Automotive Telematics Case Study
Client Data Profile
• Nasdaq listed company in the Automotive Telematics sector.
• Client has positioned itself as a data, analytics and software-as-a-service provider that analyses connected and electric vehicle data to create real-time insights.
• The business model is B2B for fields of use (FOU) of telematics data, insurance data, vehicle diagnostics data as well as subscriptions to global car manufacturers, local governments, government authorities etc.
Objectives
• The client sought a data valuation to reinforce the financial strength of the business model.
• To provide a value for off balance sheet data assets in order to leverage their data assets as collateral within a financing transaction.
• The client intended to receive funding of $100 million for recapitalization and utilise the data valuation as quantifiable evidence of data driven success.
• The client additionally sought to understand the use of the data valuation in relation to data funding and leverage for business growth.
Our approach
Day 0-5
Framing Activities
• DVP considered the data value to be directly proportional to its impact, where impact is created from the transformation of data from descriptive to prescriptive
• DVP completed a thorough analysis of data repositories and with an in-depth review of financial information, historic and future value • Analysis of data operations, acquisition, licensing, subscription, cost improvement and more were considered in the data valuation
• Checking essential assumptions on data ownership of the data
Day 6-15
Collateral Activities
• Select mathematically driven valuation methodology based on prior information, data analysis, ownership of data repository and themes
• Consideration of replacement value, acquisition, and consumption of data via clustered use cases
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Day 15-30
Valuation Activities
• Insights on data value by data ‘theme’ and recommendations on how to leverage data value were obtained
• Valuation calculation and creation
• Data decay rate and data quality weighting determined • Reports and fine tuning to finalise valuation
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Methodologies Utilised
A mathematical hybrid of:
Hierarchical Consumption Method: This was a cost-inclusive, consumption-based method, which relies on data ownership, a repository or platform with consumable data and consumers to consume the data. Use cases were clustered to accurately represent consumption.
Cost Method: Determining the price point and replacement value of data, with client specific financials and statistics, in addition to extra research to ensure the values align with industry standards
Rationale for models selection
Hierarchical clustering was selected due to the existence of an owned database, defensible data acquisition, maintenance costs and consumers of data. Additionally, multiple use cases that can be fitted into clusters to further aid the accuracy of the valuation.
The Cost Method was selected because the data was seen to be revenue generating, owned and with a market value. The client also had purchasers of data and quantifiable enhancement to their data to determine a price point.
Insights & Recommendations
• Data value by use case cluster
• Data value as an intangible asset
• Pricing validation and replacement value
• New KPI’s based on data asset value for statutory reporting
• Recommendations on data asset backed lending for collateralization and leverage
• Advanced data value forecasts, up to 5 years into the future