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Data Science metrics and KPIs

What are Data Science metrics?

Identifying the optimal Data Science metrics can be challenging, especially when everyday tasks consume your time. To help you, we've assembled a list of examples to ignite your creativity.

Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.

Find Data Science metrics with AI

While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.

Examples of Data Science metrics and KPIs

Metrics for Evaluating a Sourcing Model

  • 1. Accuracy of Predictions

    Measures how correctly the sourcing model predicts outcomes compared to actual results

    What good looks like for this metric: Typically above 70%

    Ideas to improve this metric
    • Use more comprehensive datasets
    • Incorporate machine learning algorithms
    • Regularly update the model with new data
    • Conduct extensive testing and validation
    • Simplify model assumptions

  • 2. Computational Efficiency

    Assesses the time and resources required to produce outputs

    What good looks like for this metric: Execution time under 1-2 hours

    Ideas to improve this metric
    • Optimize algorithm complexity
    • Utilise cloud computing resources
    • Use efficient data structures
    • Parallelize processing tasks
    • Employ caching strategies

  • 3. User Accessibility

    Evaluates how easily users can interact with the model to obtain necessary insights

    What good looks like for this metric: Intuitive with minimal training required

    Ideas to improve this metric
    • Develop a user-friendly interface
    • Provide comprehensive user manuals
    • Conduct user training sessions
    • Ensure responsive support
    • Regularly gather user feedback

  • 4. Integration Capability

    Measures how well the sourcing model integrates with other systems and data sources

    What good looks like for this metric: Seamlessly integrates with existing systems

    Ideas to improve this metric
    • Adopt standard data exchange formats
    • Ensure API functionalities
    • Conduct system compatibility tests
    • Facilitate flexible data imports
    • Collaborate with IT teams

  • 5. Return on Investment (ROI)

    Calculates the financial return generated by implementing the sourcing model

    What good looks like for this metric: Positive ROI within one year

    Ideas to improve this metric
    • Analyse cost-benefit ratios
    • Continuous optimisation for cost reduction
    • Align model outputs with business goals
    • Enhance decision-making accuracy
    • Regularly track and report financial impacts
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Implement these metrics

Tracking your Data Science metrics

Having a plan is one thing, sticking to it is another.

Don't fall into the set-and-forget trap. It is important to adopt a weekly check-in process to keep your strategy agile – otherwise this is nothing more than a reporting exercise.

A tool like Tability can also help you by combining AI and goal-setting to keep you on track.

Tability Insights DashboardTability's check-ins will save you hours and increase transparency

More metrics recently published

We have more examples to help you below.

Planning resources

OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:

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