Data Analysis and Data Cleansing 📊

I worked on cleansing and organising customer data using Excel. My focus was to improve the accuracy and efficiency of the dataset. I created a reliable and easy-to-manage dataset for further analysis and decision-making.

Cleanse customer data to resolve issues such as missing values, formatting inconsistencies, and errors.

Improve the accuracy of the data so it can be used reliably in future analysis.

Organise the data for efficient use by staff and stakeholders, ensuring it can be easily updated and maintained.

Working with customer data in different formats required careful attention and corrective action.

Managing multiple data sources to be merged and standardised to create a consistent dataset.

Ensuring the cleansed data was aligned with stakeholders' requirements, including project managers, directors, and managers, to meet their analytical needs.

Utilised VLOOKUP and conditional formatting to identify discrepancies, correct errors, and standardise entries across the dataset.

Applied quality checks and "sense checks" to ensure the data made sense and aligned with the intended analysis.

Structured the cleansed data in a format that made it easy for staff to update, manage, and reference, enabling quick issue identification.

Data accuracy improved, creating a true and accurate record that stakeholders could rely on. I streamlined data management, making it easier for staff to maintain and update customer information. Ensuring the data was well-organised and aligned with business needs enabled faster decision-making and issue identification.