This is quite usual to happen in edtech projects. The key is to identify a set of focal metrics and stick by them for better clarity. Use business intelligence tools like Tableau, or Power BI to crunch the large data sets efficiently and identify patterns/anomalies/outliers. Keep some scope for changes (adding/deleting/editing the focal metrics) during the project duration. "Most importantly, link the data to the actual student outcomes in order for the edtech project to be most productive. Data and real world outcomes don't exist in silos!"

Great insights! When navigating overwhelming data in educational technology, especially in the context of inclusion, I recommend a targeted approach. In addition to identifying key metrics and using data visualization tools, prioritize gathering user feedback from diverse learners to ensure their voices shape our understanding of impact. Establishing clear objectives around inclusivity helps align data collection with project goals. Lastly, creating a data strategy that focuses on equity can guide analysis and action. By integrating these methods, we can turn data into powerful tools for improving inclusive education.

The trick is to stop treating everything as equally important. Start by asking: *What problem am I solving?* Filter ruthlessly—focus on data that directly impacts that goal. Next, group similar insights to spot trends instead of getting stuck on outliers. Use visuals—charts, dashboards—to simplify patterns. And never underestimate a fresh set of eyes; share key findings with colleagues to refine focus. It's not about knowing it all; it's about knowing what matters. Always remind yourself: clarity over clutter wins.

Great question! When overwhelmed with data in an edtech project, it is important to focus on what aligns with your goals and your stakeholders' needs. Invest time in identifying the most critical metrics and use visualization tools to spot key patterns. Collaborate with your project partners for diverse perspectives and consider sampling large datasets to save time. Finally, set time deadlines for the analysis to avoid procrastination and revisit priorities as the project evolves.