Dig deeper into the origins. Where is it coming from? Go to the source and look at the data for yourself. There could be something going wrong in the transcription process.

The integrity can be ensured with simple steps: 1. Assess the credibility of each data source. Official government databases, peer-reviewed research, and reputable institutions usually provide more reliable data compared to unofficial or secondary sources. 2. Compare data points from multiple sources to identify commonalities and discrepancies. Standardize the metrics if possible and keep track of where each piece of data comes from and any changes over time. 3.In situations of high uncertainty, consult experts who can provide insights on the most reliable sources or help interpret ambiguous data and investigate whether outliers are distorting the data.

I've learned that when dealing with conflicting data sources, the key is to take a deep dive into the origins of the data—examining the "how," "when," "why," and "where" behind each dataset. Never jump to conclusions by blaming one source; this can lead to unnecessary challenges. Instead, identify the factors causing the discrepancies and assess whether the data complements or supports each other. Once you have a clear understanding, escalate the matter to higher management, recommending the creation of an integrated dataset by consolidating multiple sources to maintain data integrity.

Some points we need to keep in mind: - Are all data sources equally reliable? Which one has the most rigor in terms of data collection, standardization, parameterization, methodology, etc.? - Is it possible to standardize the data sources through a correspondence table, correction factor, or other means? - Knowing that this issue may arise in the future, maintain clear documentation on the criteria and methods used in data collection and in the calculation of indicators. - Clearly communicate the issue to stakeholders, explaining the situation encountered, the consequences of using each data source, and requesting support when necessary.

Conflicting data is something almost every research encounters, as data providers often have their own perspectives and biases, making the information not entirely neutral. In such cases, relying on trends and structural data, rather than just absolute figures, can help better understand the facts and make more informed value judgments. 相互冲突的数据几乎每一个研究都会遇到，因为数据提供者本身的视角并非中性，往往有所指向。此时，借助于于趋势和结构性数据，而不仅仅是依赖于绝对数据，会有助于理解事实和进行价值判断。