Stout was engaged to monetize connected car data by selling various data sets to third parties. We built a statistical model that assisted the client in assigning an economic value to select data based on certain parameters, such as age of the information, frequency of requests, type of data, and total volume of data set requested, among others. Additionally, data can be provided in real time or in batch/historical mode. The client was very pleased with our model, and it was decided to present our results to the C-suite as the blueprint for their data-monetization strategy. The engagement was especially difficult due to the absence of publicly available data sets that normally serve as the foundation for creating a data valuation model.

We developed a statistical model and a data valuation framework and delivered a detailed template where the model coefficients would change based on choosing different input parameters. We completed several tasks, such as:

• Researched pricing data available from a few other automotive companies and third parties
• Applied feature extraction methods to determine the importance of the input parameters with regard to the pricing data
• Calculated the weight/importance of every input variable, such as age of the information, frequency of requests, type of data, total volume of data set requested, and real-time/batch mode, among others
• Ran sensitivity analysis, where acceptable numerical ranges were provided for input parameters
• Applied and compared several statistical methods and chose the best fit to the existing data