This is PART 2 of a four-part series.

Unstructured data is driving innovation across industries. After all, MIT reports that much of the current data – almost 90% – is unstructured. Yet, despite this, companies across the spectrum can’t take advantage of it and leverage it for their business for several practical and functional reasons.


Raw data that cannot be molded or fit into a form – excel, video, audio, images – is unstructured. It doesn’t conform to a data model and, as such, has no identifiable structure, is haphazard, and cannot be read by a software program. Its lack of organization in a pre-defined manner makes it unreadable. This data can be anything, from reviews for products to stock market trends and social posts. From a larger perspective, much of the world’s data is unstructured. For example, the things we read daily on Facebook or Instagram, articles that don’t have a fixed form or shape, are all very relevant and vital but cannot be deciphered for lack of a digital program or software.

To elaborate, below are a few characteristics of unstructured data:

  • It is structureless or lacks the definition of a known structure
  • Unorganized, free-flowing, not stored in neat rows or columns like a database
  • There is no format, sequence, or flow
  • Data does not follow semantic rules
  • Because of the lack of structure, no known computer programs can make sense of it

Unstructured data can be in any form or shape. The most common types are Videos, Reports, PPTs, Memos, Social posts, Websites, and Surveys.

Accessing such information by companies can help in myriad ways.

  • Building Use Cases and White Papers
  • Foreseeing product or service trajectory and intervening when necessary
  • For innovation and new learnings or product discovery
  • To derive business intelligence and discover analytics applications
  • It can be scaled, maneuvered, and optimized to use in a variety of ways

Traditional ways of accessing unstructured data that include document scans done using OCR software are labor intensive, time-consuming, and something that not many organizations have available time, nor can they afford it.

In the new means of deriving insights from unstructured data, Machine Learning is the game-changer in allowing computers to read and make sense of it, with intelligent, semantic algorithms that can access valuable information. Moreover, it can work in both big and large businesses to access as much or as little as they want to make critical business decisions.


Look around you, and you’ll find online and offline data scattered across your interactive spaces. They can include social data, contact centre data, YouTube videos and emailers, and more. So let’s examine them a little closer.

Contact Centre Data: Contact centres are the everyday places where unstructured data originates in the insurance business. These places receive calls about products and services, interact with people, store their information, and process it to give customers a great user experience. Voice recordings are the most important; they are transcribed and stored in a database for future use.

Social Media Data: Massive real-world unstructured data is found in social media, where large amounts of information go back and forth freely. Comments, review videos, and posts, are all data that help align user experiences, with companies using them all the time for advertisement targeting, product segmenting, and much more.

Email Data: Email narratives are not structured; there’s no format they are written in, no structure to maintain. Yet they contain valuable information to ensure customer engagement for marketing products and services. Customer engagement is always relevant for providing vital feedback to be worked upon or finding areas of improvement.

Review Videos: One of the most popular videos on YouTube is a video review of products and services. The #TikTokMadeMeBuyIt hashtag had nearly 6 million hits on Tiktok. Opinions like this influence others and is another form of unstructured data that is insight-heavy but not constrained by structure and can be extracted to use in myriad ways.


MARKETING – Unstructured data can help marketing teams sharpen their KPIs. Marketing teams often have tons of legacy data they don’t know what to do with it. Market Research is a manual process involving transcribing. Translating over hours can use unstructured data APIs and programs to develop planned approaches, address negative feedback, and gauge positive and negative trends in products and categories.

CUSTOMER SUPPORT – Customer support teams, can do very well with unstructured data, breaking down team siloes. Hence, they work together to derive insights from chat transcripts and product reviews, tailoring their approach towards interactions with their customers. This process helps improve the entire service delivery and makes the company more efficient.

SOLVING CORE BUSINESS PROBLEMS – Unstructured data is the tool that can solve many manually-run business services that previously suffered from chaos and errors and were time intensive. For example, automotive firms can use unstructured data to assess vehicle reviews, identify key phrases and provide better recommendations for future customers. Similarly, banking firms can look at chat transcripts from banking forums to understand service gaps and customer challenges and address or sharpen core business products.

In the insurance industry, continuously leverages unstructured data to derive deep service insights using the platform’s predictive analytics algorithm, with the ability to ascertain early intervention of claims and closure, assess product risk, unnecessary costs, attorney interventions, and more, converting behavioral patterns to actionable intelligence. As a result, is changing how the traditional insurance industry is serviced and run, with many companies slowly making inroads into the space.

PARTS 1 2 3 4

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