1. Data Quality Quality data collection and management

2. Data Quality2 Learning Objectives By the end of this session, you should be able to: 1. Describe the importance of training data collection and management 2. Describe and appropriately complete the various data fields (including PEPFAR categories and training levels) 3. Demonstrate appropriate use of the forms 4. Identify the necessary steps to ensure data quality at each stage of the data collection and management process 5. Distinguish between data that is complete and correct and data that needs cleaning

3. Data Quality3 Group Discussion: Why Data?

4. Data Quality4 Programme Data  Measures programme inputs and outputs  Helps determine programme outcomes  Use programme data to:  Assess whether the programme is meeting its established targets  Identify and improve problem areas in a programme  Improve efficiency of the use of programme resources  Inform reporting to partners and funders

5. Data Quality5 Data Management  Systems, policies, practices and procedures that manage and organize data for specific needs

6. Data Quality6 TrainSMART  TrainSMART is I-TECH’s open-source, web-based training data collection system  Allows users to accurately track data including:  training programmes  trainers  trainees  Also enables users to better evaluate programmes and report activities to stakeholders.

7. Data QualityTraining Summit 7 TrainSMART Tool

8. Data Quality8 Data Collection  Forms:  Participant Registration Form  Trainer Registration Form  Course Form  The data entry pages have been built to follow our data collection forms that are completed in the field making data entry much easier.

9. Data Quality9 Training Levels  Level 1 (Didactic, Seminar, Lecture)  Level 2 (Skills-building) Group-based Workshop  Level 3 (Clinical Training/ preceptorship— trainer led)  Level 4 (Clinical Consultation—trainee directed)  Level 5 (TA—Tech. Assistance, other than direct care)

10. Data Quality10 PEPFAR Categories  ART  Counseling & Testing  Laboratory  Orphans and Vulnerable Children  Palliative Care (OI, TB/HIV, etc.)  TB/HIV  PMTCT  Policy Analysis & System Strengthening  Prevention  Strategic Information

11. Exercise Data Collection

12. Demonstration Entering Data in TrainSMART

13. Data Quality13 Data Quality  What is quality data?  Complete  Consistent  Timely  Accurate  What influences the quality of data?

14. Data Quality14 Need Quality Data  The quality of the analysis and interpretation of data can only be as good as the data itself  Ensure data is accurate, specific, and complete

15. Data Quality Small Group Activity

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20. Data QualityTraining Summit 20

21. Data QualityTraining Summit 21

22. Data Quality22 Data: “Clean” vs. “Dirty”  identifying incomplete, incorrect, inaccurate, irrelevant parts of the data and then replacing, modifying or deleting this dirty data

23. Discussion Clean or Dirty Data

24. Data Quality24 Data Flow  Data collection and entry should be done in a methodical and defined way  Specific individuals need to be identified to be responsible for each step of the process

25. Data Quality25 Key Points  Good quality training data collection and management is essential for accurate and complete reporting  PEPFAR and Training Categories need to be understood to enter them correctly into the training database.  Training forms correspond with data entry screens in TrainSMART  Data entry processes need to be defined at each training centre and responsible persons identified to manage the data