Aerospace Case Study
Client Data Profile
• Provides data sets and analytics tools for businesses, mostly in the aerospace engineering and construction fields
• Over $190B in accessible public capital projects
• Geographical, market and budget-based subscription
Objectives
• A data valuation to reinforce financial strength of existing data driven assets
• Client intends to receive funding and utilise the data valuation as quantifiable evidence of data driven success
• Understand the data valuation in relation to data funding and leverage for business growth
Our approach
Day 0-5
Framing Activities
• DVP considers data value to be directly proportional to its impact, where impact is created by the transformation of data from descriptive to prescriptive
• Completed a thorough analysis of data repositories and financial info from Arizona, plus forecast info from National Data sets for the client business
• Analysis of data ops, acquisition, licensing, subscription, cost improvement and more were considered in the data valuation
• Checking essential assumptions on data ownership of the above
Day 6-15
Collateral Activities
• Select mathematically driven valuation methodology based on prior information, data analysis, ownership of data repository and themes
• Maintenance and acquisition costs were considered as opposed to replacement value
Day 16-30
Valuation Activities
• Insights on data value by data ‘theme’ and recommendations on how to leverage data value were obtained.
• Valuation calculation and creation
• Reports and fine tuning to finalise valuation
Methodologies Utilised
A mathematical hybrid of:
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.
Why it was selected
Due to the existence of an owned data repository, defensible data acquisition, maintenance costs and consumers of data.
Insights & Recommendations
• Data value by sector
• Data as an intangible asset
• Pricing validation
• Recommendations on data lending and leverage