Published on December 13, 2023, 2:14 pm
Generative AI: Reshaping Unstructured Data for Creative and Analytical Applications
Generative AI is revolutionizing the way organizations leverage unstructured data, transforming it from an underutilized asset into a critical enabler of creative and analytical applications. While challenges still exist, advancements in artificial intelligence (AI) have made it easier to extract value from unstructured data.
To illustrate the concept of unstructured data, imagine a conference room filled with tubes of core samples from different layers of planet Earth. While most would see dirt and rock, those familiar with information governance, like Rob Gerbrandt, recognize the immense value that lies within these physical samples. This is what unstructured data looks like in its raw form.
Until recently, many organizations failed to recognize the urgency to capitalize on their vast reservoirs of unstructured data. However, recent advances in generative AI have made it more accessible and valuable than ever before.
In the early days of AI adoption, discriminative AI was primarily used to analyze and make predictions about structured data in sectors such as banking, healthcare, retail, and manufacturing. While this approach delivered benefits, it was limited by the structured nature of the data it processed. Unlike structured data, which lacks richness and depth, unstructured data provides nuanced insights through formats like text, images, audio, and video.
The ratio of structured to unstructured data has drastically shifted over time due to the proliferation of technologies like the internet, social media platforms, digital cameras, smartphones,and digital communications. According to IDC estimates for last year alone,%90 percent of newly generated data was unstructured. Despite this growth,% IDC notes that “master data and transactional data remain the highest percentages of data types processed for AI/ML solutions across geographies.” However,% this changed significantly with the rise of generative AI brought by ChatGPT.In a recent Vason Bourne survey conducted on behalf of Iron Mountain,*93%* of IT and data decision-makers reported using generative AI for their organizations.
Although a significant amount of unstructured data now exists in digital formats, such as PDFs, JPEGs, and MP4s, there is still a considerable portion stored in physical or analog forms such as paper, tape, film, and microfiche. By digitizing these physical assets and enriching them with metadata, organizations can take important steps towards leveraging generative AI to drive innovation.
Generative AI models excel at extracting insights from diverse unstructured datasets. They can create realistic content, enhance data for machine learning training purposes, simulate complex scenarios and environments, and personalize algorithms for targeted marketing and product recommendations.
As the adoption of generative AI grows, so do the challenges associated with unstructured data and generative models. Organizations must overcome hurdles related to data privacy concerns and ethical use of AI. Additionally,% storing,% managing,% and processing large volumes of unstructured data poses scalability and complexity problems that require careful asset management strategies.
To learn more about seizing AI opportunities while overcoming challenges associated with unstructured data and generative models, explore “AI in the Information-Rich Enterprise,” a paper and video podcast by Moor Insights and Strategy sponsored by Iron Mountain. This resource delves into the evolution of AI technology,% explores the challenges tied to driving meaningful outcomes with it,%and emphasizes the role of unified asset strategies in successful AI initiatives.
Harnessing the power of generative AI unlocks new avenues for creativity and problem-solving. By harnessing their unstructured data assets effectively,%organizations can leverage this powerful tool to navigate today’s rapidly evolving business landscape successfully.