What data research metrics should you track?

1 Data Quality

Data quality is the accuracy, completeness, consistency, and reliability of data sources and methods. Poor data quality can have negative repercussions such as erroneous conclusions, wasted resources, and damaged reputation. To ensure data quality, you should track metrics like data validity (the extent to which your data meets the standards and rules of your domain and research question), data completeness (the proportion of missing, incomplete, or outdated data in your dataset), data consistency (the degree of agreement and alignment between different data sources, formats, and definitions), and data reliability (the ability of your data to produce consistent and repeatable results under similar conditions).

2 Data Analysis

Data analysis is the process of transforming, modeling, and interpreting your data to answer research questions and generate insights. To measure its effectiveness, you should track metrics such as data complexity, relevance, efficiency, and creativity. Data complexity involves the degree of difficulty and sophistication when analyzing your data, depending on its size, structure, diversity, and variability. Data relevance measures the fit between your data and research question, objectives, and hypotheses. Data efficiency is the ratio of value and impact of your data analysis to the time and resources spent on it. Lastly, data creativity gauges the extent to which your data analysis produces novel, original, and unexpected findings and solutions.

3 Data Visualization

Data visualization is the process of presenting your data analysis in a graphical or visual form that makes it easy to understand, explore, and communicate. To ensure good data visualization, it should be clear, accurate, engaging, and informative. When assessing data visualization, metrics such as data clarity, accuracy, engagement, and informativeness should be tracked. Data clarity relates to the ability of the data visualization to convey a message without ambiguity or confusion. Data accuracy measures the fidelity between the data visualization and the data analysis. Data engagement measures how well the data visualization captures the attention of its audience. Finally, data informativeness looks at how much information and insights are provided by the data visualization.

4 Data Communication

Data communication is the process of sharing your data analysis and visualization with your audience and stakeholders, using appropriate channels, formats, and languages. To ensure effective data communication, it should be persuasive, relevant, and actionable. When evaluating data communication, you should track metrics such as its persuasiveness, relevance, and actionability. Specifically, you should assess the ability of your data communication to convince and influence your audience, the degree of fit between your data communication and the needs of your audience, and the extent to which your data communication enables and encourages them to take action based on your findings and recommendations.

5 Data Impact

Data impact is the ultimate goal and outcome of your data research, reflecting the value and benefits that your data analysis and communication generate for your field, profession, organization, or society. Although measuring data impact can be difficult, it is indispensable to illustrate your contribution and validate your resources. To estimate data impact, it is recommended to track metrics such as data citations, data feedback, and data outcomes. Data citations refer to the number and quality of references and acknowledgements that your data research receives from other researchers, publications, or sources. Data feedback entails the type and level of responses and reactions that your data research elicits from your audience and stakeholders. Lastly, data outcomes are the tangible and intangible effects and changes that your data research produces or influences, such as decisions, actions, policies, behaviors, or attitudes.