Unstructured Data and Its Importance in Enterprise

11/03/2021

Unstructured Data and Its Importance in Enterprise

Data, in all its forms, plays a fundamental role in making business decisions. A company’s success depends on its ability to collect, analyse, process and act on this data. 

 

But in today’s digitally-charged world where the amount of data being generated is increasing at breakneck speed and comes in a variety of different formats, it’s more challenging than ever for companies to process this data. 

The most difficult information for companies to process is unstructured data. 

In this blog we will cover in depth what unstructured data is, why it’s important in a business context, the challenges in processing it, and the technology that’s making it possible. 

 

Table of Contents

  • Structured Data vs. Unstructured Data
    • What is structured data?
    • What is unstructured data?
    • What is semi-structured data?
  • Examples of Unstructured Data
  • Why is Unstructured Data Important?
  • Challenges of Unstructured Data
  • AI-Powered Tools to Analyse Unstructured Data
  • Final Thoughts

 

Structured vs. Unstructured Data

structured vs. unstructured

 

 

  • What is structured data?

Structured data refers to data that resides in pre-defined models or formats (think Excel or Google Sheets) and is typically mapped to standardized columns and rows within pre-set parameters, and is usually quantitative. Structured data models are designed for the ease of data entry, search, extraction and analysis. 

Structured data can be sourced both automatically and manually and depends on the creation of a data model which defines the types of data that is included and how to process it. 

  • What is unstructured data? 

Unstructured data is all the data that is not structured in a pre-defined data, but stored in its native format. Because of this lack of structure, this data is more difficult to sort through, extract and analyse. This is usually qualitative, text-heavy data or configured in a difficult way.

Unstructured types of data can actually have internal structural elements, though. The reason they are considered “unstructured” is because their information doesn’t lend itself to the kind of table formatting required by a relational database. This data can come in both textual formats and non-textual formats (e.g. video, audio and images) and generated by both people and machines.

Unstructured data is the most abundant type of data by far. According to some estimates, over 80% – 90% of enterprise data is unstructured and is growing at a rate of 55% – 65% per year

  • What is semi-structured data?

Somewhere in-between structured and unstructured data lies semi-structured data. This type of data is also typically heavy but loosely categorised with some type of tagging structure. This data can be divided into groups, separating various elements and enabling search, but the data within these groups lacks structure. 

The line between unstructured and semi-structured data can be a blurry one, however, as some argue that all data has some degree of structure.

 

 

Examples of Unstructured Data 

email

 

As mentioned above, data that does not have a recognisable and easily searchable format can be classified as unstructured and cannot be analysed thoroughly without advanced AI technology.

Below are some of the most common unstructured data examples:

  • Business documents
  • Spreadsheets
  • Emails
  • Social media posts
  • Open-ended surveys
  • Website landing pages
  • Images
  • Audio recordings
  • Text messages
  • Chat
  • Video files

It’s important to note that although the above sorts of files may have an internal structure, they are still categorised as “unstructured” because the data they contain cannot fit neatly in a structured database.

 

 

Why Is Unstructured Data Important?

Important

 

Because over 80% of enterprise data is unstructured, it’s clear that this data holds important business insights that remain untapped. 

While structured data is important, unstructured data provides a wealth of knowledge that numbers and statistics simply can’t explain. 

Organisations must find ways to manage and analyse unstructured data so they can use it to make important business decisions, giving them a competitive advantage over their competitors. Companies not taking unstructured data into account miss out on a huge amount of business intelligence. Below are some examples of insights unstructured data can hold: 

  • Customer Experience and Sentiment 

Information found in unstructured data sources can help businesses improve the customer experience by monitoring and extract important insights from phone calls, customer service chats, comments on social media or emails. Within text and audio-rich data lies a wealth of knowledge on customer sentiment of your brand and buyer pain points. 

  • Marketing Intelligence

Unstructured data can also be a gold mine of marketing intelligence. There is loads of information waiting to be discovered that tell businesses about patterns in customer behaviour and what types of product offerings or services are the most attractive to their audience. This has a direct effect on product development and other marketing initiatives and campaigns. 

  • R&D and Innovation Opportunities

News reports, blogs, social media posts and comments and online reviews of competitors are all forms of unstructured information that provides a treasure trove of information about industry and market trends. 

Uncovering this information before your competitors puts you in a prime position to anticipate changes and adjust.

  • Regulatory Compliance

Digging deeper into unstructured data can also help highly-regulated organisations discover possible compliance issues before they have a negative impact on business. 

 

 

Challenges of Unstructured Data

challenges

 

Due to this type of data’s lack of structure, it’s a challenge for conventional and legacy software to ingest, process, extract and analyse the information. Users can run simple content searches across textual unstructured data, but businesses gain very little value from information sources such as chats, voice recordings, video, social media or other types of qualitative data. 

It’s unstructured nature also means that hundreds of human man-hours are required to sort through this data manually. As one can imagine, this inefficient way of mining data is costly and highly inefficient and ineffective for enterprises. 

There is light at the end of the tunnel, though.

There are a number of existing and developing analytics tools in the marketplace. Advancements in AI tools make it possible for machines to sort through all this unstructured data automatically, allowing humans to spend their time on more high-value tasks.

 

AI-Powered Tools to Analyse Unstructured Data

tools

 

Knowing the value hidden within their data deposits, one of the most pressing issues for business leaders is figuring out how to turn unstructured data into structured data.

While structured data analytics is already a mature process with advanced technology, unstructured data analytics is a relatively “new” industry that is still developing. 

Advancements in AI tools now make it possible for machines to sort all unstructured data automatically. These data processing solutions use a combination of machine learning and natural language processing (NLP) that can power sentiment analysis, pattern recognition and speech-to-text conversions to automatically structure and analyse data quickly and accurately. 

For example, AI algorithms can extract keywords, phone numbers, names, customer sentiment, and more key insights that are important to businesses, structure it, and put it back into your business’s internal system. With all this data now organised, business leaders can make better-informed decisions. 

Intelligent Document Processing (IDP) is an emerging technology that can classify various types of unstructured documents and data variations, store them in the correct category and format, and retrieve them for various purposes. 

IDP makes use of rules-based AI models and Optical Character Recognition (OCR) to characterise and classify documents in the correct way. Simply put, IDP allows you to scan or import different types of documents into a system without requiring having to do any prior sorting beforehand.

With machine learning tools being able to process large amounts of data within seconds, companies can eliminate repetitive, mundane tasks that were once left for humans, saving them time, resources and money.

 

 

Final Thoughts

Uncovering the hidden value in your unstructured data opens previously-closed doors that lead to a wealth of untapped insights. Although mining unstructured data has its challenges, recognising its value and employing the right tools can give your business a competitive edge and save thousands of man-hours in manual processing. 

Do you want to know how you can get the most out of your unstructured data? Get in touch for a free demo.