Quality Management Terminology Starting with “D” – Easy Explanation

1. Data Quality Assessment (DQA) – “Data reliable hai ya nahi?”

Definition:
Scientific + statistical evaluation process jisse verify kiya jata hai ki collected data intended purpose ke liye suitable hai ya nahi.

5 Steps of DQA:

1️⃣ DQOs aur sampling design review
2️⃣ Preliminary data review
3️⃣ Statistical test selection
4️⃣ Assumptions verify karna
5️⃣ Final conclusions draw karna

➡️ Example: Environmental monitoring project me water quality data verify karna

2. Data Quality Indicators (DQIs) – “Data ki health report”

Main indicators:

• Bias

• Precision

• Accuracy

• Comparability

• Completeness

• Representativeness

➡️ Simple words me:
“Data kitna trustworthy hai?”

3. Data Quality Objectives (DQOs) – “Data ka purpose define karna”

Definition:
Qualitative + quantitative statements jo define karte hain:

✔ Kaisa data chahiye
✔ Kitni quantity chahiye
✔ Kitna error acceptable hai

4. DQO Process – “Planning tool before data collection”

Scientific planning tool jo decide karta hai:

• Sampling plan

• Analysis method

• Required accuracy

• Decision criteria

➡️ Ye step ignore kiya to pura data useless ho sakta hai.

5. Data Reduction – “Raw data ko useful banana”

Definition:
Raw data ko calculations, averaging, graphs, standard curves ke through simplify karna.

⚠️ Note:
Data reduction irreversible hota hai (detail loss hota hai)

6. Data Usability – “Final data use ke layak hai?”

Check kiya jata hai:

• Accuracy ok?

• Completeness ok?

• Representativeness ok?

➡️ Tab hi data decision making me use hota hai

7. Data Validation – “Quality criteria pass hua ya nahi?”

Definition:
Procedure to verify whether data meets DQO-based acceptance criteria.

➡️ EPA guidelines ya project-specific rules follow kiye ja sakte hain.

8. Deficiency – “Procedure ya item me fault”

Unauthorized deviation ya defect.

➡️ Example:

• Wrong sampling method

• Missing calibration

• Documentation error

9. Demonstrated Capability – “Supplier ka proof of ability”

Vendor ya contractor apni capability prove karta hai:

• Test reports

• Certifications

• Trial runs

10. Design – “Planning + specification + performance requirement”

Includes:

• Drawings

• Technical specs

• Design calculations

➡️ Example:
Pump foundation design, pipeline layout

11. Design Change – “Approved design me modification”

Any revision in:

• Drawing

• Specification

• Technical requirement

➡️ Always documented approval required

12. Design Review – “Design ka quality check”

Team evaluation jisme:

• Designer

• Client

• QA representative

Check karte hain ki design workable hai ya nahi.

13. Detection Limit (DL) – “Kitna minimum detect ho sakta hai?”

Lowest concentration jo instrument detect kar sakta hai.

Types of Detection Limits:

🔹 Instrument Detection Limit (IDL)
Instrument ki minimum detection capacity

🔹 Method Detection Limit (MDL)
Complete method including sample preparation

🔹 Practical Quantitation Limit (PQL)
Reliable reporting limit (MDL × 3 to 10)

🔹 Reporting Limit (RL / LLOQ)
Calibration curve ka lowest reportable point

14. Distribution – “Data ka pattern”

2 meanings:

1️⃣ Environmental contaminant ka spread
2️⃣ Statistical probability distribution

15. Document Control – “Documents ka proper management”

Includes:

✔ Creation
✔ Approval
✔ Revision control
✔ Storage
✔ Retrieval

➡️ ISO audits me ye critical hota hai.

16. Duplicate Analysis – “Precision check in lab”

Same sample ko 2 baar analyze karna.

➡️ Result close hai = good precision

17. Duplicate Samples – “Field + lab precision check”

Same source se 2 samples leke full process run karna.

➡️ Used to check total method variability