You're facing conflicting data sources in a project. How do you ensure accuracy without delays?

To identify issues with conflicting data sources, start with these strategies: 1) Metadata Analysis - Analyze metadata associated with the datasets. This includes understanding the context, source, time of data capture, and any transformations applied. Metadata provides insights into potential sources of conflicts. 2) Data Lineage - Trace the origin and transformation history of the data. Understanding the data lineage can help identify points where discrepancies might have been introduced. This also helps in identifying the most authoritative source. 3) Stakeholder Interviews - Engage with stakeholders and data owners to gain insights into possible reasons for discrepancies. Understanding the business context can reveal hidden issues.

First find out the root cause of the discrepancy and connect with source team if this discrepancy is expected or needs to be fixed from source side. Setup process to automatically check the data quality like datatype, file format and values expected. Create a data dictionary document for teams reference.

Create a single source of truth by centrally managing master data using MDM, ensuring consistency across systems. Assign trust levels to each data source based on reliability, accuracy, and timeliness; for instance, data from a verified internal system may have a higher trust level than from an external source. Establish a hierarchy of data sources so that when conflicts arise, the value from the higher-ranked source is preferred. Develop business rules to resolve conflicts, such as preferring the latest update or prioritizing a specific source. Define policies for handling data attributes, like using the most recent update for addresses and prioritizing accuracy for names.

There should be single source of truth for each feed or system, so that it is easier to figure out the discrepancies and fix them. If a system is aggregating data from multiple sources, precedence should be set for each source. Setup rules for handling any discrepancies.

When reconciling conflicting data sources, I learned from my experience working with conflicting data from sales and marketing. For instance, when sales and marketing data didn't match, I prioritized sales data due to its direct impact on core operations (also for reliability - internal systems usually come first). I set clear rules like using the most recent data or combining values where needed, and I automated these rules with SQL to ensure consistency and reduce errors. This method created a clear, repeatable process that others could easily follow. Also documenting the process was really helpful, as it helped the team stay aligned and made the process easy for others to follow.

Paradigm shifts in Conflict Data practices automatism which is a very efficient mode of handling conflicts in the shortest possible time. With programs and instead of copying and pasting data from one application to another, you can apply many reconciliation rules at once and be sure of the data that is transferred. Decide on which of the source differences shall be addressed in accordance with which criteria, for example, decided to adhere to the particularities of the later or more accurate data. Use functions like coalesce to give a priority to the values that were obtained from more trustworthy sources to prevent passing on of errors. There are also multiple opportunities in terms of using automated tools for monitoring.

Coalesce is an important sql function to prioritise which values should be considered. This can be used to decide the priority of any particular source system.

Coalesce is an important sql function to prioritise which values should be considered. This can be used to decide the priority of any particular source system.

One effective way to ensure continuous monitoring is by using real-time dashboards and visualization tools like Tableau or Grafana. For example, I’ve seen teams integrate monitoring with AWS CloudWatch or Azure Monitor, which automatically sends alerts when data deviates from what's expected. This tools allow you to track data quality metrics and spot trends or anomalies as they happen and also allow you to respond to them quickly!

Data monitoring acts as a catalyst to identify potential errors and prevent pipeline breaks. Setting up alerts in all three zones of the pipeline (raw, enriched, and curated) helps ensure immediate notification regarding any issues. This helps mitigate the risk of further pipeline breaks.