Data is the backbone of modern digital products, but there’s a catch. Up to 90% of data generated today is invisible with current tools.
Most data is not stored neatly in a spreadsheet, but resides in places like PDFs, emails, and social media posts.
If you’re not extracting value from unstructured data, you’re leaving insights, and revenue on the table.
Structured vs Unstructured Data – What’s the Difference?
Structured data is all about rows and columns. It’s neatly organized, making it easy to store and query in databases. It includes customer records, transaction histories, and product inventories.
Unstructured data is everything else. It doesn’t fit neatly in a spreadsheet, and that’s where the competitive edge now lives.
Imagine walking into a library where every book is neatly labeled, categorized, and indexed. If you want a book on the History of AI, you can easily find it in the history section. That’s structured data.
Now picture walking into the same library, but this time it is filled with piles of books thrown everywhere. Finding the same book will require you to sift through thousands of titles. Although rich with information, it is chaotic.
Fortunately, AI is uniquely suited to dealing with unstructured data. Instead of manually searching for what you need, an AI-powered system can scan all available information, understand the content, and instantly find the most relevant data. As a result, AI-powered tools are transforming unstructured data from a major hassle into a usable asset.
What Product Leaders Need to Know about Unstructured Data
For product leaders, data isn’t just a technical concern, it’s a strategic asset. Unlocking the value of unstructured data is now essential to drive smarter decisions, build better products, and deliver standout user experiences.
1. Insights are buried in mess
You may already possess the insights for your next product breakthrough, but they are hidden in your inbox or customer service transcripts.
Step one is to understand what data you collect and how it’s stored. You can gain a competitive edge only if you’re equipped to extract insights and find the signal in the noise.
For example, a SaaS company might search through years worth of customer support emails to discover a recurring complaint, users struggle with onboarding in the first 10 minutes. By extracting this insight from unstructured feedback they might go on to redesign the user journey and reduce churn by 30% within 3 months. Without systematically surfacing this common pain point, the opportunity for product improvement would have remained invisible.
2. Modern search is vector-based
Structured filters can’t help users find a screenshot or a vague memory from last week. If your product handles massive amounts of unstructured data, AI-powered vector search can dramatically enhance user experience. A recommendation engine, for example, uses customer reviews, browsing history, and past behavior — none of which fit neatly into a database table. If you’re not building with this in mind, you’re falling behind.
For instance, Notion, an AI-powered workspace, recently implemented AI-powered search to help users retrieve notes based on meaning, not just exact keywords. A user might type “client meeting with finance team” and manage to pull up a document called “Q1 Budget Review” even though the keywords don’t match. Traditional search would have failed here. Vector search mimics how humans think, by association, enabling intuitive retrieval of the right data in cluttered data environments.
3. Compliance is no longer optional
Structured data is easy to audit, while unstructured data, such as emails and chat logs, pose compliance risks if left unmanaged. Privacy laws don’t care if your system “wasn’t built to track that”.
As a case in point, Meta was fined €1.2 billion in 2023 under the GDPR for transferring user data to the United States without adequate safeguards. While this case focused on international data transfer, it sent a clear signal: poor data governance will not be tolerated by regulators.
If even the world’s largest platforms are held accountable for compliance lapses, smaller firms cannot afford to be complacent.
The systems you build must be compliant by design. It’s not enough to say, “we didn’t plan for that”.
Key Impacts of Unstructured Data
The way businesses handle structured and unstructured data directly shapes both customer satisfaction and financial performance.
Customer Experience
When data is well-managed, users enjoy smarter search, faster support, and more personalised responses. AI-powered assistants can analyze customer emails, reviews, and past interactions to quickly deliver more relevant solutions. Accessibility tools built on unstructured data, such as voice or image processing, can also make digital experiences more inclusive for users with impairments. Ultimately, companies that effectively manage data can provide richer, faster, and more accessible service to their customers.
Business Outcomes
1. Revenue growth
Personalised recommendations, informed by user behaviour and unstructured data like product images and reviews, can drive significantly higher conversion rates.
For example, Amazon’s recommendation system is a prime example of leveraging AI to personalize the shopping experience. By analyzing vast amounts of data, including past purchases, browsing history, and customer reviews, Amazon’s AI algorithms generate tailored product suggestions for users. This approach not only boosts conversion rates but also fosters customer loyalty by creating a more engaging and tailored shopping experience, leading to increased sales volumes.
2. Cost reduction
Automating routine processes such as email triage or chatbot support can dramatically cut operational costs.
For instance, Air Canada has integrated AI to increase operational efficiency, such as by developing an AI-enabled maintenance scheduling assistant. The tool allows for advanced planning of maintenance schedules that accommodate ever-changing variables. The technology enables planning up to two years in advance, and so helps the airline reduce costs associated with maintenance delays.
3. Compliance assurance
Proactively managing unstructured data lowers the risk of costly regulatory fines and reputational damage.
As a case in point, Deutsche Bank is leveraging AI to enhance compliance and risk management. By employing generative AI, the bank can improve its monitoring of fraud and financial crime as well as partially automate compliance reporting, simultaneously enhancing quality and reducing the time required for these tasks. This proactive approach to managing data and regulatory requirements helps the bank to mitigate the risk of costly fines and reputational damage.
4. Faster decisions
AI can surface insights hidden in raw data, speeding up strategy development and boosting business agility.
According to McKinsey, generative AI can reduce the time knowledge workers spend searching for information by up to 70%, especially when dealing with unstructured data like emails, documents, and customer conversations.
The bottom line
For product leaders, understanding unstructured data is more important than ever.
Businesses that unlock the value of unstructured data will likely outperform competitors by being able to personalize the customer experience and respond rapidly to evolving customer needs. This will translate into strong profit growth, not only due to higher conversion rates and customer loyalty, but also due to potentially lower operational costs and reduce compliance risks.
With AI making unstructured data more accessible, companies that capitalize on this technological shift early have the potential to gain an early mover advantage in our data-driven world.
Zuhair Imaduddin is a Senior Product Manager at Wells Fargo. He previously worked at JPMorgan Chase and graduated from Cornell University.
Image: DALL-E
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