Reading operational data difficulties Which of the following is NOT a typical reason that operational (OLTP) data is hard to read for BI purposes?

Introduction / Context:
Operational databases are optimized for fast inserts/updates, not for analytics. BI teams commonly face quality issues, missing values, and data scattered across silos. This question asks you to spot the one choice that is not a problem.

Given Data / Assumptions:

• Common OLTP problems include dirty data, nulls, and cross-system fragmentation.
• Analytics often needs denormalization and time-variant history.
• Duplicate records are a headache; non-duplicated data is actually good.

Concept / Approach:

“Non-duplicated data” describes a desirable state: having a single, correct record for each business entity. The other options describe real obstacles when preparing data for BI. Therefore, select the item that is clearly not a reason for difficulty.

Step-by-Step Solution:

Verification / Alternative check:

Data quality frameworks aim to reduce duplicates, enforce constraints, and harmonize codes—improvements that make BI easier, not harder.

Why Other Options Are Wrong:

• Dirty data: increases cleansing effort.
• Missing values: block accurate metrics.
• Non-integrated: requires heavy ETL.
• Wrong format: forces re-modeling and aggregation.

Common Pitfalls:

• Assuming normalized OLTP schemas are directly consumable by BI without transformation.

Final Answer:

Non-duplicated data (no unnecessary repeats).