Most of the data generated today are unstructured and still vital to understanding various aspects of the world. So, how do you analyze unstructured data? And is it even valuable?

Analysis of unstructured data typically shows you what’s the cause of a certain occurrence. Today, businesses and users are constantly generating unstructured data that are unformatted and vast. As a result, traditional techniques are no longer enough to analyze unstructured data. Hold up! Before we get to learn how you can analyze structured data, do you understand the meaning of this term?

What is Unstructured Data?

Unstructured data is simply datasets that are not organized in a predefined manner. Typically, this type of data is in textual form, for instance, open-ended surveys or social media responses. However, non-textual types can also be considered unstructured data, for example, images, audio, and videos.

The use of unstructured data is growing by the day, thanks to the use of digital services and applications. Research shows that 80-90% of most company data is unstructured. And this number continues to grow alarmingly. That makes analyzing unstructured data more valuable than ever in any industry. Analysis of structured data can provide insightful statistics that numbers can’t explain.

At the same time, there’s a significant difference between unstructured and structured data. And any business owner needs to understand this tiny difference since it might impact their business’s future. With that said, we’ve talked about the difference between structured and unstructured data and how best you can utilize both datasets to steer your company’s decisions.

Why is Unstructured Data Important?

As we explained earlier, most of the data created today are unstructured, often the source of untapped value, and can be overlooked. That being the case, when properly analyzed, these data can deliver countless insights that will help your business make informed, data-driven decisions.

Moreover, through machine learning technology, you can now automatically manage and analyze unstructured data accurately and quickly. That’s possible through NLP (Natural Language Processing), machines that read text just like humans do.

Ideally, with the right Ai on your side when analyzing unstructured data, you can comfortably eliminate repetitive tasks. For example, sifting through social media, or even routing tickets.

Ai tech can learn how to extract specific keywords, phone numbers, names, and locations and understand the data’s topics, opinions, and intent. Once the data has been analyzed, you’ll get insightful information that could help you make better and more informed business decisions. The whole process is geared towards ensuring every decision in your company is backed with proof. That doesn’t necessarily lead to the success of your business, but it’s a good starting point.

How to Analyze Unstructured Data

Analyze Unstructured Data

The sheer lack of order and bulk of information that comes with unstructured data is why analyzing and managing unstructured data can be pretty overwhelming. Moreover, unstructured data can be detrimental, especially if it takes up too much space in your organization’s storage.

That’s why managing unstructured data is an essential practice, as it prevents further confusion and saves you time. So, before you employ the unstructured data analysis techniques, you must prepare the data and make it usable for insight generation. That said, here are some tips for better management of unstructured data.

  1. Determine Your Objectives

Before you analyze unstructured data, you must first set clear and measurable end goals. Otherwise, the analysis could become useless if the end requirements are not determined. Therefore, decide on the insights you aim to obtain from the unstructured data.

Do you want to understand how customers feel about a particular product or identify a face in an image? Ascertaining your objectives beforehand helps you identify the kind of unstructured data you should collect.

  2. Identify the Relevant Data Sources

Unstructured data could come from all types of sources. That’s why before starting any form of data analysis, you must determine the most suitable and relevant communication channels for the kind of data you need.

Your data sources could be online survey responses, social media activities, or customer feedback. Therefore, depending on the end goal, you can collect unstructured data in real-time or request data at every step of a consumer’s buying journey.

  3. Clean the Data

One thing about unstructured data is that it doesn’t follow any order and often contains mistakes. That can make it quite challenging for AI algorithms to analyze unstructured data. Therefore, clean up all the extra characters before analyzing your data and reduce noise. A good place to start is checking the HTML tags, misspellings, and hashtags.

Generally, the clean-up process involves slicing the data into manageable chunks for easier processing. Speak hosts intuitive software that you can use for data clean-up. Speak’s data visualization software cleans your video and audio transcripts that features a built-in editor with up to 100% accuracy.

  4. Evaluate Your Technology Stack for Data Storage and Incorporation

To maximize your data, you need more than just data analysis tools. For instance, you will primarily benefit from data storage and information retrieval tools that will come in handy in managing the flow of unstructured data. Also, you will need data visualization tools to summarize unstructured data.

Practical data visualization tools summarize unstructured data through reports, charts, and interactive dashboards. Essentially, it transforms boring spreadsheets into actionable information you can share with your teammates.

Unstructured Data Analysis Tools

To analyze unstructured data, you need to combine a wide range of techniques and tools. Today, the tools we use for unstructured data analysis combine Natural Language Processing, data visualization, machine learning algorithms, and statistical & mathematical techniques. That creates a deep learning software program that can be trained to analyze unstructured data in specific fields.

Software companies such as Speak AI have made significant advancements, especially in analyzing unstructured data. Speak provides businesses with advanced tools that help them gain insightful information from unstructured data. That said, join 7000+ individuals and teams who are relying on Speak Ai to capture and analyze unstructured language data for valuable insights. Start your free trial or book a demo to stream your workflows, unlock new revenue streams and keep doing what you love.