The Crucial Link in Contract Lifecycle Management
The value of Contract Lifecycle Management (CLM) solutions is primarily based on their ability to standardize the contract authoring process through clause and contract templates and self-service wizards, and guide new contracts through a standard workflow through to signature and execution. While many CLM solutions have been successful in delivering these process oriented goals, a number of critical functional requirements needed to meet everyday challenges in contract management still remain unaddressed.
The challenge is that many organizations have tens of thousands of active (legacy) contracts that existed prior to a CLM implementation and likely reside across any number of file share drives, document management systems, personal computers and other types of “make-shift” repositories. This is sometimes due to nature of contracting, where the Procurement team has its requirements and solutions for contracting, the Sales team has theirs, Facilities has theirs, and the Legal team uses their own processes for managing various types of legal agreements. Add some M&A into the mix, where thousands of new contacts may be coming into an organization and the problem is magnified. Each legacy contract likely contains more exposed risk due to the typical ad hoc creation and negotiation process than any new contract being created through a new CLM system.
Each legacy contract likely contains more exposed risk due to the typical ad hoc creation and negotiation process than any new contract being created through a new CLM system
When implementing CLM, most organizations will import what they deem to be the “most important” of these legacy contracts with a minimal set of relevant data points. Given CLM systems are typically implemented on a departmental basis, only the contracts belonging to that department will be imported. It quickly becomes clear that CLM solutions are simply not designed to migrate legacy contracts at the volume and breadth that organizations require, nor migrate the contract language and commercial terms at the level of granularity they need, to add value in the area of contract intelligence. New contracts benefit from CLM, but the legacy ones remain a massive headache requiring expensive and time consuming manual reviews.
If a company has embarked on a manual review initiative in an effort to import legacy contracts into a new CLM, the contract information is fixed from that point forward, leaving no way to identify other contract terms that remain as unstructured language within these contracts. There is no way to “future proof” contract data extraction nor predict new regulations. When that happens, all of those legacy contracts must be manually reviewed again, and that is a big investment in money and time.
Contract Discovery and Analytics Software:
Traditional CLM solutions are replaced by new type of software called “Contract Discovery and Analytics,” performs many key functions that can crawl on a network to find contracts across a variety of repositories, file servers, and network drives. It can automate the extraction of key contractual terms and clauses through a set of rules and policies, and allow users to review and compare terms. It can also provide the ability to create custom searches, and also import data sets into other business systems such as Customer Relationship Management (CRM), Procurement Systems and Contract Lifecycle Management (CLM).
Contract Discovery and Analytics can be an organization’s “Google for contracts.” It can also help a CLM get off the ground with a relevant set of contract metadata, but when something in the business or regulatory landscape changes. This software is much faster and more cost effective solution than manual contract reviews. It can extract metadata such as:
• Data pertaining to revenue and commercial terms: This includes volume-based discounts (potentially earned or offered), fixed renewal pricing, annual uplift allowances, Most Favored Nations (MFN) pricing, auto-renewal clauses, and minimum threshold requirements.
• Data to support regulatory mandates: This includes rev rec data for IFRS 15, leasing data for IFRS 16, financial position and collateral data for QFC and SR 14-1 reporting in banking, and the list goes on.
• Business “negative events:” This incudes data breach obligations for notifications (how and when), litigation events, warranty issues, etc.
• Merger, acquisition and divestment activities: This includes due diligence assessments looking for risk and exposure in the contracts of a target company, post-transaction integration work, or knowing how to assign which contracts to which entity in a divestiture.
A focus of the newest generation of Contract Discovery and Analytics software is putting more power in the hands of business users. Instead of being used by data scientists or highly-trained pros, it can now be “behind the scenes,” running in the background while a business user is reviewing contracts in their familiar Microsoft Word® interface. They open up contracts in Word, and the technology will highlight which areas of contracts have standard and approved language, and which areas do not, increasing user efficiency and the value of these kinds of technologies. The information from this work allows organizations to make better business decisions, resulting in a rapid and accurate assessment of which contracts need to be internalized and renegotiated, repapered, purchased, or something else.
How Does It Work?
To make contract discovery and analysis work accurately and efficiently, there are several technologies that can be used together for accuracy and speed in the extraction of data. This includes Natural Language Processing (NLP), which uses complex rules sets and statistical analysis for the identification of terms and provisions in the discovery and initial data extraction, and Machine Learning (ML).
The system learns through examples, processing multiple contracts over time and receiving feedback on how it identifies various components of a contract. As a consequence, the system becomes more effective and increasingly precise in finding what it is seeking. These systems don’t just “find,” using keyword search or text matching, but they “think,” using combinations of words and phrases to deduce concepts and gist, and then they are taught if they are right or wrong and that information goes into the learning aspect. It takes the right combinations of NLP, ML; the right set of algorithms to make these types of systems work accurately at very large scale. This is where the idea of intelligent machines emerges.
Important to all of this is the data visualization platform which will present information intuitively in order for a system to work properly. The data visualization can tell a business user important pieces of information quickly and intuitively, such as how many contracts “have both UK governing law and auto-renewal clauses,” or “how many have non-standard liability indemnifications and expire within 30 days.”
CLMs serve an important purpose to remove cost and time from the creation and management of contracts. But, they are not designed to help organizations really understand the data buried within those contracts. Contract Discovery and Analytics ensures comprehensive insight to potential risk as well as revenue and cost opportunities across ALL contracts, now and in the future. It accelerates access to the insights locked within the language of an organization’s unstructured contractual documents,and empowers them to take informed action, more quickly and cost effectively.