Imagine yourself in the shoes of the commercial head at an aviation company. It's 2022 and revenge travel is pushing demand sky-high. So, you are aggressively scouting for capacity expansion opportunities. Your organization has hundreds / thousands of contracts in the form of aircraft leases, maintenance agreements, repair contracts, employment agreements, etc. Given the nature of the industry, any new contracts that you sign will have implications and dependencies on your existing contracts. To negotiate new contracts quickly and effectively, you need a clear view of connections, inter-relationships, warranties, trigger clauses, and many other risks and opportunities in your existing contracts. That typically means hundreds of questions that your contracts team has to answer, at a short notice.
Do you have that nagging feeling that your contracts are a goldmine of useful business insights but they are are too hard to extract or query?
Well, you’re not alone. Typically, contracts are not a high priority when it comes to digitization. A WCC (World Commerce & Contracting) survey shows that on average 9% of the contract value is leaked during the lifetime of a deal. We suspect a large part of that is due to a lack of insights into existing contracts. And that is a result of the structure and language of contracts. They do not lend themselves to be easily queried, combined with other pieces of data, or sliced/diced. This article in FT articulates the issues which legacy contract language causes for companies, especially in a volatile, uncertain business environment environment.
Unless, of course, organizations digitize their contracts.
Digitizing contract data is the process of extracting key concepts, commercial terms, definitions, legal clauses, risk factors, obligations, and relationships amongst all the information, from a single contract (or a set of contracts), and inserting these into a structured database.
[The topic of digitizing contract data is often confused with – and overshadowed – by digital transformation of contract management. But let’s make this distinction before we proceed.
Digitization of contract data is the process of using Machine Learning to extract and deliver structured data from each contract.
Digitalizing contract management refers to automating and streamlining the contracting workflow and process to make it smoother and faster. This is often done using a combination of authoring, communication, and approval tools.]
Structured contract data is the foundation of effective contract data management. Contract data has remained untapped because its value is obscured by dense prose that makes any analysis or insight generation almost impossible. Here’s why digitizing contracts needs to become a core business focus.
Legalese, or the formal and technical language most contracts use, is characterized by dense prose at best, or obfuscation at worst. For contracts to have business value or to protect you from risk, they need to be converted to data – preferably structured data – that you can use. However, relying on legal experts to interpret and analyze contracts each time you need them is hardly an elegant or scalable solution – especially when you consider the number of contracts to be analyzed and reviewed.
In the life of a contract, reviewing it is hardly a one-time activity. You need to assign resources for every contract you renew, and sign. This is both tedious and repetitive, and it’s safe to assume that it is not a favorite when it comes to employees’ task lists.
Too often, reviewing and analyzing contracts are looked at as isolated activities. But the key to creating better contracts or negotiating better terms lies in the ability to zoom out and connect the dots to see the bigger picture.
ContractKen process to build best in class contract data capability for its customers
Because most businesses have contracts spread across multiple systems and even entities, the first step is to create a central contract repository. Documents proliferate across contract management tools, shared drives or hard drives, and even locked file cabinets as organizations grow. Whether these contracts are non-disclosure agreements (NDAs) related to a potential M&A deal, client service agreements, senior leadership employment contracts, or multitude of 3rd party vendor agreements, getting a handle on contract positions begins with collecting them all in one location.
Next step is to establish a connection with the source systems (or individuals) having contract documents. These days almost all document management systems allow for API based document extraction. After this comes the very important step of manually sifting through the contract documents (sometimes a sample) to ensure duplicate copies (with different names) or unsigned copies of contracts do not find their way into the central repository. This typically requires a combination of technical and manual work.
Modern contract repositories consist of a document database (a NoSQL database like MongoDB or AWS DynamoDB) connected to a web based user interface. All your documents (in their original formats - i.e. docx, txt, pdf containing text, pdf containing scanned images, png, jpg, etc.) are stored in this database. An intelligent contract repository should have these capabilities:
Discover how you could build an Intelligent Contract Repository without spending 6 months or thousands of $
Once you have built a clean and organized corpus of contracts, next step (to be performed on a one-by-one basis on each contract) is to use a combination of state-of-the-art ML algorithms, contracts expertise and proprietary contracts ontology to extract legal clauses, commercial terms, etc. and organize into a structured data model as:
Contract Text --> Key Concepts --> Sub-Concepts
Here is an illustration of how ContractKen tech achieves this for the key concept ‘Termination’ clause:
This shows how a ‘Termination’ clause is broken down into various components such as
These components are further analyzed and converted into ‘lower dimensional’ data elements like
.. and so on, depending on the key concept being broken down.
These individual data elements extracted can then be populated into a structured database and compared against ‘preferred’ positions for the organizations.
Talk to ContractKen to understand how you can digitize your contracts data
Once you've converted contract text into structured data, the real analysis, at scale, can start. You can isolate risk and spot opportunities across the entire portfolio. Contract digitization can help you take a portfolio view of your contracts and answer questions like:
Take a look at our detailed post on Contract Analytics to understand how you can drive value through business insights generated from structured contract data.
Other key benefits include:
We firmly believe companies which are able to extract structured Contract Data from documents and combine it intelligently with other sources of truths (sales data, customer data, employee data, products data, etc) in an organization will win in the marketplace.
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