How do you resolve data conflicts when working with different sources?

1 Identify the sources and types of data conflicts

The first step to resolve data conflicts is to identify the sources and types of data conflicts. Data conflicts can arise from various sources, such as human errors, system errors, data integration issues, or data interpretation issues. Data conflicts can also be classified into different types, such as syntactic, semantic, or pragmatic. Syntactic conflicts are related to the format, structure, or representation of the data. Semantic conflicts are related to the meaning, definition, or context of the data. Pragmatic conflicts are related to the purpose, use, or implication of the data. By identifying the sources and types of data conflicts, you can better understand the root causes and the potential solutions.

2 Establish a common data standard and governance

The second step to resolve data conflicts is to establish a common data standard and governance. A common data standard is a set of rules, guidelines, or conventions that define how the data should be collected, stored, processed, and presented. A common data standard can help reduce syntactic and semantic conflicts by ensuring consistency and compatibility across different sources. A common data governance is a framework that defines the roles, responsibilities, and processes for managing, sharing, and using the data. A common data governance can help reduce pragmatic conflicts by ensuring alignment and accountability among different stakeholders.

3 Use data validation and verification techniques

The third step to resolve data conflicts is to use data validation and verification techniques. Data validation is the process of checking whether the data meets the predefined criteria, such as data types, ranges, formats, or values. Data verification is the process of checking whether the data is accurate, complete, and up-to-date. Data validation and verification techniques can help detect and correct data errors, anomalies, or outliers that cause data conflicts. Some examples of data validation and verification techniques are data quality checks, data cleansing, data reconciliation, data auditing, or data testing.

4 Apply data transformation and harmonization methods

The fourth step to resolve data conflicts is to apply data transformation and harmonization methods. Data transformation is the process of converting or modifying the data from one format, structure, or representation to another. Data harmonization is the process of integrating or combining the data from different sources into a coherent and consistent whole. Data transformation and harmonization methods can help resolve or mitigate data conflicts by enhancing the interoperability and compatibility of the data. Some examples of data transformation and harmonization methods are data mapping, data conversion, data normalization, data aggregation, or data fusion.

5 Communicate and collaborate with your data team

The fifth step to resolve data conflicts is to communicate and collaborate with your data team. Communication and collaboration are essential for resolving data conflicts effectively and efficiently. Communication and collaboration can help you share information, insights, and feedback on the data, as well as identify and resolve data issues, challenges, or disagreements. Communication and collaboration can also help you leverage the skills, expertise, and perspectives of your data team members, as well as foster a culture of trust, transparency, and learning. Some examples of communication and collaboration tools are data documentation, data visualization, data annotation, data review, or data sharing.

6 Learn and improve from your data conflicts

The sixth step to resolve data conflicts is to learn and improve from your data conflicts. Data conflicts are not only problems to be solved, but also opportunities to be learned from. Learning and improving from your data conflicts can help you enhance your data quality, data literacy, and data analytics skills, as well as prevent or reduce future data conflicts. Learning and improving from your data conflicts can also help you generate new insights, ideas, or solutions for your data analytics projects. Some examples of learning and improving tools are data analysis, data evaluation, data feedback, data improvement, or data innovation.

7 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?