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Data Completeness metrics and KPIs

What are Data Completeness metrics?

Identifying the optimal Data Completeness 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 Completeness 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 Completeness metrics and KPIs

Metrics for Data Selection and Rule Development

  • 1. Data Accuracy

    Measures the percentage of data entries that are correct and error-free across the system

    What good looks like for this metric: Above 95%

    Ideas to improve this metric
    • Implement regular data audits
    • Use automated data validation tools
    • Provide staff training on data entry accuracy
    • Establish clear data entry guidelines
    • Enable error-detection algorithms

  • 2. Data Completeness

    Assesses the percentage of data records that are fully filled and not missing any critical fields

    What good looks like for this metric: Above 90%

    Ideas to improve this metric
    • Conduct routine completeness checks
    • Utilise automated form filling
    • Standardise data requirements
    • Regularly review data input processes
    • Incentivise complete data entry

  • 3. Data Timeliness

    Measures the speed at which data is updated or made available for processing

    What good looks like for this metric: Within 24 hours

    Ideas to improve this metric
    • Automate data update processes
    • Set clear timelines for data entry
    • Monitor data latency regularly
    • Establish a data steward for timely updates
    • Prioritise data updates during peak times

  • 4. Data Consistency

    Evaluates whether data is consistent across different databases and sources

    What good looks like for this metric: Close to 100% consistency

    Ideas to improve this metric
    • Implement cross-system data comparisons
    • Use master data management tools
    • Regularly review data transformation processes
    • Ensure consistent data entry formats
    • Provide training for consistent data handling

  • 5. Data Relevance

    Determines the degree to which data is relevant and useful for current business needs

    What good looks like for this metric: Above 85% of data in use

    Ideas to improve this metric
    • Regularly review and update data policies
    • Conduct user feedback sessions
    • Align data selection with business objectives
    • Utilise data analytics to assess relevance
    • Remove outdated or redundant data regularly
data-accuracydata-completenessdata-analystdata-stewarddata-management-teamit-department
Implement these metrics

Tracking your Data Completeness metrics

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

Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.

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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