DATA-DRIVEN CONTRACTS

March 8, 2013

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Contracts.

Perception—

•Require expert analysis and review.

•Are considered difficult and complex.

•Are perceived as unique and nuanced documents.

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Contracts.

Reality—

•Follow strong conventions.

•Can be reduced to useful information without much effort.

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Patterns.

We’re looking for patterns across a body of contracts.

We can only see patterns if:

•We know what to look for, and

•We’re collecting the right kind of data.

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What kind of data?

Contract Summary Data.

•Deal Terms. Highlights of contract rights and risks.

•Deal Process. Who worked on it. How much it cost us to close. How long it took.

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Objectives.

• Reduce duplication of efforts.

• Enable collaboration.

• Spend less on contract review.

• Answer questions about our contracts more quickly, and with more accuracy.

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Capturing good data.

• Consistency is the measure for success.

• We want structure in our summary data that lends itself to generating useful reports.

• We want to develop models that represent our contract activity.

• We want our data to drive our contracting standards.

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1. Documents.

A readable copy of the current contract files. A plain-text extraction should be indexed for search. If possible, keep first and final drafts.

What to capture.

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2. Parties.

Identities of key business and legal stakeholders, including the company or companies on the other side of the table. Naming conventions are critical here.

What to capture.

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3. Type of Contract.

Classification of contract type, e.g., Software License, Hosted Service, Data Subscription, Lease, Professional Services, etc.

What to capture.

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4. Confidentiality Risk.

Is confidential information being exchanged? What standards apply to safeguarding this information? What is our worst-case outcome?

What to capture.

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5. Privacy Risk.

Is personal information being provided? Handling of personal information requires heightened scrutiny.

What to capture.

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6. Business Risk.

Will the company incur business losses if the contract is breached? Is reputational damage likely to arise from a contract breach?

What to capture.

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7. Liability.

What is our exposure for other people’s failures? Are there caps or limits on liability? Is the liability exposure symmetric?

What to capture.

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8. Visibility.

What is the contract’s impact on our customers? Is management watching this contract carefully?

What to capture.

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9. Spend

How much do we expect to spend over the contract’s life? Includes recurring and one-time charges.

What to capture.

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10. Turnaround.

How long did the deal take from start to close? Actual time spent vs. start/finish times.

What to capture.

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1. Documents.2. Parties.3. Type of Contract.4. Confidentiality Risk.5. Privacy Risk.6. Business Risk.7. Liability.8. Visibility.9. Spend.10. Turnaround.

What to capture.

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• Data can uncover some impolitic insights.

• Not everyone welcomes efficiency.

• Strong incentives to retain the status quo.

• Contract review is a cost center.

Hard Truths.

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• Most of this data already exists.

• Existing data has unrealized value.

• Technology infrastructure is no longer a barrier.

• The business value of the current legal review process can be tested.

Silver Linings.

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Open source web server for capturing the data outlined in this presentation.

Feel free to use as an example, or as a starting point for a more sophisticated solution.

Under MIT license.

https://github.com/sirvine/open-contract-intake

Open Contract Intake.

YUSON&IRVINE

Sol Irvinesol@yusonirvine.com

@solirvine

71 Nassau Street 3CNew York, NY 10038

www.yusonirvine.com@yusonirvine