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KPI Decision Matrix

The KPI Decision Matrix provides you with the most important kpis to make an informed buying decision. Decision Matrix

Getting Started

  1. Add all matching solutions you want to compare to Snowman
  2. If you have experiments of the matching solution for datasets with a gold standard add them to Snowman
  3. Open the Benchmark Dashboard and select the analysis KPI Decision Matrix.
  4. Select your matching solutions and experiments in the configurator

Interpreting the Results

KPI Definition
Matching Solution Type The architectural type of the matching solution, such as "Machine Learning", "rule-based" or "Active Learning"
Use Case Which use cases does the matching solution cover (e.g., merging from two file, deduplicating one file, ...)? Some matching solutions do not support every use case.
General Costs These include the cost to buy a matchingsolution, the cost to host the infrastructure, etc.. Since thesecan vary from payment model to payment model (one-timecosts, monthly costs, pay-per-use), they are aggregated overthe entire product lifecycle to increase comparability. Sincethese can vary from payment model to payment model (one-time costs, monthly costs, pay-per-use), they are aggregatedover the entire product lifecycle to increase comparability.
Deployment Type This describes in which way the matching solution has to be deployed: Does a hosted instance exist already? Or is it just locally executable? Hosted instances,for example, often require less maintenance and might be therefore a decision criteria.
Installation Effort Embedding a matching solution in the software landscape of a company requires a specific knowledge level and amount of time which varies for each matching solution. The integration effort is split into these kpis.
Matching Solution Effort Another task is configuring the matching solution itself. Some matching solutions do not require any specific knowledge (for example, solutions where uploading only a file is sufficient), but other matching solution need detailed configurations. Because of that, we propose to measure the effort to configure a matching solution by knowledge-level and HR-Amount.
Domain Effort Matching solutions often require domain knowledge during the configuration phase: A machine learning based approach, for example, needs pre-labeled data to fit the model. For that, a person who knows the domain has to classify given pairs of records, in order to create a trainings dataset. For rule-based approaches domain knowledge is needed, too: Rules have to be created, based on pair-wise similarities. To define effective rules, it requires domain knowledge. This results in a domain effort for the specific matching solution, measured by the required expertise and HR-Amount.
Interfaces This describes the provided interfaces to access the matching solution (e.g.: GUI, API, CLI). Depending on the use case it might be necessary that the matching solution provides a specific interface: For example, the automation of specific data matching tasks requires an API or CLI.
Supported OS On which operating systems is it possible to execute the data matching solution?


knowledge-level and hr-Amount are mutually dependent. A person with a higher knowledge-level does usually need less time to configure a specific matching solution. To reflect this issue, we provide an aggregated measure. This is calculated with the manhattan-distance between the knowledge-level and the hr-Amount