Data valuation is the process of assigning value to datasets. Data valuation is the first step towards data monetization – you cannot monetize data unless you have valued the data.
In this case study, we use publicly available information to value employee data using LinkedIn and Workday as examples.
In January 2016, Microsoft acquired LinkedIn Corporation in a transaction valued at $26,200M. At the time, LinkedIn’s professional network had 433 million members worldwide. Based on this transaction, the implied value per member record was $60.51 ($26,200M / 433M).
According to an analysis done by Comparitech of public companies experiencing a data breach, share prices of those companies grew by an average 8.38% over one year but underperformed the NASDAQ by 6.49% during that period. The probability of such as data breach is low, but we can use these figures to calculate the impact of a potential data breach on the value of employee data records at Workday, Inc.
On October 29, 2020, Workday’s market value was $51,000M, with a reported 42 million users as of the prior December, yielding a market value per user of approximately $1,214. Applying the 6.49% underperformance figure, we can calculate that the potential impact of a data breach for Workday would be $78.79 per end-user data record ($1,214 x 0.0649).
As these examples demonstrate, quantifying the value of datasets through data valuation can provide insights and value to the business.