Introduction:

This was a project for my IT Data Analyst position. The data is about Salesforce and Whatfix users and the main point of this analysis was to understand user engagement with Whatfix and its impact on Salesforce activities. The project also aimed to uncover insights about employee roles, engagement levels, and the correlation between Whatfix usage and Salesforce activities. It consists of 3 worksheets--the first contains 470+ records for users who engaged with Whatfix, the second contains 40,460+ records of all staff, faculty, and student workers at the UofA, the third contains 1,460+ records of actions a user took using Salesforce during the month of September.

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Insights:

There were many important insights to uncover from analyzing this data:

• How many employees are there per role, job title, parent organization, job family, and job function?

• What's the number of engagements with Whatfix by user, parent organization, and role?

• Do users who use Whatfix have more activities in Salesforce?

• What's the correlation between the Salesforce account age and different activities in Salesforce?

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Tools:

This project was analyzed using SQL, Tableau, and Python:

• SQL was used because I wanted to clean the data thoroughly and discover further insights with SQL's more complex functions. I started by cleaning the 3 worksheets which included changing table and column names, dropping columns, updating blank values, and changing data types. Then I outlined a total of 16 questions and wrote queries for each one. The clauses used included: SELECT, FROM, WHERE, LIKE, GROUP BY, SUM, COUNT, HAVING, ORDER BY, LIMIT, CTE's, SUBSTRING_INDEX, INNER JOIN.

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• Tableau was used because it was the tool requested for analysis by my supervisor and key stakeholders. I started by importing the Excel workbook with the 3 worksheets as 1 data source. I created a total of 29 worksheets with visuals comprised of text tables, bar graphs, column charts, packed bubbles, and scatter plots. Then I created 8 dashboards, with a couple worksheets in each dashboard. Lastly I combined these dashboards into a neat story, which included 8 slides. I presented this story to 2 key stakeholders interested in the relationships between Salesforce and Whatfix.

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• Python was used because it was requested by my supervisor due to its handling capabilities and ability to analyze and visualize the data all in one platform. The data was already cleaned from previous steps, so I started analyzing it. I outlined a total of 12 questions and used Pandas to answer them and Matplotlib to visualize them. The main Pandas syntax used were groupby and sort_values. The main Matplotlib visuals used were bar graphs and column charts.

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Impact:

The impact from analyzing this data was enhanced user engagement with Whatfix and optimization for tool adoption. The key stakeholders I presented this analysis to were able to enhance user engagement by better understanding what types of people use Whatfix and how they use it. Moreover, adopting this tool was easier, because now they had concrete data that told them this tool was working and users needed it.​

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