Unstructured Data

What is unstructured data?

Unstructured data refers to information that does not fit into a pre-defined model or structure. It is typically non-relational and often unorganized or incomplete, unlike structured data which fits into well-defined models, such as tables and fields.

Unstructured data can include emails, documents, audio files, videos, webpages, etc. It is usually more difficult to process and analyze than structured data, as it requires advanced analytics tools such as Natural Language Processing and Machine Learning.

Unstructured data can provide valuable insights that would otherwise be overlooked or ignored. Because of this, many businesses are turning to unstructured data analysis in order to gain a competitive advantage and make decisions based on more accurate insights.

What are some examples of unstructured data?

Unstructured data is information that does not have a pre-defined data model or format. This type of data is growing in popularity as more complex datasets are created, requiring new strategies for organizing and analyzing them. Examples of unstructured data include

• webpages
• PDFs
• audio files
• videos
• images
• social media content

Unstructured data use cases

Fraud detection

Financial institutions are constantly grappling with the challenge of detecting fraudulent activities. Unstructured data, including emails, customer support transcripts, and social media interactions, can be analyzed to identify suspicious patterns and potential fraud. Machine learning models can be trained to flag potential risks, helping financial institutions prevent fraud and maintain a secure environment.

Sentiment analysis

Unstructured data in the form of customer reviews, social media comments, and forum discussions is valuable for understanding public opinion about products, services, or brands. Sentiment analysis uses natural language processing techniques to analyze text to identify emotions and opinions. This information can help businesses make informed decisions, improve customer experience, and design targeted marketing campaigns.

Trend analysis

Businesses need to stay ahead of the curve to remain competitive. Unstructured data, including news articles, blog posts, and social media chatter, can be used to identify emerging trends, industry shifts, and competitor strategies. This information allows companies to stay informed and adapt to changing markets.

Pros and cons of unstructured data

Pros

• Cost and time savings - A properly implemented unstructured data pipeline can automate tasks that previously required large amounts of manual effort from employees.
• Discover hidden opportunities- By tapping into unstructured data a business can potentially find hidden insight into their market or customers which can lead to more revenue.
• Improved security - Unstructured data analysis can improve security by finding correlations in behavior for fraud or application security based on previous incidents.

Cons

• Implementation costs - Storing and processing unstructured data at scale to generate insights can be expensive due to the cost of hiring needed to acquire the skills needed to implement. The services and hardware required to run an unstructured data analysis program can also be expensive.
• Privacy concerns - Unstructured data may contain sensitive data that is regulated or needs to meet compliance standards. If not done properly this could lead to legal issues or PR problems for a business.
• Data quality - Ensuring data is an accurate representation of the real world is a challenge and if not done properly can result in inaccurate decisions being made off a sample of data that doesn’t reflect reality.

Frequently Asked Questions

Structured data vs. unstructured data?

Structured and unstructured data are two totally different formats of data. Structured data is orderly with a clearly defined model that helps organize it, while unstructured data lacks any particular pattern or structure.

An example of where structured data is superior to unstructured data is when doing customer analysis. Structured information like past purchase history, website visits, demographic details, etc., can be easily collated and stored in a relational database, making it easier to query and get meaningful results from the raw data. Unstructured customer feedback such as emails to customer service representatives or comments on social media posts would need more time and effort to analyze.

What is semi-structured data?

Semi-structured data is a type of data that has some elements of structure, but also allows for flexibility in its format. Unlike structured data which is rigid and organized in a pre-defined way, semi-structured data can be used to capture both structured and unstructured information.

An example of semi-structured data would be customer reviews from online sources such as Yelp or Amazon. These reviews contain elements of structure, such as the user’s name and rating, but also allow for unstructured commentary about their experiences with a product or service. Companies can use this semi-structured data to analyze customer sentiment and get a better understanding of how customers view their products.

Semi-structured data can also be found in social media posts, where users may discuss a topic or product without following any particular format. Companies can then use this unstructured information to gain insight into public opinion and sentiment related to their products or services.

Semi-structured data is becoming increasingly popular as companies realize the value in being able to quickly and easily access customer feedback. It provides a valuable resource for companies looking to understand their customers better, and is likely to become even more important in the future.