Unstructured Data Analytics: Examples, Challenges & Techniques

Whether we like it or not, the vast majority of data is unstructured. If you’re not analyzing unstructured data, you’re missing out. 

In today’s blog post, we’ll talk about the characteristics of unstructured data, its importance, and biggest challenges. We’ll also discuss how to analyze unstructured data and choose the right unstructured data analysis tool for your business. 

_{What Is Unstructured Data?}

Unstructured data is data that doesn't have a specific format or organization. It can be textual, like emails and social media posts, or non-textual, like videos and voice recordings. Unlike structured data, which is numerical and neatly organized, unstructured data contains human language and doesn't follow a strict pattern. Unstructured data is subjective and unpredictable, which makes it hard to analyze but also allows businesses to find valuable insights, identify problems, and optimize processes. 

Let's consider emails and social media posts. Emails can have different lengths, topics, and formats. There's no fixed way that all the emails are structured, so they are a source of unstructured data. Similarly, social media posts, like tweets or Facebook posts, are unstructured because they don't follow a specific template or format. Both emails and social media posts can help you understand what your customers think about your product, but since there are so many of them and every one of them is unique, you need special tools and techniques to analyze them properly. 

_{What Are the Characteristics of Unstructured Data?}

Unstructured data refers to information that does not have a predefined format or organized structure. Unlike structured data, which is organized into fixed categories or fields, unstructured data lacks a consistent framework. Here are some characteristics of unstructured data:

• Not organized into fields or categories and comes in various formats such as text documents, emails, social media posts, images, videos, audio recordings, and others
• Features written or spoken human language, making it more ambiguous and difficult to analyze
• Comes in large volumes, which poses difficulties for storage, processing, and analysis 
• Contains rich contextual information such as timestamps, geolocation, or author information
• More difficult to query due to the lack of a predefined structure
• Requires preprocessing, cleansing, normalization, tokenization, and feature extraction before analysis
• Contains a wealth of valuable information and insights that can be extracted through NLP, ML, and data mining techniques

_{Example of Unstructured Data} 

Unstructured data can come from a wide range of sources, for example:

• Text Documents such as Word files, PDFs, plain text files, presentations, and reports
• Emails and their attachments, including the body of the email, subject lines, sender and recipient information, and any files or images attached to the email
• Social media posts, comments, shares, tweets, hashtags, and messages
• Websites, blogs, forums, online articles, online comments, and user-generated content
• Multimedia content such as images, videos, audio recordings, or podcasts
• Voice data derived from voice recordings, voicemails, call center recordings, and AI voice assistants 
• Customer feedback obtained from online review platforms, feedback forms, surveys, and social media mentions

_{The Importance of Unstructured Data Analysis}

Due to its volume and lack of organization, unstructured data can be overwhelming, so many businesses don’t tap into this valuable resource. However, with the right tools and data analysis expertise, unstructured data can help your business succeed in many ways.

_{Extracting valuable insights}

By analyzing unstructured data, businesses can uncover patterns, trends, and sentiments that are not evident in structured data. A lot of customer data, such as social media posts and customer reviews, are not structured, so while they might take some time and effort to process, they contain valuable information and actionable insights for your business. With those insights, you can identify market opportunities and drive customer loyalty.

_{Understanding customer sentiment}

Customer sentiment is complex, so it is hard to capture without analyzing unstructured data. By analyzing qualitative customer data and performing sentiment analysis, businesses can identify customer satisfaction levels, pain points, and areas for improvement. With advanced text analysis techniques, you can determine the intensity of emotion, the urgency of the feedback, and customer sentiment towards specific attributes of your product. 

_{Improving product development}

Customer feedback analysis is essential for creating a customer-centric product. Unstructured data can drive product development and innovation by identifying customer insights, feature requests, and recurring issues. This information can help prioritize product enhancements and optimize product offerings. By analyzing unstructured market data, businesses can also identify emerging trends and stay ahead of the competition.

_{Decision-making and strategic planning}

Unstructured data analysis provides businesses with a foundation for data-driven decision-making and strategic planning. Unstructured data can help businesses gain a holistic view of their operations, customers, and market landscape. This enables them to make more informed decisions, identify growth opportunities, and turn them into competitive advantages for their business. 

_{Challenges of Analyzing Unstructured Data}

Unstructured data poses several challenges for data analysts. Here are some common challenges associated with unstructured data:

_{Data volume}

Unstructured data is often vast in volume, which makes it difficult to store, process, and analyze. Manual processing of unstructured data is very cumbersome, so businesses need to prioritize resources and decide which data sources are worth analyzing. 

_Scalability

Unstructured data analysis can be resource-intensive, and scalability becomes a concern when dealing with large volumes of data. If you want to analyze large amounts of unstructured data, make sure your infrastructure and analytics tools can handle the growing volume and complexity of data.

_{Interpretation and subjectivity}

While unstructured data is a goldmine of insights, extracting and interpreting them is not an easy process. Information that comes from unstructured data is subjective, so context understanding and domain expertise is required to determine the value of data. 

_{Analysis techniques}

If you want to effectively analyze unstructured data, you need tools that go beyond traditional analytics methods. Approaches like NLP, machine learning, text mining, and image recognition allow businesses to save time on manual data processing and derive insights from unstructured data. While there is no shortage of solutions to choose from, it may take some time and data analysis expertise to find the one that meets your requirements and doesn’t break the bank. 

_{Unstructured Data Analytics} 

Artificial intelligence and machine learning transformed the way we analyze unstructured data. With ML, AI, and NLP techniques, we can extract meaningful insights and patterns from unstructured data in a fast and scalable manner. 

Before analysis can take place, unstructured data needs to undergo preprocessing steps, for example, removing irrelevant or duplicate information, standardizing formats, tokenization, and feature extraction. Afterward, data can be analyzed with NLP and ML techniques. 

Natural language processing (NLP) techniques play a crucial role in unstructured data analytics. NLP models can understand human language and utilize text analysis techniques such as sentiment analysis, entity recognition, and topic modeling. With these techniques, businesses can eliminate repetitive tasks, identify customer pain points, gauge overall customer satisfaction, and discover common themes in unstructured customer data.

ML and AI algorithms can be trained to recognize patterns and make predictions based on unstructured data. Machine learning models can also be used for tasks like image recognition, video analysis, text classification, and recommendation systems, so no matter what type of data you want to analyze, there are ML solutions that can make the analysis easier and more effective.

_{Unstructured Data Analytics Tools}

When it comes to data analytics, there’s a variety of tools to choose from depending on your requirements, domain, and data sources. For example, text analytics tools can help you extract information from textual data by leveraging ML and NLP techniques. You can use open-source libraries like NLTK by Python or spaCy for tasks like sentiment analysis, entity recognition, topic modeling, text classification, and keyword extraction. With these techniques, you can determine how your customers feel about your product, which features they talk about the most, and what aspects of your product they praise or criticize the most. 

If you’re looking for an AI-powered tool for analyzing unstructured customer data, Essense might be a perfect fit for you. Essense can analyze multiple data sources at once, such as Appstore reviews, Hubspot tickets, and Intercom conversations, and turn unstructured customer data into valuable customer insights. 

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