Unstructured data can be a gold mine for AI insights. Yet many organizations underestimate the value (and the complexity) of their unstructured data. SharePoint files, email archives, contract PDFs, manufacturing notes, and more contain a wealth of untapped insights. Bringing this data into an AI project necessitates a strategy around data integration, privacy, and organizational buy-in.
Unstructured data isn’t neatly stored in rows and columns. It might be text-based (emails, contracts, or reports) or contain images and other media. Organizations have more of it compared to traditional structured data. This data often holds unique insights about business processes, customers, and operational bottlenecks, but it’s not inherently organized. Extracting meaningful insights demands a strategic approach.
Below are a few key considerations when introducing unstructured data into an AI solution.
Before AI models can process unstructured data, organizations must collect and centralize it. Know where the data is stored across the company, and make sure that data is relevant and up to date.
Integration platforms (iPaaS solutions) can link disparate systems (from SharePoint to ERP to CRM) so unstructured data can be accessed in a secure, unified environment.
Privacy matters for all data, structured or unstructured. Unstructured data frequently includes sensitive or personal details—think salaries, customer invoices, or confidential contract terms. Masking or anonymization techniques can protect individuals’ privacy while still allowing AI to use the data.
Once unstructured data is secure and centralized, AI can help unlock insights in ways humans can’t (or don’t have the time for). A few examples are below:
Data that would normally take someone weeks to parse is now significantly easier to understand. The information previously ignored is now giving companies a competitive edge.
Rather than restricting AI to a select group of data scientists, consider an “AI-as-a-service” model within your company. Provide user-friendly tools that allow employees across different functions to explore relevant data, test AI features, and propose new use cases.
With the right guardrails democratizing AI helps teams solve their own challenges and accelerates adoption.
Working with unstructured data is an iterative process. Start small with a pilot project to prove AI’s value. Each success story helps create buy-in across the rest of the organization. Use employee feedback to refine workflows and improve training. When something works, consider how to use the successful techniques across the organization or among other unstructured data troves.
The future isn’t about replacing human expertise but rather changing how it’s harnessed. Using AI for unstructured data analysis can shift how (and how fast) decisions are made. It can drive value and derive insights in previously unfeasible ways. Those who invest in AI-driven unstructured data insights now will be better positioned for future success.
Unsure where to start? We’d be happy to provide guidance. Reach out to us anytime at info@trenegy.com.
Trenegy is a non-traditional consulting firm based in Houston, Texas.