- What is unstructured data?
- How does unstructured data differ from structured data?
- What are the challenges of managing and storing unstructured data?
- How do you analyze unstructured data?
- Unstructured Data - FAQs
- Protecting the future value of your data
What is unstructured data?
Unstructured data is data that doesn’t have a predefined schema or data model. It’s the opposite of structured data, which is typically used in traditional relational database systems (RDBMS), and formatted in rows & columns. Unstructured data can be managed with more modern technologies such as NoSQL databases, data lakes and data warehouses.
Think of all the media files, documents and emails that your organization stores outside databases — that is unstructured data. The majority of data created by your business each day is unstructured — and failure to capture and analyze it represents a significant missed opportunity. Unstructured data can provide important additional context that improves the overall accuracy of your analytics — and business decisions you make.
How does unstructured data differ from structured data?
Traditional relational database engines are capable of extracting insights from the carefully ordered data they contain (structured data). This comes at a cost however — a complex set-up and configuration that potentially limits options for future change.
Important information is often stored in unstructured sources like text files, emails, images and sensor data. The free text contained in an email is very different to mpeg-encoded video files, but both contain valuable information for generating insights. The only commonality is that they lack the formal structure required by a relational database schema.
A specialist unstructured database provides a way to store, manage, and query any digital asset. Many unstructured databases also offer capabilities to query information across various data tiers, including data lakes, online archives, clusters, and local storage.
Where structure is required, you can enforce schema validation in your applications that access the unstructured database engine.
What are the challenges of managing and storing unstructured data
Unstructured data cannot be forced to conform to the columns and rows format of a traditional relational database. Some relational databases provide support for the BLOB (Binary Large Object) type allowing storage of unstructured data but offer little additional functionality; you can store and retrieve blobs, but you still cannot query it well. You must also define the structure of data before it can be written to the database.
An unstructured database like MongoDB takes a different approach to data storage. Text files and other unstructured assets are stored as JSON formatted documents.
Because of the sheer physical volumes of data involved, NoSQL databases can scale infinitely. By building on top of a data lake or similar, capacity can be added quickly using inexpensive commodity hardware. This will be essential when dealing with real-time data like social media updates or IoT sensor feedback.
The MongoDB NoSQL engine can also be connected to your AWS, Azure, or Google Cloud platforms for maximum scalability.
How do you analyze unstructured data?
Paradoxically, unstructured data needs to have some basic level of structure applied before it can be properly analyzed. In some unstructured databases, data must be analyzed by extraction.
Imagine you want to run facial recognition on a photo. First you would map key reference points to create a series of measurements (distance between eyes, width of mouth, etc). This map can then be used to compare against key points from other photographs until you find an example of where the measurements fall within acceptable limits of tolerance.
In reality, you have applied a structure to the file that allows for comparison and analysis. MongoDB helps to accelerate this process by applying a ‘semi structure’ to assets as they are stored. All original metadata is stored in the NoSQL JSON document, while the additional semi-structure accelerates retrieval and analysis.
Unstructured Data - FAQs
Is unstructured data useful?
95% of all data generated each day could be classified as unstructured. Emails, social media updates, photographs, etc. are all unstructured – and all contain valuable information for big data analysis. With the assistance of a suitable NoSQL unstructured database, you can extract this information to add additional context and value to your analytics function.
How is unstructured data used?
Unstructured data contains important information like customer sentiment that is not easily obtained through statistical methods. Using information from multiple unstructured sources — Twitter feed, RSS feed, and inbound emails, for example — you can perform advanced textual analysis that indicates customer sentiment down to the individual level. These insights provide context, balance, and additional value to traditional insights, increasing their strategic value.
Protecting the future value of your data
By not applying rigid structure during the save procedure, a NoSQL unstructured database leaves your data virtually untouched. You are then free to structure it however you choose to access it, without changing the underlying JSON documents in any way. This ensures that all contextual information and metadata is available for future operations which may look considerably different to your current needs.
Start your journey towards an unstructured future with the free tier MongoDB Atlas subscription — no credit card required.