One of the biggest obstacles in gathering business intelligence of an organization is failing to learn from the entire data set consisting of both structured and unstructured data.
Unstructured data, such as that seen in emails and documents, is usually ignored due to a lack of qualified people to undertake data analytics, insufficient toolkits, false assumptions, and many other reasons reducing the process’ accuracy and efficacy.
When information is created without following set parameters like social media posts, reviews, blogs, reels, retail bills, podcasts, and alike it is called unstructured data.
All this data contains some information but it is very difficult to process such information as it is not properly organized.
Unstructured data can be classified based on the following traits:
Structured data is data organized in a predefined format and added to columns and rows of set parameters like in a Relational Database.
Unstructured data is a disparate group of data stored in its native format in a non Relational Database.
Business Documents, emails, legal documents, videos, chats, and anything that doesn’t conform to a preset structure qualify as unstructured data.
Business Meeting Notes, Legal Contracts, and Presentations are often produced as pdf, SharePoint files, printed documents, or even handwritten which is difficult for Data Extraction tools to decipher.
These huge amounts of unstructured data however contains important information regarding client feedback, employee details, vendor clauses, and much more.
Emails are sent as formal, informal, and marketing communication. The content inside the email is unstructured as it may contain some image, pdf, or link. It may be embedded as an image or poster within the content or come attached like a pdf.
Emails being a mode of one-to-one communication will contain very unique information.
Social Media is a sea of unstructured data. Threads on popular posts, academic or cultural groups, explainer videos, etc. often turn out to be an archive of data on a particular topic.
Customer Feedback contains crucial data for a business to grow in the right direction. This can come in the form of online surveys, forms, social media reviews, emails, and CRM. This information comes in an unstructured format.
Reviewing customer feedback will help a business to stay on track and analyze market sentiment.
Webpages contain hoards of information. They also keep continuously changing and offer the latest information.
Data in a webpage comes in the form of text, images, videos, and attachments and contains important information for sector-wise market analysis.
It is appealing to create data warehouses from existing databases and use the resulting data for analytics. An issue with this approach is its over-reliance on structured data.
There is a vast resource of information tucked under unstructured data. The majority of data created today in the form of emails, chats, reviews, blogs, AI-generated contents that are unstructured. Deciphering them can give valuable insights into business and market trends.
Analyzing customer communication data can indicate the type of language they prefer to talk in and device marketing communication accordingly.
Analyzing social media posts and reviews helps to figure out what people like about a business and what they are complaining about. This can lead to the discovery of new pain points and building of innovative products.
Performing survey analysis, especially on open-ended questions will bring in more nuances to customer feedback and make the business more sentient towards customers’ wishes.
Unstructured data once converted to structured data can be used for analysis over various machine learning and Artificial Intelligence engines to eliminate repetitive tasks for a business.
Unstructured data cannot be analyzed with the help of traditional methods and tools. As unstructured data doesn’t carry certain predefined parameters there is no one suit that fits all kind of process for extracting unstructured data. Some of the popular tools and methods for unstructured data extraction are:
Speech To Text conversion tools uses Artificial Intelligence to convert voice into text which can be further processed to extract information. A retail industry where a chunk of the feedback comes in the form of speech can benefit from such resources.
Natural Language Processing or NLP is one of the AI technologies transforming the space of data science research with its humanlike ability to interpret text. NLP run on text generated by Speech To Text tools can be an important catalyst to improve the business intelligence of an organization.
Data Stacking is a method that involves splitting a group of large volumes of data into smaller data files, and stacking each of the variables into a single column.
This involved sorting through a large volume of dataset to identify some common traits and relationships to be able to predictively analyze likely outcomes. Data mining helps to sift through repetitive data and accelerate the pace of decision-making.
Azure Cosmos DB combined with Azure functions makes storing unstructured data superfast and easy with much less code than required to store structured data in a relational database.
This comes with an AWS package and is an advanced NoSQL database management system. The schemaless nature of DynamoDB allows each data item to have a different number of attributes. This property makes it suitable for storing unstructured data which also lacks a fixed number of attributes.
Microsoft Power BI has a feature called Get Data allowing Power BI to select both structured and unstructured data across a wide spectrum of on-premise to cloud based data.
IBM Cognos Analytics combined with an AI engine like IBM Watson can consume unstructured data like product reviews or customer surveys. It can then display the sentiment towards surveyed products along with corresponding sales revenue and product inventory data.
Earlier there were not enough tools to analyze unstructured data and it was merely locked up in on-premise databases without being explored, called Dark Data. The advance of Artificial Intelligence solutions along with increased computing speed has opened up a treasure trove of enterprise level insights that the companies can’t simply let go to waste.