​Methodology:

I performed a comprehensive analysis of the school partner data to identify key metrics and patterns contributing to underperformance. I developed ETL (Extract, Transform, Load) pipelines to automate data flow and facilitate seamless integration of data sources. By leveraging Power BI data modeling techniques, I reduced time-to-insight by 70%, significantly enhancing access to critical data for informed decision-making.

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Data Collection and Cleaning Process:

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• Data Collection: I sourced data using various collection methods, including SFTP and Microsoft Teams private channels for student outcome records. This diverse data was consolidated into a central repository in Microsoft SharePoint.

• Data Cleaning: The data cleaning process involved:

  • Standardization: Ensuring consistency in data formats, including name formats, grade levels, etc.

  • Validation: Checking for errors or discrepancies, such as missing values or outliers, and addressing these issues through imputation or removal.

  • Normalization: Adjusting values to a common scale without distorting differences in ranges to facilitate accurate analysis.

  • Power BI Connector: Utilizing the Power BI connector via the web and employing Power Query to streamline the data cleaning process efficiently.

 

Challenges:

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• Diverse Data Collection Methods: Different collection methods across schools necessitated standardization of data to ensure consistency and reliability.

• Administrative Staff Overwhelm: Administrative staff were often unaccustomed to and overwhelmed by the data requirements, hindering effective data management and analysis.

 

Business Questions Addressed:

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• What were the total intervention hours and sessions conducted?

• How was student growth measured in Math and ELA?

• How many available members were there per subject?

• What was the current membership count?

• What were the growth trends over time?

• How did we compare to the rest of the network in terms of performance?

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Findings:

The analysis revealed critical insights into the factors affecting partner performance, highlighting areas for improvement in engagement strategies and data collection practices.

Action Plan:
 

Based on the findings, I developed a strategic action plan aimed at:

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• Enhancing partner engagement through targeted initiatives.

• Standardizing data collection methods to improve accuracy.

• Providing training and support to administrative staff to alleviate their data-related challenges.

 

Business Impact/Outcome:

The implementation of this action plan was expected to foster better partnerships and ultimately lead to improved performance metrics throughout the organization.