Kanishka Suresh Babu
MPS Analytics’26
Internal Audit Co-op at Hologic Corporation

What challenge did you tackle during your co-op?

One of the main challenges I tackled during my co-op at Hologic was dealing with inconsistent and messy data across different audit and reporting systems. When I started working on KPI dashboards, the data coming from multiple sources didn’t align. There were differences in formats, missing values, and inconsistent definitions of key metrics. If I had just used the data as-is, the dashboards would have been misleading, which defeats the whole purpose of audit reporting. To fix this, I had to first understand how each dataset was generated and what each field actually meant. Then I cleaned and standardized the data using Alteryx, built validation checks, and ensured consistency before pushing it into Power BI. I also worked with stakeholders to clarify metric definitions, so everyone was aligned on what the numbers represented. The biggest takeaway was realizing that data accuracy matters more than just building dashboards. Anyone can create visuals, but if the underlying data is wrong, it’s useless. 

How did you approach solving this complex organizational challenge?

To address the data inconsistency issue during my co-op at Hologic, I approached it in a structured way instead of jumping straight into building dashboards. First, I broke down the problem by identifying all the data sources involved and understanding how each dataset was generated. I realized that the inconsistencies weren’t random; they came from differences in definitions, formats, and manual inputs across systems. Next, I focused on standardization. I used Alteryx to clean and transform data by handling missing values, aligning formats, and creating consistent field definitions. At the same time, I built validation checks to catch mismatches early instead of discovering them after the dashboard was built. 

I also made sure to communicate with stakeholders to clarify what each KPI actually meant. This was critical, because without a shared definition, even clean data can lead to wrong conclusions. Finally, I integrated the cleaned and validated data into Power BI and built dashboards that reflected accurate and reliable metrics. 

What skills did you develop and apply during this experience?

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