How do you handle inaccurate data in content metrics?

1 Identify the source of inaccuracy

The first step to handle inaccurate data is to identify where it comes from and how it affects your metrics. There are many possible sources of inaccuracy, such as human errors, technical glitches, data gaps, sampling methods, or external factors. For example, you might notice a sudden spike or drop in your traffic, engagement, or conversions that is not related to your content quality or strategy, but to a change in the analytics tool, a bot attack, or a seasonal trend. To identify the source of inaccuracy, you need to audit your data collection and analysis processes, check for anomalies and outliers, and compare different data sources and time periods.

2 Correct or discard the inaccurate data

Once you identify the source of inaccuracy, you need to decide whether you can correct or discard the inaccurate data. Depending on the nature and extent of the inaccuracy, you might be able to fix it by applying filters, segments, or adjustments to your analytics tool, or by manually editing or deleting the data. For example, you can filter out bot traffic, segment your audience by location or device, or adjust your attribution model to better reflect your customer journey. However, if the data is too corrupted, unreliable, or irrelevant, you might have to discard it and focus on other metrics or data sources.

3 Communicate the inaccuracy and its impact

Another important step to handle inaccurate data is to communicate it and its impact to your stakeholders, clients, or team members. You need to be transparent and honest about the data quality issues, how they affect your metrics and KPIs, and what actions you are taking to resolve them. For example, you can explain how the inaccuracy occurred, how it skewed your results, and how you corrected or discarded the data. You can also provide alternative metrics or data sources that are more accurate and relevant to your content goals and strategy.

4 Establish data quality standards and best practices

The final step to handle inaccurate data is to establish data quality standards and best practices that can prevent or minimize future inaccuracies. You need to define what data quality means for your content strategy, what metrics and KPIs you use and how you measure them, and what data sources and tools you trust and rely on. You also need to document your data collection and analysis processes, train your team members on how to use the data correctly, and regularly monitor and review your data quality and performance.

Inaccurate data can be a challenge for any content strategist, but it doesn't have to be a disaster. By following these steps, you can handle inaccurate data in content metrics and ensure that your data-driven decisions are based on reliable and relevant information.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?