Making Better Business Decisions with AI: Techniques to Analyze Unstructured Data


How Artificial Intelligence is Turning Data Mining into Knowledge Mining

As per a recent IDC study, the collective sum of the world’s data will grow from 33 zettabytes in 2018 to 175 ZB by 2025, a compounded annual growth rate of 61 percent. Nearly 30% of the world’s data will need real-time processing. Are you ready to harness the power of this data?

But at the same time, data on its own is meaningless. Without a viable way to extract insight from that data – to uncover the real story hiding just beneath the surface – it won’t benefit your business nearly as much as you probably think it will.

Of course, that demands the question – how are you supposed to gain access to that insight when you’re talking about data volumes of that magnitude? More than that, how do you finally put that data to good use when so much of it is unstructured in the first place?

Managing unstructured data and extracting value from assets like documents, emails, presentations, social media posts, videos, and images isn’t really about storage and organization. It’s about making that content as functional as possible, allowing you to derive meaningful intelligence from it so that you can make better and more informed business decisions moving forward.

For many years, businesses have relied on the slow, expensive, and error-prone process of human interaction to accomplish this goal. But now, artificial intelligence solutions aren’t just making the data mining process easier than ever – they’re also making it every bit as valuable as you always hoped it would be.

AI techniques to turn raw data into value

Automatic classification & content processing: The most immediate way that artificial intelligence can help your business get more value from your data has to do with automatic classification and content processing. Modern-day AI technologies are capable of a lot more than just text recognition. They’re also able to “understand” that text (a process referred to as sentiment analysis) and, because of that, can classify it accurately. Based on that classification, the solutions can also create automated workflows – all without requiring human intervention at any point.

Data extraction: Data extraction is another significant advantage of AI solutions for many organizations. Rather than forcing humans to spend hours sifting through the information stored in various formats such as documents, emails, audio files, etc. AI technologies can be trained to extract intelligence from the information much more efficiently than any other system. Over time, AI can even learn how to estimate the relevance of the new information in a given data extraction task.

Document clustering: Document clustering is another example – that is, using AI to cluster documents together based explicitly on the information those files contain and WITHOUT any prior interaction having taken place on your part. Many news aggregation services, with Google News being one prominent example, use this approach to group related articles into topics and categories. But with a solution that understands the similarities between documents and how they are related, you can do it with everything from documents to corporate emails and more.

Unstructured Data Handling

Unstructured Data Handling

Advanced security: AI can also help tremendously with your content in terms of sophisticated security offerings. By detecting sensitive info, they can automatically mark these documents for secure data access across the board. They’re highly accurate and use biometric techniques to identify those employees who have permission to access particular data and, more importantly, those that don’t.

Entity extraction: Finally, entity extraction. Based on data sets, AI can not only learn to tag documents, but they can also learn from repeated actions (either those that are human or machine-based) to extract various entities within a document.

On the surface, these advancements can help with tasks like classifying image and sound in addition to the text – thus adding structure to data where none existed before. But diving deeper, knowledge mining can also capture information from all kinds of sources through vision, language, and even speech.

The Power of Knowledge Mining: Breaking Things Down

Part of the reason why heterogeneous data has always posed such a problem for so many businesses is that, by its very nature, data is spread out over multiple systems and in numerous formats. Until recently, even sophisticated AI solutions had a problem with this as they were often designed for narrow applications and to meet a specific need. They couldn’t capture attributes in different types of content, which lead to spending considerable time and effort to homogenize data from all sources.

Thankfully, AI-based learning algorithms have advanced to the point where this goal is now attainable – thus eliminating the need for “cleaning” data before analysis can begin. Not only can knowledge mining capture information from many different kinds of sources, but it can also work with multiple formats through vision, language, and even speech.

Think about it like this: by design, artificial intelligence solutions require massive amounts of information to “learn” effectively. Data is, in effect, learning material for machine learning algorithms. The more data you feed these solutions during the learning stage, the better the conclusion at the end of the process. This is all thanks to the fact that modern-day AI technologies learn through examples and patterns provided by humans.

So in other words, AI doesn’t just make data mining and critical goals like sentiment analysis easier at the start of the process – they actually become even more effective at their jobs as time goes on.

You’re already seeing this play out in a wide range of different ways, particularly in terms of concepts like Enterprise Content Management (ECM). Most businesses use ECM solutions for storing and organizing their data. But by applying AI techniques like machine learning, predictive analytics, and data visualization, you instantly extract value from that data at the same time. That insight can then be used to make better and more informed decisions moving forward.

Not only that, but it makes it possible to arrive at those ideal decisions faster than ever, too – thus making it easier to capitalize on opportunities soon after they develop as opposed to being forced to sit back and watch them pass you by.

Putting Your Data to Work For You

Of course, this concept plays out in other ways, too. Yes, much of the value of AI in data mining has to do with insight extraction during the early parts of this process. But there are ways to continue to use artificial intelligence to turn that value into immediate action, too.

Maybe the most common example of this has to do with the ways that many organizations are using chatbots to improve customer experience. The Royal Bank of Scotland, for example, used AI to create a self-service chatbot on its mobile application. This almost instantly reduced the workload of real human agents by feeding information from automated conversations back into the company’s knowledge base, thus making the system smarter and more impactful.

Another example of this is the Healthcare Concierge by Welltok – an AI-powered chatbot designed for on-demand consumer guidance. The solution itself analyzes existing consumer profiles and context to personalize responses when people ask a question or have a concern using natural language processing (NLP). It can then provide on-demand answers to questions about benefits and healthcare costs for consumers. Not only does this go a long way towards creating a more personalized experience, but it also drives more efficiency and lowers cost resource utilization as well.

But the key thing to understand is that most of these use cases – from simple data mining to content processing to data extraction, analysis and more – are all happening without human intervention of any kind. Once your data sources are in place, and the solution has been set up, it literally improves its ability to accomplish these core functions every day.

Not only does this immediately help extract insight to make better business decisions, but it also accomplishes one of the most important goals of all: it frees up the valuable time of your human employees so that they can focus on those matters that truly need them.

All of this is possible even when talking about totally unstructured data thanks to the raw power that artificial intelligence brings to the table.

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