This article gives a brief introduction to what unstructured data is and the importance for businesses to analyse this data in order to understand their customers better and offer them a great customer experience.
September 6, 2023
Types of Unstructured Data:
For a better distinction, we differentiate between two basic types of unstructured data:
- Text data (Emails, documents, contracts, websites, comments, news, reviews etc.)
- Non-text data (Presentation, videos, audiodata, speech recording or pictures)
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 analyse 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 analyse 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 analyse 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, sentiment analysis, keyword extraction etc. to gain valuable business insights from unstructured data. Without these tools it would be impossible for organisations 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 analyse 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 specialises 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 analyse and categorise large volumes of unstructured text data into viable insights displayed across an interactive dashboard with different visualisations.
Click here to start turning your unstructured data into valuable insights with Cauliflower.