To realize the benefits which exist after the contract has been signed, it’s necessary to not only understand the terms and provisions contained in your contract but to capture these for ongoing management.
When a contract is signed, most organizations do a good job of tracking the contract’s basic operational requirements (e.g. renewal dates), but the same can’t be said for a contract’s legal components (e.g. indemnification). In addition, the attributes organizations tracked years ago may not be what they need to know today.
As contracts carry crucial information with legal and regulatory bindings, any missing or incorrect information can trigger penalties and other legal consequences. Failing in capturing a payment date can attract more interest on payment or even a termination, or an incorrect renewal date can result in a loss of revenue.
Contracts are all structured and worded differently. Attributes can be phrased in different ways, there can be ambiguities, and data can be presented in various forms. Most contracts are also in text form that is non-computer searchable, so the information needs to be put into a format that a computer can read. You might think of software to do that, or re-entering all the data points manually. But software alone cannot parse out all the essential attributes and their nuances.
Though extraction technology has come a long way, no machine can get 100% of the data accurately. Typically, you can expect 40–70% of the data to be abstracted through automation. In this regard, it certainly does the heavy lifting, but the results are less than stellar and insufficient to populate your CLM. Even when software claims a high level of accuracy, the extracted data may not always be relevant or usable when actually reviewed. And even the best software may require multiple passes through the text to identify the relevant information.
How do you get accurate and comprehensive results from extraction?
To get a comprehensive attribute set for your contract lifecycle management system, you need human quality oversight at a professional level. Someone has to augment the software outputs with logical interpretations. This is needed at every step in the process, as there are multiple stages where ambiguities, errors, or omissions can affect the final results.
The best solution is therefore to combine technology with human intelligence. Advanced extraction engines, with the help of algorithms and logic, can do the heavy lifting, while legal professionals can vet the results for accuracy and provide the context and interpretation that software alone may miss. Combining software with professional quality oversight enables ambiguity correction, repair of errors (misread and otherwise), omission remedy, and validation of the extracted information.
Accuracy and comprehensiveness are critical because even one attribute you miss could be the most critical one triggering a missed opportunity, incurring a penalty, or causing a deadline to be missed. The risk to your business is just too great for the outcome not to be as comprehensive and accurate as possible.