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Unstructured Data
Updated on:
July 19, 2023

Unstructured Data

Unstructured data refers to digital information that is not organised in a predefined format (e.g. table rows and columns), making it more challenging to analyse and process for in order to extract valuable insights, compared to structured data.

  • Unstructured data is not organised in a predefined format
  • Difficult to process and analyse
  • Examples include text, images, videos, and audio files
  • Unstructured data requires advanced analytical tools and techniques for effective utilisation

About FullCircl

FullCircl is a Customer Lifecycle Intelligence (CLI) platform that helps B2B companies in financially regulated industries do better business, faster. Its solutions allow front and middle office teams to win the right customers, accelerate onboarding and keep them for life.

FullCircl has merged with ID&V platform provider W2 Global Data to provide regulated entities with the next generation of regulatory compliance.

Unstructured data, as the name implies, is data that does not adhere to a specific structure or format, making it difficult to organise, process, and analyse using traditional database management systems.

Unstructured data comes in various forms, such as text, images, videos, and audio files. Examples of unstructured data include websites, news articles, customer emails, social media posts and interactions, call transcripts, and scanned documents. As this data is not easily quantifiable, it poses unique challenges for organisations seeking to harness its potential value.

To effectively leverage unstructured data, financial services organisations need to employ advanced analytical tools and techniques. These may include natural language processing (NLP), machine learning algorithms, and artificial intelligence (AI) to extract valuable insights and make better-informed decisions. For instance, analysing customer communication data can help organisations understand customer sentiment, assess risk, and develop targeted engagement strategies.

A good example of unstructured data in the financial services sector is adverse media, which includes negative news coverage and other publicly available information concerning individuals or entities. Financial organisations must monitor and evaluate adverse media to identify potential risks, meet regulatory compliance requirements, and protect their reputation. Unstructured data from adverse media can be found in a wide range of sources, such as news articles, blog posts, social media, and web forums.

Due to the unstructured nature of adverse media, conventional data processing methods are often insufficient for effectively analysing and extracting relevant information. Advanced analytical tools, such as natural language processing and machine learning algorithms, enable organisations to sift through vast volumes of unstructured data to identify and assess potential risks associated with their customers, partners, or even their own operations. By incorporating adverse media analysis into their risk management and compliance strategies, financial services organisations can proactively identify potential threats, maintain regulatory compliance, and preserve their reputation in an increasingly competitive market.

In conclusion, unstructured data represents a vast and largely untapped resource for financial services organisations. By implementing cutting-edge analytical tools and methodologies, these institutions can unlock the hidden value within this data, leading to improved decision-making and a competitive edge in the market.

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