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Big Data is a big deal. We currently live in an age where information is everywhere, and data is exponentially growing at a rapid pace. This data has the potential to enable machine-assisted decision making, automation and business optimization, essentially setting the groundwork for digital transformation.

Yet 60% of global data and analytics decision makers say their company is sitting on 100 terabytes of data or more and are struggling to keep up with the overflowing amount of information. Studies have shown that 80% of the world’s data is unstructured, and remains locked within Enterprise Information Management (EIM) systems. Unstructured data encompasses information that is continuously produced by various sources from social media, IoT and smart devices, emails, voicemails, presentations, legal depositions, web pages, videos, and more.

Unstructured content, on its own, or paired with structured data, can be put to work to refine a business’s strategy. But a major challenge that enterprises are struggling with today is first, understanding the complexity and volume of data that their business generates and second, knowing what to do with it.

Unlocking the Value of Unstructured Data

The unstructured data being generated every day inside and outside businesses holds targeted, specific intelligence that is unique and valuable, and can be used to find the keys to current and future business drivers. By ignoring Big Data, businesses are unable to derive actionable insights that can lead to tangible outcomes. But done correctly, intelligent corporations can turn this huge stash of dusty data into richer insights and make data-driven decisions, while reducing cost and staying ahead of the competition.

Data can be collected, fed into other applications, algorithms applied, information exchanged through deep learning and insights discovered to improve decision-making. For example, asset-intensive industries can put their data to work in the form of predictive maintenance and resource scheduling. Data can inform HR departments on hiring analysis to fill roles with the best candidates. Algorithms can be applied to help find ways to retain top talent based on measuring potential.

But for businesses to thrive in today’s dynamic digital landscape, they need the right tools to unlock data from every known channel, with access to insights that lies in the deepest depths of an enterprise. Advances in text analytics and machine learning are giving companies more power to cross-examine unstructured content, rather than leaving them to rely on intuition and gut instinct.

Practical AI: The Right Tools will Bring Positive Outcomes

There’s no doubt that Artificial Intelligence (AI) is changing the world. And it’s changing the enterprise first – including some of the most paper-intensive industries such as healthcare, government and banking. Utilizing practical uses of AI, like text mining and AI-augmented capture, enables organizations to bring their data to life regardless of the source.

Text mining is a process that allows a machine to read unstructured textual data, which usually contains more valuable context and insights than its structured counterparts. With this technology, machines are able to learn to not only identify any mentions of people, places, things or events, but can also read written text and assign emotional tone to each of these mentions. Using the financial sector as an example, sophisticated analytics allows banks and financial organizations to spot and understand trends, like common product complaints or frequently asked questions.

AI-augmented capture takes text mining a step further by capturing and interpreting content locked in documents, be them in paper form, scanned in, or in your digital files. It then uses AI to tread and understand the content in documents, classifying them more effectively, so that they then can be automatically routed to the right people, with the right priority level and in compliance with the sensitivity of the information in the document. For example, government entities and large enterprises struggle to grapple with the pure volume of incoming information as part of critical operations, from onboarding, invoicing, claims, customer correspondence, and more. But these tools largely reduce the need for error-prone, time-consuming manual intervention for all processes.

Using the right tools can provide visibility into what customers are valuing at that moment and allows organizations to identify new product categories or business opportunities. By deploying these practical uses of AI and analytics, organizations will shift from being data-driven to insights-driven, which has become an absolute must for companies to keep up with competition in the modern day and age of the digital revolution.