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The challenge
- Clean deals dataset and merge with timecard-transaction data
- Investigate and test current hypotheses about the effects of M&A deal variables on legal work
- Generate insight on the legal work of future deals given certain variables
How did we help?
The team received anonymised timecard-transaction data and deals data, which recorded characteristics of historic deals. The timecard data was rolled-up to a deal level with key aggregated variables from an assortment of 28 tables using SSMS. The deals data was then parsed and cleaned using a Python data pipeline before being merged with the timecard dataset.
After creating the working dataset, the team investigated the effects of deal characteristics on multiple target variables using a series of visualisations and test-models. These target variables were related to the volume, intensity and type of legal work completed on M&A transactions. Two interactive visualisations created in R were also used to quickly display combinations of effects against the target variable.
Some of the quantified effects stood in line with legal intuition and other effects were initially surprising but later explained. In all instances, intuition and feeling were quantified, which opened up interesting discussions on estimating the required legal work for future M&A deals.
Key facts & findings
Merged and cleaned two distinct databases into a working signal-rich dataset
Engineered 1,000,000 timecards into principal deal variables
Identified key drivers of the volume, intensity and type of legal work
Estimated effects of drivers on future legal work
Developed two interactive visualisation tools for east data insights and discovery
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Team members: Otto Godwin (team lead), 鬚ana Krstievi, Mingyang Tham, Mark OShea, Wenyu Guo
Author: Otto Godwin
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