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Unlocking The Power of Unstructured Data with Artificial Intelligence

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By Aashish Mehta Chief Executive Officer, nRoad

With almost every business vertical going digital, it is often said that ‘data is the new oil.’ However, what is often not given enough importance is that oil is not fit to drive our machines until it is refined and presented in the desired forms such as diesel, petrol, gas or aviation fuel. The case with unstructured data is almost identical.

It is estimated that unstructured data accounts for approximately 80% of the data generated and stored by organisations globally. As the data volumes grow, companies grapple with multiple challenges, especially the need to safely store the data and gleam actionable insights from it at scale and speed. Now, this process of extracting relevant data from the diversity of unstructured sources, such as text documents, images, and audio and video files, then standardising it to create reports and inputs, and finally incorporating the findings into operational processes is easier said than done. According to estimates, industries like the financial services sector are witnessing accelerated growth of data generation. Enterprises all over the world are expected to generate 175 Zettabytes (1 ZB = 1 trillion GBs) of data by 2025, and as mentioned earlier, about 80% of this will be unstructured. Turning this data into meaningful business intelligence is a Herculean task for most contemporary enterprises.

Conventional approaches of dealing with unstructured data are slow, error-prone, and costly. There is always the risk of human error, oversight and fatigue caused by an incessant inflow of unstructured data that can overwhelm even the most experienced personnel. Optical character recognition (OCR) tools can help with the digitisation of data to some extent, but it cannot add context to it. Even in companies where robotic process automation (RPA) is undertaken, while it might be capable of compiling data by picking it up from sources and adding it to the database, it can’t carry out format changes, data structures, or any other tasks that convert unstructured data into structured actionable insights that can help businesses transform their customer experience, facilitate superior decision-making, fuel innovation and product development, mitigate risks, save costs, and give the enterprise a competitive edge. That’s why it is absolutely essential to unlock the power of unstructured data with artificial intelligence.

According to a McKinsey report, organisations that harness unstructured data can achieve a 10%-20% increase in revenue and a 20%-50% reduction in costs. The global market for NLP technologies is projected to reach $43.3 billion by 2025, indicating the growing demand for analysing unstructured textual data.

Large tech enterprises have been quick to act on these predictions, and have built solutions that aim to address the problem. For instance, Amazon has come up with Textract, Google has various APIs such as Vision, Document, AutoML, and NLP. Microsoft has also enabled unstructured data processing in its Cognitive-Services suite, and IBM has Datacap to offer. There is no doubt that all of these solutions are good when it comes to handling large chunks of unstructured data, and to explore it or even use it for prototyping. However, these are sector agnostic tools and they often struggle to provide adequate and accurate domain specific insights. There could be errors due to the incorrect understanding of industry terminology, and understanding the complexities or commonalities between different data sets. Thus, even when you become aware of the need to leverage unstructured data, it is not always possible to achieve the desired outcomes through prevalent or manually-driven approaches.

To harness the potential of unstructured data, businesses need to invest in advanced data analytics tools and techniques. Usage of deep learning tools powered by NLP, AI, and ML can help them derive domain specific insights and identify patterns that generalist solutions are not able to achieve.

The other and more potent solution would be to work with a service provider that is specialised in crunching the unstructured data and has extensive technology infrastructure and talent to derive precision insights. This approach not only helps enterprises get access to greater insights regularly, but also eliminates the need for heavy in-house investments in infrastructure, hiring personnel and developing customised tools.

Conclusion
Unstructured data is of utmost importance for any modern business as it holds insights that can transform business growth, operational efficiency, customer experience and operational costs. However, to achieve the best yield, it is important that companies review their data analytics and structuring approaches. Integration of advanced AI tools with data-flows can simplify the processes to a great extent. It is this AI-first and specialised approach towards analysis of unstructured data that will separate tomorrow’s winners from losers in verticals such as financial services!

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