Unstructured data is produced in abundance across every organization in some form or another. Whether it’s images, videos, text-heavy emails, or system 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 operational data from analyses—and potentially leaving critical insights 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.
Unstructured data, on the other hand, comes in many formats, from intelligence to free-text field reports, making it difficult for traditional analytics systems to process, search, and analyze at scale.
However, there is a massive amount of information and insight hidden in unstructured data if organizations have the tools to understand it in context of operational objectives.
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 a complete operational picture so leaders can see exactly where opportunities and threats lie across the lifecycle.
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.
For example, supply chain strategies can be improved by analyzing inventory trends, parts inventory lists, staffing plans, procedures, repair times, repair logs, and shift planning,
Analyzing all of this data for patterns can reveal intel that allows teams to understand supply chain constraints in the context of failure rates and availability in order to better support maintenance actions.
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.
Legacy data management systems also frequently struggle to integrate with modern unstructured data management solutions, often requiring a custom-built system to process high-volume, high-velocity data from distributed operational environments.
2. Complexity
Unstructured data also introduces additional complexity to enterprise data analytics and decision-support systems.
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.
This makes it challenging for analysts to determine:
Which datasets are relevant to a specific use case
Whether the data is reliable, timely, and trustworthy
How disparate sources connect across the operational picture
The Virtualitics AI Platform takes all types of unstructured data, then uses AI-based data analytics to surface insights that empower analysts to support more informed decision-making across operational scenarios. 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 AI
The amount of unstructured and structured data that your organization produces will only continue to grow as operational environments expand. Artificial intelligence and machine learning analytics software is the key to understanding the patterns, relationships, and trends hidden within all your complex datasets.
Virtualitics not only uses AI to power our industry-leading analytics tools, but also provides guided and automated insights that help analysts and other non-data scientists interpret data independently, supporting faster decision cycles and improved readiness.
With AI-driven analysis embedded across the enterprise, teams can maximize the value of every operational dataset and improve decision advantage across the organization.
