Evaluating Data Products
Before investing in research or production, a data product must be evaluated against two critical axes: Technical Validity (Does it work?) and User Utility (Is it useful?). Use this interactive tool to assess your potential product.
Criterion 1: The Algorithm
Does the methodology successfully create a New Signal from the noise?
Criterion 2: The Process
Do users or business processes find this signal Useful?
1 Signal Creation Analysis
A data product is only viable if the algorithm can extract a meaningful signal from raw data. Use the simulation below to visualize how "Algorithm Efficacy" transforms raw noise into an actionable trend. If the signal is weak, the product fails technically.
Key Indicators
💡 Insight: A score below 60 usually indicates the technology is not yet mature enough to support a product.
2 User Utility & Process Fit
Even a perfect algorithm fails if it doesn't solve a problem. Select the attributes below that apply to your potential users. The chart will update to show the "Utility Profile" of the product.
Utility Drivers
The chart displays the balance of utility. A strong product scores high in Actionability (it drives decisions) and Trust (users believe it).
Strategic Decision Matrix
Combine your Technical Signal score and User Utility score to determine the next step. Should you "Start Research" or pivot?
Recommendation:
Adjust the inputs above to generate a strategy.