Unstructured data is produced in abundance by every business in some way. Whether it's images and videos, text-heavy emails, or sensor data, all of these have the potential to increase competitive advantage if meaningful, actionable insights can be extracted from them. But traditional analytics tools haven’t been optimized to pull from or make sense of unstructured data sources. This means organizations are excluding a huge cache of their data from all their analyses—and potentially leaving revenue-generating information on the table. Fortunately, advances in AI technology now enable businesses to leverage their unstructured data in exciting new ways. 

The question is, in the world of unstructured data, how can organizations extract key insights from this dataset—without the headache? 

What is Unstructured Data?

Unstructured data is anything that doesn’t have a pre-defined data model nor is it organized in a pre-defined way. It can be human- or machine-generated and usually doesn’t live in file-based systems rather than transactional ones.

Examples of unstructured data include:

• Analytics from AI and machine learning algorithms
• Sensor data
• Functional data from Internet of Things devices
• Geospatial data
• Weather data
• Surveillance data
• Collaboration and productivity applications
• Text files (e.g., emails, spreadsheets, chatbots, scholarly journal entries)

Many of today’s data management challenges stem from the fact that up to 90% of the world’s data is unstructured, and that number is only going up. According to some predictions, the amount of unstructured data will increase to 175 billion zettabytes by 2025.

The Difference Between Unstructured Data and Structured Data

While unstructured data is heavily prevalent within organizations, there is an abundance of structured data as well. Structured data are records in a database environment that can be easily mapped into designated fields (like name and zip codes) and have clearly defined data attributes. Because of this, they are easy to search and pull information from. 

On the other hand, unstructured data comes in so many different formats that it’s been difficult for a single data mining tool to be able to process, search, and analyze these. But there is a massive amount of information and insight to be found in unstructured data if you have the tools to understand it. 

What are the Advantages of Analyzing Unstructured Data?

Nearly everything we do—from collaborating with coworkers to shipping inventory to heating and cooling our offices—is enabled and improved through the analysis of unstructured data. The main benefit of analyzing this type of dataset is that it provides businesses with the whole picture of the organization so they can see exactly where opportunities, and threats, lie. 

For example, targeted marketing strategies can be improved by analyzing consumer behavior trends, call center transcripts, online product reviews, chatbot conversations, and social media mentions. Analyzing all this multidimensional data for patterns can reveal intel that better personalizes the customer web experience or determines the best time to send out email offers that lead to improved sales. 

However, surfacing these insights isn’t a simple process. 

Why is Unstructured Data Challenging to Use?

The lack of consistent structure makes this data incredibly challenging for traditional BI and analytics tools to ingest and analyze. There are two main issues with unstructured data that need to be overcome to maximize its value: expense and complexity.

1. Expense

The massive quantity of unstructured data can significantly increase costs for cloud-based storage. To keep storage expenses in check, it’s helpful to evaluate all of your organization’s data and create separate storage strategies for cold and hot data.

The unchanging or “cold” data can be stored in unmanaged cloud-based storage, freeing up your budget for storing the “hot” data that requires regular backup and replication.

Legacy data management systems are another potential source of extra spend. Legacy systems often do not play well with modern unstructured data management solutions, which may require custom-building a solution to effectively process and manage high volumes of unstructured data.

2. Complexity

Unstructured data also introduces additional complexity to enterprise data analytics. With a large amount of raw, unorganized data flowing in from many disparate sources, indexing is difficult and error-prone due to unclear structure and lack of predefined attributes.

This disorganization and lack of well-defined attributes makes it difficult for analysts to determine which datasets are relevant to a particular use case and whether the data is high-quality and trustworthy.

The Virtualitics Intelligent Exploration platform collects all kinds of unstructured data, then uses AI-based data analytics and multidimensional visualizations to surface insights that empower analysts to bring revenue-generating ideas to stakeholders. With Virtualitics, organizations in every industry can take control of their complex data management and put their unstructured data to work.

Make Sense of Unstructured Data with Intelligent Exploration

The amount of unstructured—and structured—data that your organization produces will only continue to grow. Artificial intelligence and machine learning analytics software is the key to understanding the patterns, relationships, and trends hidden with all your complex, multidimensional data. 

Virtualitics not only uses AI to power our industry-leading analytics and visualization tools, but also provides guided and automatic insights that help analysts and other non-data scientists read, understand, and use the data independently. With everyone in the organization benefiting from the power of AI-driven analysis, you’ll be able to maximize the value of every piece of business data produced.