Types of Unstructured DataImportance of Unstructured Data

Unstructured Data

What is it? And How Valuable Insight can be gained from it!

The amount of data being generated daily is so vast, IDG predicts that by 2025 there will be 175 zettabytes of data, it is estimated that as much as 80% of data is defined as unstructured.

Types of Unstructured Data

For a better distinction, we differentiate between two basic types of unstructured data: Text data and non-text data:

Text data

  • Emails
  • Documents
  • Contracts
  • Websites
  • Comments
  • News
  • Reviews
  • etc.

Non-text data

  • Presentations
  • Videos
  • Audio Data
  • Speech Recording
  • Images
  • etc.

Unstructured data can’t be easily stored in a column-row or excel table, which makes it harder to process. It is not readily searchable and difficult to evaluate, which is why for years companies did not have any way to efficiently analyze this type of data. Luckily with the recent years of advancements in technology specifically Artificial Intelligence (AI) that have been designed in order to gain valuable insights and analysis from unstructured data

Importance of Unstructured Data

Since the bulk of data produced today is unstructured data, it is vital for companies to find ways to handle and analyze it in order to be able to act on the data and make significant business decisions. In highly competitive settings, this helps companies succeed. If this information is overlooked, companies do not use everything that is accessible to them to be successful.

Although organizations have relied for years on structured data insights, it was not until instruments were created to analyze structured data that the abundance of unstructured data embedded in information became available and usable in a practical way for companies. Artificial intelligence algorithms now help automatically extract meaning from the regular volumes of unstructured data being generated. Businesses use AI tools such as data mining, text mining, text classification, sentiment analysis, keyword extraction etc. to gain valuable business insights from unstructured data. Without these tools it would be impossible for organizations to efficiently manage unstructured data. 

Customer analytics is one use case for unstructured data. When companies collect data from different sources such as chatbot conversations, twitter posts and online reviews of services or products and use AI to spot patterns or trends in the information from these sources, to take quick actions that can improve customer relationships and quickly win back dissatisfied customers. For example unstructured data can contain valuable information for the marketing team. Decision-makers discover what services or products are most relevant for their target market with the ability to easily search and analyze large volumes of data and identify trends in consumer behavior. This kind of information is extremely valuable for product development. 

“CaseWorx Baurecht” is a specified German OCR tool that specializes in Germany’s “Building Law”. Optical character recognition (OCR) is a tool that recognises text inside images, such as scanned documents and photos. OCR is used to convert any kind of images containing written text into machine-readable format. CaseWorx uses AI analytics to structure, process and solve cases more efficiently.

For businesses to fully understand the value of unstructured data they need to invest in data analytic tools in order to provide an exceptional cost-effective customer experience and help their business grow in an exponential manner. Cauliflower uses AI for it’s automated data analytics, which can analyze and categorize large volumes of unstructured text data into viable insights displayed across an interactive dashboard with different visualizations.

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