In the fast-paced world of data analytics, clarity is often a rare commodity, especially when dealing with unrefined datasets. A recent project for Safari Connect, a bus and matatu booking platform in Nairobi, exemplifies this challenge. An Operations Director reached out, frustrated with a chaotic Excel spreadsheet that had accumulated over time. The directive was clear: clean the data, analyze it, and present professional findings to the board within a week, all while ensuring the CEO would be present. High stakes indeed.
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The task involved taking 290 rows of untidy data filled with inconsistencies and errors, cleaning it using PostgreSQL, and transforming it into six valuable business insights that could guide decision-making. The initial step included creating a staging table where all columns were defined as TEXT to prevent import errors from unexpected data formats.
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As the data was processed, it became evident that numerous categories of errors existed, ranging from improperly formatted names and phone numbers to inconsistent date entries. With 23 identified issues, each was tackled through precise SQL queries, ensuring that the data was not only clean but also reliable for further analysis. The final cleaned dataset allowed for a deeper dive into the operations and performance metrics of the company, leading to actionable insights about routes, driver performance, revenue trends, and customer satisfaction.
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Ultimately, the analysis revealed crucial information, such as the most profitable routes and the correlation between driver ratings and passenger satisfaction. This meticulous approach to data cleaning and analysis not only provided clarity to the CEO but also set a precedent for how data should be handled in future projects at Safari Connect.
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Read the original article: Dev.to
