Unstructured Data

What is unstructured data?

Unstructured data is information like text, video, or audio that doesn’t have a predefined format or schema. Unstructured data is typically human-generated, but it can also be generated by machines. Regardless of its origin, unstructured data doesn’t fit a preset data model or schema, and therefore can’t be stored in a traditional relational database management system (RDBMS).

Most of the data that organizations generate and collect is unstructured data. This data contains crucial insights for making informed business decisions, but because the data lacks structure, organizations typically need to use advanced techniques to analyze it. To address this challenge, businesses are turning to artificial intelligence (AI) and machine learning (ML) tools to help power their analytics applications.

This page will cover:

• Unstructured data vs. structured data
• Examples of unstructured data
• Unstructured data use cases
• Pros and cons of unstructured data
• How to analyze unstructured data
• Unstructured data tools
• Conclusion

Unstructured data vs. structured data

Unstructured and structured data have distinct differences, including the types of analysis you can use the data for, the schema used to organize the data, the data format, and how the data is stored.

Structured data is usually stored in a relational database where it can be easily mapped into designated fields. For example, customers can be identified by consistent details such as phone numbers and addresses. Information is categorized in a rigid format, ensuring consistency that makes the data easier for both humans and algorithms to search, process, and analyze. To effectively search data in relational databases, database administrators often use structured query language (SQL).

Unstructured data, on the other hand, can’t be stored in a traditional relational database because it lacks a consistent internal structure. This lack of structure provides the advantage of flexibility, but makes datasets more difficult to search, process, and analyze.

Examples of unstructured data

Examples of human-generated unstructured data include texts, emails, social media, documents, webpages, photos, audio files, video, and much more.

Machine-generated unstructured data can consist of log files from websites, servers, networks, and applications. It can also include satellite imagery, surveillance footage, and sensor data from IoT-connected devices.

Unstructured data use cases

• Business intelligence: Insights for better business decisions
• Customer analytics: Using data to better understand and service customers
• Communications analysis: To ensure regulatory compliance
• Social media tracking: Analyze conversation and interaction patterns
• Predictive maintenance: Manufacturers use sensors to detect potential failures

Pros and cons of unstructured data

Unstructured data has noticeable advantages and disadvantages regarding flexibility, business insights, and working with datasets.

Pros

• Flexible: You can maintain datasets in different formats that aren’t uniform.
• Insightful: Data-driven decisions yield better and more predictable business outcomes.
• Abundant: Unstructured data comprises the majority of business-generated data.

Cons

• Difficult to search, process, and analyze: Lack of uniformity is challenging.
• Resource intensive: Effectively managing, maintaining, and using massive volumes of unstructured data can be nearly impossible.
• Difficult to share: Collaborating effectively on large datasets is complex and requires significant investment.

How to analyze unstructured data

Various tools and techniques for analyzing unstructured data include:

• Data mining: This process involves techniques like data cleaning, classification, clustering, and visualization to uncover patterns and relationships within unstructured data. Once you organize the data, it’s easier to interpret and act on.
• Machine learning: ML is good for unstructured data analysis because it can analyze large datasets. First, the data must be transformed into a specific format for ML algorithms, then methods like text classification, clustering, natural language processing (NLP), and deep learning are used for analysis.
• Predictive analytics: After you convert unstructured data into structured data, you can use predictive models like regression, decision trees, or neural networks for forecasting. The insights gained from predictive models help an organization make decisions and plan for the future.
• Sentiment analysis: This involves cleaning and tokenizing unstructured text, then using sentiment analysis methods (lexicon-based or ML) to determine if the sentiment of the text is positive, negative, or neutral. This data is used to better understand the customer experience and make decisions accordingly.
• Natural language processing: NLP uses methods like tokenization, lemmatization, stop words removal, and topic modeling to process data. Using NLP for unstructured data analysis is especially useful in healthcare, finance, and marketing.

Unstructured data tools

• Couchbase: A distributed database that supports both key-value and document data models.
• MongoDB™: A document-oriented database that stores data in JSON-like documents.
• Apache Cassandra: A distributed database that stores data in a column-family format.
• Redis: A key-value store you can use as a database, cache, and message broker.
• Amazon DynamoDB: A managed NoSQL database service provided by Amazon Web Services (AWS).
• Neo4j: A graph database that stores data in nodes and edges.

Conclusion

Overall, unstructured data makes up the majority of all data generated and collected by organizations, and it provides a significant opportunity to improve business decision-making. Organizations must have the proper platform and tools to maximize this opportunity.

Non-relational databases, or NoSQL databases, are becoming increasingly popular due to their ability to handle unstructured or semi-structured data. They use a variety of data models to accommodate diverse data types and structures, making them well-suited for handling large, complex datasets that may evolve.