Unstructured Data Management and AI Within Enterprises

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Unstructured data has always been a major matter of concern within enterprises. Inculcating the use of Artificial Intelligence within these organizations is the suggested ultimate solution, given the highly time-consuming activity of manual examination of individual pieces.

Today, almost all the major enterprises across the globe are embracing AI technology to open new doors of development as well as to maintain the competition such that as per various studies conducted, the global AI market is expected to witness a tremendous growth from $4.07 billion in 2016 to $169.4 billion by 2025.

An intimidating issue faced by enterprises is the unstructured data, with many companies encountering various issues regarding the overall quality of data and its labelling. Given the traditionally followed norms, the transition to AI is viewed as a herculean task- almost 96% of enterprises are struggling with data management, with many companies encountering issues with the overall quality and labelling of their data. AI models are only as good as the data upon which they’re built given past data but the system should be meticulously updated by considering the future data. 

AI goes hand in hand with rigorous groundwork and constant analysis of miscellaneous data sources which can therefore be extremely time-consuming as well as complex. Fortunately, various tools are available to facilitate organizations to unclutter and organize their unstructured data. These tools include pattern recognition algorithms – these algorithms quickly tag and categorize large quantities of images, a tedious process when performed manually. 

The new concept of ‘hybrid approach to management and training’ is required to break down unstructured data and to build data hubs that store, unify, and deliver data effectively with a high level of automation. AI models are considerably more efficient and relatively effective when the machine and human processes are interspersed, thus enabling human moderators to predict and biases and prevent the possible degradation of models. Therefore, businesses should seek out such tools that combine human and machine capabilities as the dependence on either of these individual factors alone can be both cumbersome and inefficient. 

Unstructured data can seem to be a constantly overwhelming factor but this can be brought to controllable boundaries by combining AI and humans to streamline this data, making it a child’s play to extract insights across multiple applications. AI is expected to generate billions in value across industries by imbibing both sophisticated technology and domain-specific expertise. Jointly, humans and machines can transform unstructured data into vital intelligence and similar categories, thus delivered as a domain-specific expert system.

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