Unleashing the power of unstructured data: A guide to applying artificial intelligence

With almost all industry verticals going digital, it is often said that “data is the new oil”. What is often not appreciated enough, however, is that oil is not suitable to power our machines until it is refined and exists in the desired form such as diesel, gasoline, natural gas or aviation fuel. The situation is much the same with unstructured data.

It is estimated that unstructured data accounts for approximately 80% of the data generated and stored by organizations around the world. As data volumes grow, enterprises face multiple challenges, not least the need to securely store data and derive actionable insights from it at scale and speed. Today, the process of extracting relevant data from a variety of unstructured sources such as text documents, images, audio and video files, then standardizing it to create reports and inputs, and finally incorporating the findings into operational processes is easier said than done.

It is estimated that data generation in industries such as financial services is accelerating. It is expected that by 2025, global enterprises will generate 175ZB (1ZB=1 trillion GB) of data, about 80% of which will be unstructured. For most contemporary enterprises, turning this data into meaningful business intelligence is a daunting task. Traditional methods of processing unstructured data are slow, error-prone, and costly. With the constant influx of unstructured data, there is always a risk of human error, oversight, and fatigue that can overwhelm even the most experienced personnel. Optical character recognition (OCR) tools can help digitize data to an extent, but they cannot add context to it. Rewritten content: Traditional methods of processing unstructured data are slow, error-prone, and costly. With the constant influx of unstructured data, there is always a risk of human error, oversight, and fatigue that can overwhelm even the most experienced personnel. Optical character recognition (OCR) tools can help digitize data to an extent, but cannot add context to it

Even in enterprises adopting robotic process automation (RPA), although it may be able to do so by fetching it from the source It compiles data and adds it to a database, but it cannot perform formatting changes, data structuring, or any other tasks. Transforming unstructured data into structured, actionable insights can help businesses transform customer experiences and drive superior decision-making, Drive innovation and product development, reduce risk, save costs, and provide businesses with a competitive advantage. That’s why unlocking the power of unstructured data with artificial intelligence is an absolute necessity.

According to reports, organizations that utilize unstructured data can increase revenue by 10%-20% and reduce costs by 20%-50%. The global market for NLP technology is expected to reach $43.3 billion by 2025, indicating the growing demand for analyzing unstructured text data.

Big tech companies quickly acted on these predictions and developed solutions designed to address the problem. For example, Amazon launched Textract, and Google launched various APIs such as Vision, Document, AutoML, and NLP. Microsoft also enables unstructured data processing in its suite of cognitive services, and IBM also offers Datacap. There is no doubt that all these solutions are good when it comes to handling large amounts of unstructured data, exploring it and even prototyping with it.

However, these tools are industry-agnostic and often struggle to provide sufficient and accurate domain-specific insights. Errors can occur due to misunderstanding of industry terminology and incorrect understanding of complexities or commonalities between different data sets. Therefore, even if there is awareness of the need to leverage unstructured data, it is not always possible to achieve the desired results through popular or manually driven methods

To realize the full potential of unstructured data, enterprises need to invest in advanced data Analytical Tools and Techniques. Leveraging deep learning tools powered by natural language processing (NLP), artificial intelligence (AI), and machine learning (ML) can help enterprises gain domain-specific insights and identify patterns that cannot be achieved with generic solutions

a A better solution is to partner with a service provider that specializes in handling unstructured data and has extensive technology infrastructure and talent to gain accurate insights. Not only does this approach help businesses gain deeper insights on a regular basis, it does so without requiring significant internal investment in infrastructure, recruiting staff, and developing custom tools. of unstructured data is critical to modern enterprises because the insights it contains can transform business growth, operational efficiency, customer experience, and operating costs. However, to gain the best benefit, businesses must review their approach to data analysis and structuring. This process can be greatly simplified by integrating advanced artificial intelligence tools and data streams. It is through this artificial intelligence-led approach to professional unstructured data analysis that will determine the gap between future winners and losers in vertical fields such as financial services!

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