Specialists can analyze unstructured data by converting it into a structured format or employing machine learning algorithms

The unstructured data in your business is full of valuable information. However, you have to know how to analyze it. Unstructured data, by definition, doesn’t fit neatly within a database. To glean insights from your images, audio files, videos, text documents, emails, and websites, you need to systematize them first. That’s where unstructured data analytics can be an essential resource.

Unstructured data analytics refers to the process of organizing unstructured data, then using expert knowledge and sophisticated software to find patterns within. Analyzing this data can yield useful business knowledge. You could learn about anything from emerging product trends to overall customer sentiment. The more indexable data you can extract along the way, the more accurate your results will be.

By managing your documents efficiently and leveraging the right tools, you can optimize your day-to-day operations and make better business decisions.

Need help getting a handle on your data? Check out Unstructured Data: A Guide for Business for more information.

What is unstructured data analytics?

Unstructured data analytics is a purposely broad term. There are thousands of types of unstructured data and just as many ways to analyze them. The simplest way to understand the concept may be to compare it to structured data analytics.

Structured data comes in organized, searchable databases. These databases can store names, addresses, order numbers, and similar clerical information. To analyze structured data, you can run specific searches and get hard facts in return. Structured data analytics is a simple and direct process with a precise outcome.

Compare and contrast that with unstructured data analysis. If you have a motley assortment of images, sound, text, and code, there’s no practical way to search all of them at once. If you convert this information to structured data, you might lose vital context. If you leave it as-is, you might need a subject matter expert to sift through every resource manually. Not only is that time and resource-heavy, but, even with that expertise, some dark data may be difficult to find or interpret.

Still, the contents of unstructured data may be well worth analyzing. They could contain:

• Photos that show real images of what you sell
• Audio files that contain actual conversations with customers
• Social media messages that reveal how your audience feels about you
• Emails that communicate important business plans
• HTML code that keeps your website running smoothly

By delving into unstructured data, a healthcare company could learn how effective its treatments have been. A new restaurant could see how the local community likes its food. An online store could shore up its cybersecurity against new threats. This kind of knowledge could save money and attract new customers in the short run. In the long run, it can grow a business.

Did You Know?:RICOH PaperStream Capture software can digitize documents in just a few clicks, with intuitive controls and accurate optical character recognition (OCR) features. It can also create searchable text files and save them either locally or in the cloud.

Tools and techniques for unstructured data analytics

Generally speaking, the first step in unstructured data processing is converting as much information as possible to structured data. (There are a few exceptions, which we’ll cover.) This process is much easier if you have an excellent document management plan in place.

If you have a large backlog of physical records, for example, you should scan and digitalize them first. Software such as RICOH PaperStream Capture uses optical character recognition (OCR) technology to automatically extract text as it scans. It can then organize the scanned files in local or cloud folders.

To analyze unstructured data, consider the following techniques:

Manual inspection

Employing an expert to collate and study unstructured data can be a slow process. At the same time, it’s also a time-tested and reliable method. Every piece of unstructured data requires some type of interpretation. This is true whether you’re analyzing code for errors or listening to podcasts for mentions of a client. Humans are extremely good at finding patterns. Given enough time, they can read, watch, or listen to your unstructured data and tell you what you need to know. 

Text mining

Text mining is often the first step in unstructured data analysis. By running text documents through specialized software, you can essentially turn unstructured data into structured data. These programs pick up patterns in text and can derive information such as names, phone numbers, and email addresses. They can even isolate specific words or phrases.

You can create a database once you’ve derived specific, queryable information. From there, you can analyze the database as you would any other structured data source.

Sentiment analysis

Once you’ve mined unstructured data for text, you can do some interesting things with the results. Sentiment analysis, for example, is a way to learn how people feel about your business. Suppose you’ve text-mined a set of online reviews. You can program an algorithm to find positive or negative words, such as “good” or “bad.” You could also search for more complex phrases, such as “best food” or “worst haircut.”

Some algorithms can gauge how strongly people feel. Others use context clues to detect complex language, such as sarcasm or double negatives.

Data visualization

Data visualization is the process of turning numbers into pictures. Charts, graphics, and infographics are all examples of the format. After you’ve text-mined unstructured data, visualization provides an easy way to see what it all means.

A word cloud, for example, can show you which words occur the most in a series of online posts. A chart can illustrate customer demographics, while a graph could show you trends over time. Data visualizations are also excellent resources for websites and reports.

AI and ML tools

Artificial intelligence (AI) and machine learning (ML) are new and potentially powerful tools for unstructured data analytics. That’s because they can analyze unstructured data in its native formats.

Rather than converting information to structured data, an ML algorithm could analyze a series of pictures, podcasts, or emails and discover common threads among them. The process varies depending on your exact tools. Large language models (LLMs), for example, excel at analyzing text, while AI coding tools can fix holes in programming assignments.

Did You Know?:Ricoh provides a wide array of data management solutions, from creating cloud-based workflows to managing hardware remotely.

Contact Ricoh for expert solutions

If you have a mountain of unstructured data and no easy way to analyze it, Ricoh can help. Our scalable scanning solutions offer ways to digitize your physical records, index data from your scanned documents, and optimize workflows in hybrid and remote workplaces. Our powerful fi Series scanners capture clear, detailed images, while our robust PaperStream software can extract text and organize files.  To see how Ricoh could turn your unstructured data into actionable insights, contact us to book an appointment today.

To see how Ricoh could turn your unstructured data into actionable insights, contact us to book an appointment today.

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