The data quality is crucial in all data analysis. The sources, preprocessing and codes should be minutiously revised to avoid problems such as data leake. In this context it is very important to read the code's documentation, some have many surprising tricks.

When stakeholders rely on inconsistent data, it can jeopardize the accuracy of decisions. To address this, start by ensuring everyone is aligned on the same datasets and definitions, clarifying all data sources upfront. Encourage open dialogue among stakeholders to discuss and resolve discrepancies collaboratively. Implement regular feedback loops to update and verify data accuracy, maintaining a shared foundation for reliable, statistics-based decision-making.

Establish a Single Source of Truth: Collaborate with stakeholders to agree upon a centralized, reliable source for each data category (e.g., sales, customer data, KPIs). Whether it is an agreed-upon database, BI tool, or data warehouse, ensure everyone is in agreement on where "true" data resides. Define data standards and protocols: Standardize data formats, definitions, and calculations to leave no room for misinterpretation—for example, revenue calculations and date formats. Document the standards and make them available to all concerned so discrepancies do not arise in the future.

Imagine a meeting where the numbers don’t match—one stakeholder quotes one data set, another presents something different, and the discussion feels like a puzzle with missing pieces. So how do we navigate this maze? 1) Identifying the root cause of discrepancies in data sources, timing, or metrics definition is key. 2)A unified database or reporting tool ensures that everyone is working with the same information. 3)Regular meetings between teams help align definitions and expectations. Sometimes, a simple conversation can solve a lot. 4)Clear guidelines on how data is collected, reported, and used will help maintain consistency now and in the future.

First, source of data should be provided by parties. If it is different platforms for both parties; then it is a whole different issue- attribution/counting logic/etc should be discussed. So a very granular investigation(even transaction level) might be necessary here. If the source of data is the same, then time period should be checked. Then, queries/segments used in delivering the data should be checked, a very small nuance can be the factor creating the whole difference. Most probably these steps will uncover what is the reason of inconsistency. Then, a commong ground should be met based on business needs.

Start at the beginning, ensure the same source data is used else different teams will recommend different outcomes. They will be pushing forward for incompatible solutions if they are making different assumptions from different inputs.

Deplore the 5 Whys as to identify the root cause of discrepancies. This powerful technique - at the same time - opens the communication between all stakeholders. Next step is to take the necessary actions as to eliminate the root causes and final step is to validate the results by implementing control monitor mechanism.

Establishing proper procedures is crucial to eliminate data inconsistencies, ensuring stakeholders rely on accurate information for informed decisions. Implement standardized data entry, regular audits, and a centralized repository to maintain a single source of truth. Consistent reviews and staff training further enhance data reliability, enabling confident and strategic decision-making.

Here’s how to ensure informed, unified decision-making! 📊 Establish a Single Source of Truth: Centralize data in one shared, accessible platform, so everyone’s on the same page. ✅ Transparent Validation: Use automated checks and manual reviews on key metrics to catch discrepancies early and build trust. 📅 Regular Alignment Meetings: Host monthly reviews focused on data clarity and open Q&A to address concerns upfront. 📈 Real-Time Dashboards: Design interactive dashboards that spotlight high-impact metrics, making it easy to spot issues. 🔄 Feedback Loop: Post-decision analysis keeps data quality top of mind. Align your data, and empower your team to make confident, informed decisions! #DataDriven #StakeholderAlignment #DecisionMaking

I would say that it is also important to debate the sources of those data inconsistencies: once we have discarded potential systematic errors linked to the questions or to the enumerators, it is important to debate whether they are really inconsistencies or whether they might reflect heterogeneity