Unstructured data 101

  • Understand what unstructured data is and why you should care about it
  • Learn why analysing unstructured data is difficult
  • Learn how you can analyse unstructured data out of the box
min read

What is unstructured data?

Unlike structured data, unstructured data is information that is not arranged by a pre-set, conventional data model or schema. Unstructured data therefore cannot be stored in traditional relational databases and it cannot be analysed by traditional Business Intelligence tools.

Common unstructured data categories are text, images, audio and video. Examples include emails, customer comments, project documents, presentations, social media posts, rich media of websites, text messages meeting recordings and more.

Unstructured data is everywhere, eating all data

MIT and MongoDB report that 80-90% of data generated by companies is unstructured and the volumes rapidly increase every day. Even though unstructured data presents significant competitive advantage to companies, it is locked away for most businesses.

Unstructured data drives better decision making

Executives who are 24% more likely to exceed their business goals state that unstructured data is “one of the most valuable sources of insights” – according to a Deloitte survey. Even though, only 18% of organisations take advantage of unstructured data.

Data driven decision making is about making decisions based on actual data rather than intuition and high-level observation alone. People at every level of the organisation have conversations that could be supported by insights gathered from unstructured data.

Hidden insights in unstructured data support business goals empowering everyone in the organisation to make better decisions.

Analysing unstructured data at scale is impossible with traditional tools

It all starts with realising the full value of your data. Then it requires investment in artificial intelligence and non-traditional data analytics tools.

The reason for this is unstructured data is qualitative in nature, present in so many formats that aren’t uniform and it doesn’t fit into predefined data models. It can’t be stored and managed in relational databases using structured query language (SQL) nor it can be processed and analysed with traditional BI tools at scale.

Vector technology; a state of the art solution to analyse unstructured data

Vector technology is a subset of artificial intelligence. Vectors are mathematical representation of qualitative information. They store the meaning of unstructured data in a language that machines can understand. Vectors can be obtained from neural networks, on almost any type of data.

Relevance and similarity problems are present in all facets of businesses; when it comes to identifying common patterns in large qualitative datasets, predicting risk based on historical patterns, recommending similar actions / content / products based on criteria that is hidden to humans, or searching by meaning and more.

Vector technology is The state of the art technology for solving similarity and relevance problems.

How can you take advantage of your unstructured data?

Your unstructured data is supposedly everywhere. Without being analysed it remains dormant and inactive. So it’s a no brainer to tap on it. It really is a low hanging fruit if you have the right approach and the right tools.

This is where RelevanceAI comes into play. An out of the box, end-to-end solution

Book your platform demo here with our vector experts and learn how you can take the next steps.

Alternatively with knowledge of Python and Juypiter notebooks, you can create an account and get started today.

Unstructured data 101
Benedek Zajkas
April 12, 2022
Find out how your business can glean insights through unstructured data with vectors

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