As a Business Analyst, you receive a dataset from a business unit with missing values and inconsistencies. How would you approach this to ensure reliable insights?
RoleBusiness Development Associate
DifficultyMedium
TopicTechnical
Asked at
Why This Is Asked
Aboitiz operates data-driven units like UnionBank and data science teams. The interviewer tests your analytical rigor and understanding that reliable insights depend on clean data, reflecting the company's commitment to innovation and excellence.
General Approach
Outline a step-by-step process: assess data quality, choose appropriate cleaning methods, document everything, and communicate limitations. Show you balance thoroughness with practical deadlines.
General Answer
Start by framing your process around the question of what the data is actually meant to answer, because every cleaning decision flows from that. Say plainly that you would first run a quick audit to categorize the problems, separating missing values from inconsistencies like duplicate entries, format mismatches, or outliers that look like entry errors. For missing data, explain that you would check whether the gaps are random or follow a pattern, then decide between dropping, imputing, or flagging them, always noting the trade-off for the insight's reliability. For inconsistencies, say you would trace them back to source systems or manual entry habits, then standardize using clear rules, like date formats or naming conventions, and document every change so the business unit can audit your work. Bring in the Philippine context by mentioning that in a fast-paced setting like a BPO or a utility group, you would still resist the urge to just clean everything quickly, instead prioritizing the fields that matter most to the decision, and you would communicate early if the data cannot support a certain conclusion. Close by saying you would present the insights with a confidence level, explicitly stating what the data cannot tell you, which shows you value integrity over a polished but shaky answer.
PH Interview Gotcha
Some might say, "Deadline naman na, kung anong meron na lang." This undermines data integrity. Instead, show a methodical approach that prioritizes accuracy even under time pressure.
Sample STAR Answer▾
S
Situation
In a university project, I analyzed survey data for a local utility provider that had incomplete records and conflicting entries.
T
Task
I had to turn this unreliable dataset into a clean foundation for presenting service improvement recommendations.
A
Action
I first assessed the extent of missing and inconsistent data by counting gaps and checking for patterns. For missing values, I used median imputation for numeric fields and mode for categorical ones. For inconsistencies, I cross-referenced with source logs where possible and standardized formats. I documented each step so the business unit could replicate the process. Then I created a dashboard highlighting key trends with caveats about the data quality.
R
Result
My cleaned dataset let the team correctly identify billing issues as the top complaint, and the business unit implemented my recommended tracking system.
Key Takeaway
Systematic data cleaning is the foundation of trustworthy analysis.
Attempt Your Answer
Write your own answer, then get instant AI feedback graded against:
- •STAR Structure
- •Specificity & Numbers
- •Ownership Language
- •PH Workplace Context
Get AI feedback on your answer — free.
3 free AI-graded answers + 1 free mock interview, no card needed.
Sign Up FreeAlready have an account? Log in
Share Your Answer (0)
Sort
Sign in to join the conversation.
No answers shared yet — be the first to show how you'd approach this.
Related Questions
EasyCultural Fit
Passion For Foodservice Sales
Business Development Associate
MediumBehavioral
Organizing A Sampling Event
Business Development Associate
DifficultCommunication
Role-Play: Pitching a Budget Shift
Business Development Associate
DifficultTechnical
Handling A Skeptical Chef
Business Development Associate