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Contract Analytics: Unlocking Hidden Insights 

Contracts contain a world of critical data that organizations can leverage to make strategic decisions–including rights, obligations, restrictions, risks, and financial value, to name a few. 

On average, however, according to World Commerce and Contracting, more than 9% of a company’s annual revenue is lost due to inefficient and ineffective handling of contracts throughout their lifecycle.  

Hence, contract analytics–turning contract data into business intelligence leveraging technology and techniques such as artificial intelligence (AI), machine learning, and data mining–is an essential part of the contract lifecycle management process.  

The promises of contracts analytics as part of a broader contract management strategy are many; and when the limitations and opportunities are understood, legal teams, contract managers, and their business unit counterparts can realize the full potential of this powerful tool.   

Benefits of contract analytics  

By surfacing data-driven insights to the business, contract analytics supports informed decision-making, such as contract renewals, terminations, or renegotiation. Moreover, it can help organizations:  

  • Improve efficiency by automating manual tasks associated with contract analysis;  
  • Accelerate deal cycles for contract negotiations and approvals;  
  • Identify and mitigate potential risks, such as indemnification clauses, liability limits, and unfavorable termination clauses, and help legal assess the financial stability and performance history of vendors;    
  • Reduce costs by finding cost-saving opportunities through contract renegotiation and renewals, supplier optimization, and improved pricing strategies;  
  • Optimize revenue by identifying underutilized assets or missed revenue streams, for example; and,   
  • Enhance regulatory compliance by identifying clauses that may trigger compliance obligations.    

Contract analytics tools are also a ripe use case for AI, helping legal professionals and contract managers find contracts faster and gain information (such as liability clauses, milestones, and the like) across contracts, rather than retrieving and reviewing contracts one by one. Further, sophisticated natural language processing (NLP) processes can help users better understand the nuances of legal language and extract more precise information. 

 
Despite the benefits of contract analytics, including the promises of AI integration, there are challenges that limit its full potential.     

Limitations in using contract analytics tools: the data   

The contract data itself can limit the efficacy of analytics tools. Today, the majority of contracts are in unstructured form, which makes complex data mining and analysis difficult. This is due to the variability and complexity of legal language, nested clauses, and nuances that make it difficult for even the most advanced machines to accurately extract and interpret information. To illustrate this point, a limitation of liability clause could be written in many different ways, and isolating concepts would be difficult for a single contract, not to mention across thousands of contracts and data points.  

Data quality issues and numerous contracts, work orders, and amendments associated with a single supplier or customer, for example, further lead to suboptimal contract analysis results. (Data hygiene and roll-ups or governing summaries address these issues.)  

In short, unstructured contracts can conceal valuable insights even with the most advanced AI and analytics tools, with many adopters finding that deployments have not met expectations or have failed altogether.    

Unlocking contract analytics insights across the enterprise  

To address the inherent challenges of unstructured contracts, the trend is shifting towards more structured contracts as a way to optimize their information value. To move from unstructured to structured data formats, experts extract data and store it in the correct locations, and in a consistent format, for software to easily analyze vast amounts of data without burdensome manual review.  

Built on a contract data model with thousands of structured contract positions, and applied to both new and legacy agreements, the data output allows organizations to transform their contracts into usable data that can be stored in their contract management system. This gives companies full insight into that data across their contract portfolios that go beyond basic contract data points such as counterparty name, effective date, and expiration date.  

Moreover, organizations can integrate structured data with other enterprise systems, empowering business line users to make better data-decision decisions. For example, a sales team could use contract insights to identify upselling and cross-selling opportunities or analyze historical contract data to forecast future revenue trends. A finance team can use contract data to forecast future revenue and expenses, analyze contract terms to identify potential cash flow impacts, and identify financial risks such as counterparty risk and foreign exchange risk.  

Once data is structured, the opportunities to leverage contract insights are nearly endless.  

Elevating contract analytics with data  

Contract analytics is a powerful tool that legal teams should not leave on the table. By making the contract data contract analytics “ready,” legal teams can streamline processes, enhance efficiency, and make more informed decisions by understanding potential risks, cost savings, and future revenue opportunities.  

UnitedLex helps organizations surface critical and granular insights by converting complex contract data into simple and easy-to-use business intelligence. Learn more about our Contract Solutions.    

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