The Data Differentiator: Unlocking The Power Of Unstructured Data To Fuel AI
In the rapidly evolving digital era, unstructured data has become a key player in businesses’ digital transformation journeys. Encompassing a vast array of information forms (from videos and audio clips to images and text), this data type is growing exponentially. For the first time, advanced AI technologies enable us to process, reason and extract meaningful insights from this data, unveiling a world of untapped business opportunities.
A staggering 80% of all data generated is unstructured, such as video surveillance data, images, videos and loT sensor data, which pose both immense potential and significant challenges for organizations looking to catalog and analyze their data. Effectively managing and leveraging this data with AI-powered tools is not just an option but a necessity for businesses aiming to uncover hidden insights and create value.
Recent strides in data storage and AI technology innovation are simplifying key complexities of unstructured data management, allowing organizations to move from merely managing data to turning proprietary unstructured data into a competitive differentiator. Enterprises are increasingly leveraging the newfound ability to generate actionable, data-driven insights from their data. Organizations have to understand the dynamic world of unstructured data, its management challenges, and how new AI capabilities can help simplify managing and enriching this data.
The Unstructured Data Landscape
Organizations today have millions or even billions of files, but their traditional storage systems are confined to storing file names without insight into the actual content of that file. This lack of context is the challenge in keeping unprecedentedly high volumes of unstructured data that companies want to retain for insights and analysis.
How do you make sense of that much information when you can’t even discern what exactly it is or what purpose it might eventually serve? How do you know what’s happening in every clip of that video surveillance footage, for example? Is there a slip and fall? Did someone get injured?
Traditionally, it would require hundreds of hours of human resources to manually search through every video clip to find that data. With AI, however, the organization can automatically tag, annotate, catalog and organize the data—making it easily searchable, accessible and, ultimately, usable in a new way.
Data enrichment should be in the very DNA of the storage architecture. By infusing AI capabilities into the core of data storage, organizations empower their unstructured data to contribute to business insights and outcomes actively.
AI Differentiation Using Your Data
AI models require massive amounts of data from which to learn. Most organizations are using readily available data on the internet to fuel and train their AI models. However, what sets you apart from every other organization in the data-driven arena and gives you a differentiated advantage is your unique data—things like your organization’s support calls, training videos, research data, video surveillance footage and more.
It is exclusively shielded from the homogeneity of generic datasets that others can access. It is a tailored asset inherently aligned with the intricacies of your operations. This level of customization ensures that every piece of information extracted is relevant and essential to giving your organization a critical competitive advantage. Organizations that have archived and retained all of their unstructured data will have a distinct advantage over those that have “thrown theirs away.”
The rise of AI and unstructured data—and, therefore, the value contained in that data—has shifted how organizations think about their storage and infrastructure. An end-to-end strategy is essential because extracting value is about navigating the entire life cycle of unstructured data, from creation to enrichment and analysis to archival, ensuring that AI intelligence is embedded at every step.
To begin this journey toward implementing an AI-based data strategy, there are several steps organizations should take to future-proof their organizations.
It begins with a thorough evaluation of existing data assets, with a focus on assessing their quality, accessibility and relevance. Clear goal-setting aligned with specific business objectives—complemented by establishing measurable key performance indicators (KPIs)—serves as a cornerstone for success. On the technology side, establishing an AI model evaluation process as early as possible is a challenging but critical step.
Another important aspect is standardizing data collection, storage and management processes with an emphasis on automation, which ensures consistency and reliability and must be maintained from the beginning of the AI initiative.
Simultaneously, the development of robust data governance policies addresses critical concerns surrounding privacy, security and compliance. The capabilities of AI technology are growing exponentially, and staying at the forefront of it is not negotiable. For example, emerging methodologies such as retrieval-augmented generation (RAG) can effectively harness internal expertise while mitigating potential challenges such as hallucination and keeping the proprietary data secure.
More than ever, the success or failure of fast-moving AI projects hinges on two main factors: the team and the culture. When we encourage a culture where trying new things and sharing knowledge is valued, it helps us work together better and get better results. It’s important to keep investing in training our teams so they understand data and AI better. This helps everyone stay on the same page and keep improving fast.
Unstructured data, once an overlooked aspect of data management, has now become a cornerstone in the AI-driven business landscape. The organizations that will thrive are those that recognize the intrinsic value of their unique data and leverage AI to transform this data into actionable insights. As we look ahead, the ability to manage, enrich and use unstructured data will not just be a competitive differentiator but a fundamental criterion for success in the digital age.
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This article was written by Forbes and originally published here.
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