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Streamlining Partnership and Learner Data for Better Insights
A Interactive Case Study on Organizing Data and Enhancing Decision-Making
The Challenge
Key Points:
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Disorganized, duplicated, and unstructured data across Google Drive.
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Struggled to track learner origins and partnership impact.
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Needed a better way to measure the effectiveness of educational institutions and partners.
Our organization faced a significant challenge in managing a large volume of unorganized data.
Information on learners, educators, and educational institutions was scattered across Google Drive, with no clear system for sorting or tracking. Files were duplicated, lacked consistent naming conventions, and were often missing dates. We needed a solution that could help us track where learners came from, measure educator engagement, and assess the effectiveness of our partnerships with institutions.
The Approach
To tackle the problem, we began by consolidating all available data into a single location. We then created clear definitions for key terms such as "learner," "educator," and "educational resource." With these definitions in place, we developed a systematic process for handling missing data and incomplete records. Google Sheets, ChatGPT, and Google Docs were essential tools in organizing, analyzing, and documenting every step of the process.
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Key Points:
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Consolidated all data into one location for easier access and analysis.
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Defined key terms to standardize our approach and ensure consistency.
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Used tools like Google Sheets and ChatGPT for sorting, formatting, and documenting.
Building the Data System
Creating a Clear and Actionable System
Developed spreadsheets to track and categorize learners and educators.
Created a Partner Dashboard to assess cost of acquisition and partner effectiveness.
Automated data calculations using Google Sheets formulas for better efficiency.​
Creating a Clear and Actionable System
Once the data was consolidated, we focused on making it actionable. This involved creating a series of spreadsheets to track and categorize learners, educators, and institutions. We implemented formulas to automate calculations, such as tracking the number of educators from specific institution types (e.g., Historically Black Colleges Universities, Majority Serving Institutions). A key addition was a Partner Dashboard, which tracked the cost of acquisition (COA) for each partner, allowing us to assess the value of each partnership.
Frequent meetings with leadership to adjust the system based on real-world feedback.
Refined data definitions and presentation formats for clarity and effectiveness.
Focused on key metrics that truly reflected the success of our partnerships.
Adjustments and Feedback
Throughout the process, we engaged in frequent feedback sessions with leadership to ensure the system was both practical and effective. We quickly learned that not every definition or methodology worked in practice as expected. As a result, we refined our approach, adjusting both the data definitions and how we presented the data. These discussions helped us understand what truly mattered in terms of measurable impact and what could be refined.
Final deliverable: 5-page visual report showcasing key metrics.
Established SOP for data handling and ongoing management.
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Organized folder structure within Google Drive for easy access.
Clear, Organized, Actionable Data
After six months of refining the system, we achieved our goal: a clean, organized dataset with actionable insights. The result was a 5-page visual report that highlighted key metrics, such as the number of educators engaged, the type of institutions involved, and the effectiveness of each partnership. We also created an organized folder structure in Google Drive for easy access and created a Standard Operating Procedure (SOP) to ensure consistency in the future.
What We Learned
Key Points:
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Organized data enables better decision-making and partnership effectiveness.
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Iterative refinement based on feedback is crucial for success.
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The system is scalable and adaptable for future growth and optimization.
This project taught us the importance of structured data in making informed decisions and optimizing partnerships.
We learned that while it’s important to have a solid foundation, the process is iterative and requires constant feedback and refinement.
The system we’ve built is flexible, allowing for future updates and growth, and lays the groundwork for more sophisticated data analysis and decision-making moving forward.
