25jan26 - how to do profilng in which tool and how

 Here’s a clean, interview-ready rewrite focused on one 3rd-party tool (Informatica Data Quality), with step-by-step profiling and reporting. You can say this almost verbatim.

---

Tool used for data profiling

I use Informatica Data Quality (IDQ) to profile data files and generate data quality reports before loading them into the database.

---

Step-by-step: How I do data profiling in Informatica Data Quality

Step 1: Ingest the data file

• Connect the source file (CSV / Excel / flat file / S3 / database)

• Define the source metadata in IDQ

• Validate column names and data types

📌 Purpose: Ensure the file is readable and structurally correct.

---

Step 2: Run Column Profiling

• Use Column Profile in Informatica Analyst

• Analyze each column for:

  • Data type distribution

  • Null and blank percentage

  • Min / Max values

  • Distinct count

  • Value frequency

📌 Purpose: Understand the content and detect obvious issues.

---

Step 3: Run Data Domain & Pattern Analysis

• Apply data domains (date, email, phone, numeric)

• Use pattern analysis to detect invalid formats

Examples:

• Invalid email formats

• Date columns stored as strings

• Mixed data types in one column

📌 Purpose: Validate format and consistency.

---

Step 4: Identify Duplicates

• Use duplicate analysis on key fields

• Identify exact and fuzzy duplicates (if needed)

Example:

• Same order_id appearing multiple times

• Same customer with slight name variations

📌 Purpose: Prevent double counting and incorrect metrics.

---

Step 5: Apply Business Rules

• Create business rule transformations

• Examples:

  • Order amount > 0

  • Delivery time between 0 and 180 minutes

  • Order status ∈ allowed values

📌 Purpose: Ensure data follows business logic, not just technical rules.

---

Step 6: Generate Data Quality Score

• Assign weights to rules (critical vs non-critical)

• Calculate overall data quality score

• Categorize issues:

  • Critical

  • Warning

  • Informational

📌 Purpose: Measure readiness of the file.

---

Step 7: Create Data Quality Report

• Generate profiling reports from Informatica Analyst

• Report includes:

  • Column statistics

  • Failed rules

  • Duplicate counts

  • Data quality score

📌 Purpose: Provide transparency to stakeholders.

---

Step 8: Go / No-Go decision

• If critical rules fail → Reject the file

• Notify source system / upstream team

• Reload only after correction

📌 Purpose: Prevent bad data entering the warehouse.

---

How I ensure the file is in good shape

A file is considered ready when schema validation passes, nulls and duplicates are within thresholds, business rules are satisfied, and the overall data quality score meets acceptance criteria.

---

Strong closing line (interview gold)

“Using Informatica Data Quality, I profile the data structurally, statistically, and against business rules, generate a data quality report, and enforce Go/No-Go criteria before loading the data.”

---

 

Just say 👍