Effectively leveraging data is crucial to the success of today’s enterprises. Data helps organizations hone business strategy, predict trends, better understand customers, personalize user experiences, improve engagement, increase revenue, and more.
There are two broad categories of data: structured data and unstructured data.
Structured data—think customer names, purchase records, or page views—is easy for machine learning algorithms to decipher. Unstructured data is, as its name suggests, more open-ended—examples include open-ended surveys, emails to customer support teams, social media posts, and online reviews. While most businesses collect large amounts of unstructured data daily, this information has historically been difficult, if not impossible, to leverage without investing in expensive custom data engineering initiatives.
No longer. Advances in AI and machine learning now make it possible to process and interpret unstructured data alongside structured data. This opens up substantial competitive advantages for businesses that act quickly.
In this article, we’ll dive into the differences between structured and unstructured data, advances in unstructured data management and unstructured data analytics, and the potential of this new technology for today’s enterprises.
Structured Data vs. Unstructured Data
When most people think of the word “data,” they probably picture structured data. Also known as quantitative data, structured data is information that’s easy to fit into spreadsheets or tables. In a business context, this might include customer information, sales records, website analytics, or supply chain details.
Structured data’s inherently organized form makes it easy for humans and machines alike to understand and interpret it. There are a plethora of technological tools available to interpret this data, and even the least tech-savvy business users can readily make use of it.
However, structured data is also inherently inflexible and therefore limited in what it can reveal about customers and business trends. For example, an e-commerce business might use customer click data to personalize a website experience or messaging. This is helpful but only reveals the customer’s true preferences and interest in the products.
Additionally, from a back-end perspective, structured data is typically organized in a rigid, predefined schema, which imposes constraints on how the data can be stored and accessed. Enterprise-grade storage solutions that can handle structured data at scale may be cost-prohibitive for some organizations.
Unstructured Data
Unstructured data is qualitative data that isn’t organized in any particular structured manner. It doesn’t fit neatly into a spreadsheet, which can make it harder for humans and machines alike to analyze and derive insights from it at scale. Businesses collect vast amounts of unstructured data or natural language strings every day — according to one projection, 80 percent of data will be unstructured by 2025. Examples of unstructured data include multimedia data like videos and pictures, social media posts, emails, online reviews, call transcripts, open-ended surveys, and more.
Unstructured data management is key to business success. Studies show that organizations that use customer behavioral data outpace their peers by 85 percent in sales growth and more than 25 percent in gross margin. Unstructured data gives businesses a first-hand window into customer engagement, preferences, use patterns, brand loyalty, pain points, and more and offers invaluable insight into market trends. Enterprises can use this data to identify opportunities and risks and ultimately improve business strategy proactively.
Unstructured data is information-rich and flexible. It provides a real-time picture of customer sentiment and preferences that can be hard to glean otherwise. It’s also extremely flexible and easily collected since it doesn’t need to be predefined and can be stored in a data lake for accessibility.
Unstructured Data Analytics
The benefits of unstructured data are clear. However, there are also challenges that come along with its use. Today, the typical organization analyzes and uses only 0.5 percent of its vast unstructured data stores. While machine learning tools to analyze structured data have existed for decades, until very recently, programming computers to understand natural language (a primary component of much-unstructured data) was an academic pursuit that saw mixed results, even with significant investment.
Now, advances in artificial intelligence (AI) enable businesses to seamlessly integrate AI platforms to process and interpret both structured and unstructured data streams. These tools help enterprises overcome the challenges of unstructured data—making sense of complex, open-ended documents; assessing the quality and reliability of this data at a large scale; managing sensitive information; and synthesizing large volumes of unstructured data to surface trends, predict future behavior, and more.
By leveraging unstructured data, enterprises can more effectively monitor customer behavior, detect product sentiment and purchasing trends, and ultimately better serve their target audiences. Companies can also provide more authentic and effective personalization workflows based on information customers have explicitly shared with the organization—rather than indirectly acquired data gathered through other means.
AI platforms can use unstructured data to proactively identify trends, like changes in the market or purchasing patterns, and surface these predictions to teams across the organization. This gives enterprises a leg up when honing business strategy and provides a clear competitive advantage.
How Frame AI Helps
As the leading Streaming AI platform for enterprises, Frame AI helps major brands and software companies transform unstructured data into actionable insights that reduce costs and increase customer value. Frame AI’s platform employs a breakthrough Stream-Trigger Augmented Generation (STAG) architecture to monitor and analyze massive amounts of unstructured data continuously.
Frame AI integrates seamlessly with existing data infrastructure and systems, and can be tailored to each enterprise’s unique needs. Founded by PhDs in machine learning and data science, Frame AI empowers marketing, customer experience, and product teams with tailored, best-in-class AI strategies to leverage unstructured data for business results.
