Siloed Approaches to Unstructured Data Analytics Won’t Work: nRoad


AI-based data synthesis and analytics company nRoad has laid bare the high prevalence of untapped unstructured data, which can unlock huge potential for organizations. The company describes what organizations should do to extract value for compliance, market intelligence, and other tactical and strategic operations and gain an edge over competitors.

nRoad’s latest report delves into how organizations can leverage their trove of unstructured data and convert it into meaningful insights for competitive advantage. The AI company offers solutions specifically to transcribe data but concluded that it would take an ecosystem-wide approach to tackle unstructured data.

nRoad points out that the global datasphere will surge to 163 zettabytes by 2025, 80% of which will be unstructured. The proportion of unstructured data has remained consistent at or around 80%, at least since 2019, according to this Gartner studyOpens a new window . Despite 95% of organizations prioritizing unstructured data management, the untapped data dump still exists.

Unstructured data doesn’t have a data model, is qualitative, and cannot be processed and analyzed via conventional data tools. As a result, the interpretation of unstructured data, including text, rich media (audio, video), social media posts and conversations, internet of things (IoT) sensor data, and web server logs, is difficult. The data is not searchable and is usually stored in non-relational (NoSQL) databases.

Clearly, an opportunity exists to unlock value and expand the organizational breadth of view. nRoad estimates that two-thirds of financial data is hidden in content sources that are not readily transparent.

“The explosion of unstructured data is partly the inevitable consequence of the continued digitization of life—human communication itself is unstructured. As more of our interactions move online, the trails left by these communications continue to grow,” said Aashish Mehta, CEO at nRoad.

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“Advances in technology move faster than humans’ ability to respond to them, and the growth of unstructured data also results from novel technologies colluding with legacy bureaucracies and organizational practices. For many enterprises, the types of documents circulating within the nebula of unstructured data are identical to those from the days of fax machines.”

Mehta cites the example of consumers still needing to submit PDFs for identification while applying for a loan even though the application is being made online. Hundreds of avenues where unstructured data could have streamlined operations or assist in decision-making through actionable information currently lie unused because of the volume, velocity, variability, and variety of data.

Unstructured Data Processing | Source: nRoadOpens a new window

This is primarily why cloud-based solutions, such as Amazon’s Textract; Google’s Cloud Vision, Document, AutoML, and NLP APIs; Microsoft’s Azure Cognitive Services suite, and IBM’s Datacap fall short when performing domain-specific tasks, says Mehta.

Harnessing actionable insights is also an uphill, time-consuming, expensive, and error-prone task if done manually. nRoad says optical character recognition (OCR) is inadequate given it doesn’t contextualize extracted information.

Meanwhile, robotic process automation (RPA) is a piecemeal solution that can prove ineffective in case of any structural, layout or format-related changes between the source and target destination. Natural language processing (NLP) models such as OpenAi’s GPT-3 can help. Still, it is resource-intensive, not to mention it produces generalized outcomes that may or may not help the organization completely address niche problems.

Mehta goes on to postulate a solution that involves using an ensemble of tools holistically working together to extract accurate information from unstructured sources within specific use cases. “The landscape that emerges to tackle unstructured data will not consist of a single winner-takes-all platform,” Mehta noted.

“Instead, the ecosystem will be far more fragmented and specialized, with solutions providers responding to specific enterprise needs and generating business outcomes based on their demonstrated abilities to solve a handful of challenges relating to unstructured data rather than their abilities to solve all of them.”

Presently, organizations in the finance and retail sectors have made the most of their unstructured data.

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