Upgrading your data extraction technology is tough and comes with lots of questions. If you have a CLM system or are preparing to buy one, have you analyzed the importance of the information hidden in your legacy contracts lying in cabinets or shared drives for your new CLM system?
In this guide, we’ll cover some of the very pertinent questions:
- What are legacy contracts and why they are important?
- Why do legacy contracts need to be migrated?
- What are the challenges with contract migration?
- Approach to legacy contract migration
What are legacy contracts?
Legacy data is the data from your legacy or older contracts. These are the contracts that have been executed before today. They may be current and still relevant or may not be i.e., expired.
These contracts often go missing or are hard to find after signing, as they tend to be stored in different formats and locations like drawers or cabinets, file-sharing folders or drives, document repositories, or management systems. These can still be the active contracts that can play deciding role in the growth of an organization.
Are Legacy Contracts still important?
One of the biggest questions that companies face during a CLM initiative is what to do with their legacy contracts. The challenge is that often these agreements are poorly organized and the information within them is either forgotten or is considered irrelevant to any decisions made in today’s business.
However, legacy contracts may contain much more impact than many anticipate. For example, often obligations within these agreements extend past the expiration date and may have monetary windfalls if they are not tracked properly. Legacy contracts can also contain information about previously negotiated terms which can give the company leverage in future negotiations. It, therefore, becomes crucial to bring legacy contracts into the present when going through a CLM initiative.
This does not mean that every contract needs to be tracked and extracted from. A best practice is to sort the contracts first to define which ones really need to be migrated and which ones can be left behind. There are numerous ways to go about this process a couple of which are listed below:
- Date of Expiration: If a contract is over a certain number of years past its expiration, you can be confident in not having to bring it over.
- Focus on Highest Spend: Most businesses should be focused on their biggest customers. As such, focus primarily on the highest spend clients first and then expand to the rest afterward.
There are many approaches to this challenge and it is important to find the best one for each situation individually.
Why do legacy contracts need to be migrated to CLM?
Do you have a CLM system but are not able to generate the expected ROI from it?
Whenever a such situation arises, you must go back and re-evaluate the most important question if all your contracts (active as well as legacy contracts) are migrated into your new CLM tool. because you cannot manage and analyze contracts that are not visible.
It is really important to use historical data in reporting to make accurate forecasts and predict events properly. If you started capturing data from the day onwards you installed your CLM system you’ll lose the track of your historical data resulting in incomplete or inadequate reporting.
You might also lose a track of some of your still active contracts like one with the evergreen clause. So, for the above reasons, it is crucial to migrate your legacy contracts and track their metadata on your new CLM system
Having a CLM system is not just the end, capturing information from all of your contracts is the solution. Migrating legacy contracts and data into a searchable database is essential to stay on top of your obligations.
Having legacy contracts on a structured as well as a searchable database, like a CLM system, has advantages such as:
What are the challenges with contract data migration?
- Unstructured Data: In absence of a centralized repository there is a chance that data has multiple versions i.e duplicates, and email versions. This data needs to be cleaned before you move to a new platform to avoid messy or unorganized data.
- Working in silos: To have an effective rollout of a contract data migration project, all teams need to be on the deck which means data silos need to be broken down.
- Duplicate records: Having thousands of contracts with a single client, you may have several files with similar naming systems. You may also have multiple copies of each contract due to amendments that the contract went through before approval.
- Incompatibility: Data formats of the old and new systems don’t match up while moving from one system to another that uses a different database structure or when data is imported into a new system that uses a different file format. (such as OCR files)
According to MIT, data-driven decision-making for businesses can increase their productivity by at least 6%. Yet, barely 0.5% of the world’s business data is used and analyzed properly.
Approach for legacy contract migration
Whether you are searching or looking for your first CLM system and going through the selection process, or you have already determined who it is that you are going to move forward with the journey that you’re about to start; some of you might even have a CLM system and be struggling with adoption and getting that success that you were hoping for, or you may even be struggling with the idea that you deployed your CLM system, but you haven’t gotten your current and, legacy data into the system yet. There could be different scenarios with different requirements with one goal data-driven decision.
To effectively and flawlessly execute your contract migration process, here are a few things to consider:
Step 1: Decide which contracts you want to migrate and whether or not all the documents need to be in the new CLM. Typically, PDFs only are signed documents. Can JPGs, MSG files, Doc and XLS files, etc. be ignored?
Step 2: Decide what data points you want to report on across a set of these legacy contracts.
Step 3: Establish extraction guidelines, including exception handling, for each of the data points.
Step 4: Set up your CLM for ingestion of these attributes using the same rules.
Step 5: Ingest smaller portions for testing to ensure bulk ingestion goes smoothly.
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