Is your organization trying every trick in the book to analyze and put together all the available data for streamlined, round-the-clock data management? The good news is that you are not alone. Most companies do not care to analyze and structure their business-critical data, while most are unable to do so and a few find it difficult to extract the right insights out of them.  

In fact, unstructured data accounts for 80 to 90 percent of all data generated in an organization 

That is a lot of unstructured data, which might hold some potential insights for the business decision-makers, simply getting lost or hidden somewhere in databases of an organization. Leveraging huge amounts of data manually is a taxing task and time consuming. This is where AI can help companies move databases from unstructured to structured. Let’s take a closer look at how artificial intelligence help enterprises tap into unstructured data to unlock actionable information for business. 

Unstructured vs. Structured Data 

Simply put, unstructured data are datasets that do not have a predefined format or structure. Such data does no good for businesses, making it so difficult to process, analyze and extract its information. In fact, the vast majority of data generated in an organization contributes to unstructured data which are usually stored in what is called a data lake. Examples include e-mail conversations, text conversations, social media posts, blogs, video, audio, call logs, and feedback forms. 

On the other hand, as the name suggests, structured data are datasets that are organized in pre-defined manner and format, such as Excel and Google Sheets, where data is added to standardized columns and rows. This makes structured data easier to store, analyze and categorize data that is simple to store, dissect and effectively usable for organizations. However, structured data accounts for less than 20 percent of all data. Examples of structured data include accounting transactions, address details, demographic information, star ratings by customers such as NPS and CSAT scores.  

Unstructured data: the hidden part of the iceberg that is all too often ignored 

Fig 1 – Structured Vs Unstructured Data 

It is data that can offer meaningful information for a business to do better than what it did the previous year. According to Forbes, there are 2.5 quintillion bytes of data created each day, and only 0.5% of data is analyzed. This means that companies are not short of data, but the means they can extract insightful information from their data. Fortunately, with latest advancements in AI and machine learning, it is possible and affordable for enterprises to easily sift through and find potential insights from the vast majority of unstructured data, which most times go unnoticed and unutilized by most companies. 

Unlike some decades ago, today customers are empowered with more choices and varieties of products/services. If there is anything, unstructured data tells us, it is the fact that customers always leave some crucial information behind. And most often, it is somewhere lying around in data centers like clothes on the floor of a teenager’s bedroom.  

How does AI solve the unstructured data conundrum?  

Almost everyone who uses smartphones are familiar with music streaming websites and real-time AI-enhanced voice assistants such as Cortana, Siri, Alexa and Google’s voice assistant. These services extensively use AI, which combines with machine learning and NLP to make sense of what people say and respond appropriately in real-time. This is one of the many examples of how AI can be potentially harnessed to discover and gain meaningful insights from data that is buried somewhere in unstructured data.  

Here are some examples of how AI can help enterprises get some insights from unstructured data: 

  • Using NLP (natural language processing to extract crucial information behind obsolete business emails, word documents, old surveys, customer reviews and social media posts 
  • Pattern Recognition algorithms can play a crucial role in finding information that can be later used to build intelligent marketing strategies 
  • By reading and understanding large blocks of unstructured text, like the kind found in emails and other messages and actually decipher what the sender wants.  

Keep your customers close and the data they leave behind closer 

So, what is the best way to process and find the latent and hidden value in unstructured data? Clearly, it is not through manual sorting, which not only does result in a complete waste of resources, but also time consuming and unnecessary when there is a possible solution out there. Looking for insights in places where data has not been refined or categorized is like looking for a needle in a haystack. Moreover, unstructured data goes hand in hand with machine learning. And, machine learning uses artificial intelligence to analyze, decipher and understand the logic in unstructured data. With NLP (natural language processing) and machine learning, it is more than possible to make machines take up mundane, repetitive tasks, so that employees can spend time on tasks that need human intelligence. 

Wrapping it up 

We are living in an era where AI is edging its way into our personal and professional lives. As enterprises are in the quest of growth and development, it is no news that AI is the first thing that gets more attention. As far as AI goes, feeding the right data to an AI model is the first and foremost step of deciding on an algorithm. This means, only structured data can give organizations that edge they need when it comes to resilience and the edge against competitors. In the past, only less than 20% of enterprises across the globe were able to gain insights into their customers and locate the right data needed to be more competitive. Now with AI, machine learning and more, companies can convert their unstructured data into powerful strategic assets.