Digital Themes

Unstructured Data

What is Unstructured Data?

Unstructured Data refers to various types of text data, audio files, images, videos, and any other data type that can't be stored in traditional structured databases. Unstructured data is qualitative and not structured through predefined models, remaining in its native form until it is analyzed.

Aside from images, audio files, and video files, much of the valuable unstructured data come from social media conversations or open-ended survey responses. These are quite text-heavy and require the use of advanced analytics such as data mining to extract any understanding. This type of data provides further insight into customer preferences and company perceptions. Structured and unstructured data can both be housed in the cloud, but unstructured data is stored in its original format within vast data lakes and therefore takes up a lot more space than a structured database.

Machine learning technology can enable automatic data management and analysis of unstructured data, delivering instant, accurate insights that lead to informed, data-driven business decisions. Utilizing technological advancements, such as artificial intelligence (AI) and natural language processing (NLP), machines are empowered to read text as humans do, which eliminates tasks like routing tickets, manually tagging, or poring over countless social media posts.

AI technology automatically extracts keywords, recognizes opinions and intent, and identifies subjects that are central to a specific business. With unstructured data analytics tools, supplied with machine learning and NLP proficiencies, a business can skip the time-consuming practice of manually analyzing unstructured data and automate the search for valuable insights through customer emails or service tickets. Managing unstructured data involves tools that can help a business improve the customer experience and discover gaps in their business or the market. These automated tools are more efficient, accurate, and scalable than human analysts.

What are some examples of valuable Unstructured Data?

  • Social Media - Social media is somewhat organized, or semistructured data. Social media data volume grows so rapidly, that it necessitates a real-time record of brand mentions, ideas, and any statistics that can provide useful data to be mined for public or customer opinions. Some companies use unstructured data analytics tools to follow trends in the field or with competitors in real-time.

  • Business Documents - Presentations or legal documents are lengthy and can include tables, images, and PDF or XML files. While text can be organized in a predetermined format, these documents are rarely structured in a way that can be analyzed without NLP or AI technology.

  • Emails - Similar to social media, emails can be semistructured, but still unable to be organized through predetermined models. They do contain valuable information, including customer opinions and pain points, specific topics mentioned negatively (i.e. sentiment analysis), or if a customer is close to churning. Text analysis software scans through hundreds of emails, extracting pertinent customer information, organizing the data by category, and routing any issues to the appropriate departments.

  • Survey Responses - Surveys are traditionally designed with multiple-choice questions to produce responses that are easier to analyze, however, there is more to gain from open-ended questions. Responses in the customer's own voice are invaluable but need to be dismantled and reorganized into usable data before they can be accurately analyzed. Once responses are collected, business intelligence tools can be used to classify, interpret, and analyze the unstructured data.

  • Images, Audio, and Video - Multimedia files can be tagged with keywords, titles, or subjects, but are still considered unstructured as it takes advanced processing to determine what that image or audio represents. Speech-to-text technology can, as it sounds, convert audio files into text to be analyzed with NLP software. Another example is facial recognition software used to analyze images and videos.
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