Artificial Intelligence (AI) is transforming industries by automating tasks and generating insights, but its true effectiveness depends on high-quality, relevant data. Structured data is the most critical data type to maximise AI's benefits.  

Structured vs. Unstructured Data 

Data is structured or unstructured depending on the format and schema it is based upon. A schema describes the organisation and storage of data in a database and defines the relationship between various tables.  

Structured data has a fixed schema, sorted into table rows and columns, such as name, address, identification number, date, and so on. Since structured data has a standardised and defined format, data analytics tools, machine learning algorithms, and users are all able to interpret and use it consistently. However, retaining data in this structured form is more rigid based on the level of effort to maintain it. 

Unstructured data has no fixed schema or predefined format. It lives across emails, social media comments, audio files, chat transcripts, or other documents in different repositories, and it is difficult to parse and analyze. Since it is not in a structured universal tabular format, unstructured data is much more flexible. However, the majority of data is unstructured, and it exists across an enterprise since it can be collected so quickly and easily.

Table 1: Structured vs. Unstructured Data  

The Future of Data in an AI-First World 

Despite the rise of unstructured data and advancements in AI's ability to process it, structured data is poised to play an increasingly important role in the future of legal AI as it is built in and leveraged within Contracts Lifecycle Management (CLM) and Data Management Systems (DMS): 

• Clarity and Consistency: Structured data's inherent clarity, consistency, and efficiency make it a critical foundation for building reliable and scalable AI systems. 

• Knowledge Graphs: Structured data exhibits a synergy with knowledge graphs, enhancing AI's ability to interpret context and provide more accurate and relevant responses. 

• Machine Learning Models: Structured data is crucial for training many types of machine learning models, particularly for tasks like classification, regression, and prediction. 

• Integration with Unstructured Data: There is a growing trend towards integrating structured and unstructured data to create more comprehensive and insightful AI applications. 

Table 2: Benefits of Structured Data for AI 

Embracing Structure for AI Success 

Despite its advantages, structured data presents challenges and drawbacks that organisations must address to harness its value for AI. 

Key Challenges: 

• Inflexibility: Adapting to evolving data requirements and capturing complex data types can be difficult without significant changes to the underlying schema. 

• Resource Intensity: Maintaining and scaling the infrastructure for structured data can be resource-intensive, with long-term costs associated with managing these environments. 

• Data Silos: Structured data can be fragmented across different systems and departments, leading to data silos that hinder a holistic view for effective AI applications. 

• Real-World Data Fit: Not all real-world data fits naturally into a structured format, potentially leading to the loss of valuable information or inefficient modeling. Hybrid approaches may be necessary. 

• Predefined Data Models: The rigid nature of structured data can make it difficult to adapt to new business needs without substantial adjustments. 

Data governance is crucial for ensuring data quality, consistency, and security, which forms the foundation for reliable AI applications. Organisations must consider the cost of structuring, storing, and maintaining structured data for AI. Planning a data model with an understanding of these challenges will help ensure that your organisation can be flexible and ready to evolve with ever-changing AI tools.  


Karthik Radhakrishnan is the Director of CLM R&D and Chief Architect at Epiq. He is an experienced Technology Leader with over 35 years of IT experience, specialising in the conception, development, and management of software applications. With over 20 years of expertise in Contract Lifecycle Management (CLM) and Configure, Price, Quote (CPQ) domains, he consistently delivers high-quality solutions to clients.