Now that AI can unravel the secrets inside a charred, brittle, ancient scroll buried under lava over 2,000 years ago, imagine what it can reveal in your unstructured data–and how that can reshape your work, thoughts, and actions. Unstructured data has been integral to human society for over 50,000 years. More recently, some of the earliest examples are ancient scrolls and tally sticks–notched wood or bones used to record transactions, debts, and ownership.
The low variety, velocity, and volume of data back then are almost impossible to imagine in the digital age, where a single company stores billions of valued assets for its customers, including critical business information, highly sensitive data, and cultural and historical artifacts. Among these are more than one billion glass pathology slides, 700 million cubic feet of records, and 68 exabytes of data.
Fast forward from ancient times to about a decade ago, when early use cases based on unstructured data (such as automobile safety and predictive maintenance) emerged. While the use of unstructured data to solve other problems has expanded over the past several years, many organizations still shy away from applying AI to unstructured data that’s born digital or stored on paper or other media. One reason is that documents, medical records, emails, images, video, and audio and so on, are almost impossible to prepare, manage, and use in AI applications before recent technological strides in areas such as AI, computer vision, and large language models such as those used in generative AI.
Unlike structured data, which fits neatly into databases and tables, etc. unstructured data:
- Lacks a predefined format, requiring sophisticated tools and techniques to process and interpret
- Has historically required significant budget and expertise to digitize, prepare, store, and analyze
- Is the subject of a complex regulatory landscape around data privacy and security concerning the storage, processing, and sharing of unstructured data.
Going back to our early examples of unstructured data and depending on what business you’re in, ancient artifacts may or may not be relevant to your organization’s goals and AI strategy. I also doubt that all the data your organization owns that’s been strategically stored or piling up is accurate and trustworthy–-nor that you need to invest in making it so if it’s irrelevant and you don’t plan to use it. But now is the time to understand what “information” should be digitized and which digitized assets should be enriched and tagged with metadata so that they are searchable and can be connected to other relevant records. It’s also time to decide how to protect both the physical and digital versions of assets that may have been overlooked in terms of AI opportunities and are subject to evolving regulatory compliance mandates.
Why organizations are betting on unstructured data for AI
The stakes are higher and the barriers to entry are lower than ever before.
High stakes: Unstructured data is essential for differentiating enterprises because it provides a rich, nuanced understanding of various aspects of the business environment. By analyzing unstructured data, enterprises can uncover trends, detect anomalies, and make more informed and nuanced decisions to gain a competitive edge.
Lower barriers: Advanced AI capabilities, including machine learning, deep learning, generative AI, and intelligent agents, are reducing the obstacles to extracting value from unstructured data. These technologies enable organizations to transform vast amounts of unstructured data into actionable insights with greater ease and efficiency.
AI can prepare unstructured data for other AI applications, minimizing the manual work of tagging, indexing, and validating data. Meanwhile, the declining cost of AI infrastructure and the rich toolsets for activating unstructured data mean that smaller organizations can afford to participate in the opportunities that AI and unstructured data make possible.
The rapid adoption of generative AI models empowers enterprises to create data, images, text, and video at unprecedented rates, feeding back into information lifecycle management and AI training processes. With everything and everyone as sources of information, organizations can transform data for search, connection, and analysis.
Does monetizing your unstructured data still seem daunting? You’re not alone. In a survey of 700 IT and data decision-makers conducted by Vanson Bourne and sponsored by Iron Mountain, 38% said that sourcing, protecting, and preparing data from physical and digital assets for use in generative AI model training is a challenge.
What if a single company with a worldwide footprint could store and manage your physical and digital unstructured data and help you monetize it?
Iron Mountain InSight® Digital Experience Platform (DXP) is a scalable software-as-a-service (SaaS) platform designed for this purpose. It ingests, prepares, and processes unstructured content for generative AI use, integrating seamlessly with your business processes via pre-built connectors or application programming interfaces (APIs). The platform includes a headless content management system, which provides centralized content management and delivery to any front-end system or device. That means developers can use any technology stack and publish content consistently across various digital channels. With its low-code/no-code architecture, InSight DXP simplifies the development process, empowering business users and non-engineers to build and customize applications without extensive coding knowledge. Leveraging advanced AI technologies, the platform automates manual processes, enhances efficiency, and integrates with the latest generative AI applications to ensure secure, private data handling.
Learn more about InSight DXP and see what IT and data decision-makers say about generative AI and the role of a unified asset strategy.
