Organizations increasingly aim to be data-driven, looking to gain insights from existing information to drive decision-making and growth. While being ‘data-driven is the goal for many businesses, its execution is more difficult. For a start, a shortage of qualified data analysts means organizations struggle to hire staff with the right expertise. Deficient toolsets and faulty assumptions also present a challenge. One of the biggest obstacles, though, is that organizations are rarely making use of their entire data set: they are not taking unstructured data into account.
For the first article of this two-part series, I will explore what unstructured data is and what tools can be implemented to leverage the potential of your unstructured data for business success.
What Is Unstructured Data?
Structured data comprises numbers or text that fit into the predefined fields of a database. Conversely, unstructured data is any information that doesn’t fit neatly into a database. It includes the information stored within PDF documents, emails, or social media posts. It includes text and numbers, and in some cases video, sound, and images. These cannot be arranged according to a row and column schema.
According to Gartner, unstructured text makes up nearly 80 percent of all global content, so how can businesses analyze it?
The Right Tools
Extracting value from unstructured data requires the appropriate tools. Although there is an abundance of options out there, a modern enterprise search solution is a simple and streamlined tool that can be integrated with ease. Most importantly, enterprise search tools can leverage unstructured data by making it searchable.
How Does It Work?
Software ‘crawlers’ mine through all the content within an organization, including Microsoft Office documents, PDFs, emails, PowerPoint documents, you name it. Once that’s complete, a classified index is created comprising all the structured and unstructured data in one place. What’s left is an enterprise-wide search engine.
To get the most out of unstructured data, enterprise search tools can be modified and updated to incorporate new features and harness new abilities. For unstructured data specifically, an analytics platform with a connected set of applications may be required. One of the most important functions when considering unstructured data is Natural Language Processing (NLP), which uses a combination of artificial intelligence, computer science and linguistics to interpret the written text as a human would, rather than as a machine. This has vast applications for society, but in this case, NLP is vital as it can recognize subtle aspects of a conversation that a computer would not, such as tone.
In the modern-day, where data is so plentiful, unstructured data must be treated as a fundamental aspect of a business’s strategy, feeding into operational functions like data security and compliance to improve business efficiency and reduce risk. Agile by nature, enterprise search facilitates a digital transformation to become truly data-driven, supporting businesses in not only getting insights from unstructured data but accurately recognizing patterns within it.
In my next article, I will discuss in more detail the benefits of incorporating the insights from unstructured data in your business strategy, leaning on examples from high-value, complex use cases from industries like health and life sciences.
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