files like email messages and word processing documents, audio and media files, social media posts, graphics, and surveillance/satellite imagery all count as unstructured data.
Unstructured data is maintained in its native formats and has not been sorted or labeled; it cannot be translated into a spreadsheet through programming commands, nor can it be stored in a relational database in its raw format.
Without the predefined data model of entities, variables, and relations, running automated analyses of unstructured data is much more difficult.
Growth in Unstructured Data
Opting to work only with structured data is not an option for modern businesses and organizations. According to MongoDB, “80 to 90 percent of data generated and collected by organizations is unstructured,” and the volume of this type of data is growing exponentially, especially compared to the growth of structured data.
The knowledge we can gain from unstructured data is far-reaching and rich in diversity, particularly as more people gain access to the internet and technological devices. We are creating more data than ever before, and this data can be used for nearly any purpose:
- Advertising companies can use unstructured data to pick up on trending topics among interest groups or target audiences.
- Governments and global think tanks can chart movements or changes of populations, which then influence policy proposals.
- Logistics companies and manufacturers can better understand how their processes affect end products.
- Banking and Financial Institutions can refine existing services and develop new tools to fill gaps in customer support.
- Hospitality and Entertainment corporations can identify meaningful investment opportunities for expanded properties or amenities.
- Real Estate firms can more adeptly respond to buyers and sellers according to swiftly changing needs and market shifts.
These are only a few examples of how mining unstructured data can support common industry objectives. If it can be imagined, relevant unstructured data likely already exists – reams of valuable information that can revolutionize how businesses and organizations respond to their clients and constituents.
However, the tools needed to properly analyze unstructured data are relatively new in the field. Thus, tapping into the knowledge contained in unstructured data remains a major challenge for businesses and organizations, up to 95% of those polled by TechJury). Furthermore, “97.2% of organizations are investing in big data and AI” to develop adept solutions to unstructured data. Emerging tools and technologies will empower companies and organizations with the ability to leverage both data and content insights, expanding beyond business intelligence into lustrous fields of predictive analytics, machine learning, and data discovery and profiling.
Manage and Store Unstructured Data
To work with unstructured data, we must first be able to retain and store unstructured data. As before, we’ll first review structured data to understand the roots of data storage.
SQL databases (a type of RDBMS) are built for structured data – quantitative, numerical-based information comprising variables and entities. The tabular nature of RDBMS means that storage solutions take up less space and are also easily scaled within data warehouses. This makes a SQL Database much more cost-effective to maintain and expand as needed.
Data warehouses are familiar components to business intelligence components. They are, in essence, the central hubs for the entire system, from which all insights are derived. The warehouse serves as a repository for data and also runs queries and analyses.
As a data warehouse collects data and the databases within the enterprise grow, a rich data history develops, which provides an invaluable resource to analysts and scientists within an organization. The information is stable, flexible, and largely accessible, often referred to as “the single source of truth” within an enterprise. The data warehouse itself forms the backbone of widely recognized reporting and dashboard features within UI.
Unstructured data cannot fit into the relations-based structure of RDBMS. Non-relational or NoSQL databases must be used instead to store and manage unstructured data. However, the qualitative nature of the data makes it harder to store, even as it absorbs more space.
The answer to this challenge is found in a data lake. This type of data repository offers an interesting level of convenience and flexibility, in that data of all kinds, structured or unstructured, raw or clean, can be added. Data lakes are scalable tools that support advanced storage and processing of unstructured data such as big data and real-time or IoT analytics, as well as machine learning.
The downside of a data lake is that all that unstructured data is not organized. The very quality that makes this repository a necessary solution to recapture the value of 90% of our available data is the same quality that makes it a challenge to implement. A variety of analyses can be run using the data stored in a data lake, but that raw data needs to be processed and organized before it can deliver any meaningful insights. Without proper oversight or consistent organization of stored data, it is very easy for a data lake to become a “data swamp.”
The often raw and uncategorized nature of data lakes means that they are not often used directly by business analysts. Instead, data scientists and developers must first curate and translate data before delivering it to business analysts, who interrogate the data and adjust business decision processes. Considering the costs of expertise, a data lake may not be immediately accessible without the support of enterprise-grade developer teams.
Alternatively, smaller organizations and businesses can set up a data lake to capture data they plan to use in the future. Establishing a data lake without an immediate implementation strategy offers two distinct benefits:
- The cost to maintain a data lake is relatively low, especially compared to maintaining a data warehouse.
- Early data collection ensures a rich vein from which to work later and the ability to establish data patterns and history.
As a third option, businesses and organizations can take advantage of Software-as-a-Service or Infrastructure-as-a-Service solutions. Services include widely recognized names in the industry, including Microsoft Azure and Amazon Web Services (AWS). Azure offers both data lake storage and analysis in a two-part service: Azure Data Lake Storage and Azure Data Lake Analytics. Selecting AWS as your service provider grants you access to Amazon’s suite of complementary services, including Amazon RedShift (data warehousing), Amazon Athena (SQL Storage), and Amazon QuickSight (business analytics), among others.
FileCloud Meets Your Data Storage Requirements
FileCloud is an adept solution that is continuously evolving to meet new challenges across diverse industries. You can opt for FileCloud Server (on-premises) or have FileCloud host your data in our world-class data center with FileCloud Online.
There is also a hybrid solution available that combines the best of both worlds: on-premises hosting for high-touch data, cloud storage for archived files, and FileCloud’s ServerSync to support synchronization and easy access to files and permissions wherever you are and across your devices.
With our on-premises solution, you can choose your storage provider, either by integrating with an already-deployed service or by setting up brand new storage buckets. Integrations include Azure Blob, AWS (and AWS GovCloud), Wasabi, Digital Ocean, Alibaba, and more.
With Microsoft Azure, you can take advantage of pre-built FileCloud VMs on the Azure Marketplace, with deployment in seconds. FileCloud’s integration with Azure also ensures your active directories, permissions, and files are migrated into your FileCloud platform.
Opting for the AWS S3 storage service also provides a wealth of different options within the Amazon Marketplace. The FileCloud integration offers smooth data migration for existing files and permissions, as well as access to robust features like endpoint backup, data analytics, monitoring and governance, security, and granular permissions.
For those concerned about the relative data security of unstructured files, users can employ FileCloud’s integration with Symantec. This powerful security software suite provides anti-malware, intrusion prevention, and firewall features. Even better, Symantec’s Data Insight Solution supports administrative-level review of unstructured data to check usage patterns and access permissions.
Unstructured data is an increasingly vital element of analytics and business intelligence for companies and organizations of all sizes. The challenge lies in how this unstructured data is stored and leveraged to yield actionable insights.
Sophisticated solutions like FileCloud are working to put this unstructured data at your fingertips. By partnering with major leaders in the field, FileCloud aims to provide you with secure, scalable, efficient storage options that support sustainable growth and meaningful relationships with your target audience.