1. Data Management
Module 2 Session 5

2. Overview
This session considers the role of Data Management (DM) within the Project Life Cycle
It is important to plan effective DM at the project planning stage
We show how negligence in DM can results in wrong decisions being made at policy level

3. Objectives To introduce the basic concepts of Data Management
To identify the stakeholders in Data Management
To outline the stages and levels of Data Danagement
To equip participants with skills to manage their data

4. Epi-Info vs MS-Access Epi-Info creates an Access Database file
It is easy to learn and easy to use
MS-Access is more flexible e.g. in terms of designing data entry screens (show demo)
BUT: MS-Access has a steep learning curve
Consider engaging an expert for more complex data structures/surveys

5. What is “Data”? Data can be defined as “individual measurements”
They are the individual records in a questionnaire
They are the “raw materials” in a field or laboratory research activity
block
plot
cobwt
grainwt
carbon 1
17.7
811
2.6 2
19.9
136
2.54 3
28.3
172
2.83
26.63
148
2.41
24.88
170
2.39
25.46
158
2.47

6. What is “Meta-data”? So meta-data turns raw data into “information”
Is data about the data
Describes the dataset
Enables effective management of the data resources
Allows the dataset to be fully understood
Is an essential part of the data documentation
So meta-data turns raw data into “information”

7. What is “Information”?
Information is processed data from which conclusions can be drawn
Information is a valuable resource for decision-making and for planning
It is the results of processing, gathering, manipulating and organising data in a way that adds to the knowledge of the receiver

8. What is Data Management?
DM is concerned with “looking after” and processing data – it involves:
Looking after field data sheets
Entering data into computer files
Checking and correcting the raw data
Preparing data for analysis
Documenting and archiving the data and meta-data
DM is the consolidation of data (and meta-data) in a way that is easy to manipulate, retrieve and maintain

9. Why is DM important?
Ensures data for analysis are of high quality so that conclusions are correct
Good DM allows further use of the data in the future and enables efficient integration of results with other studies
Good DM leads to :
Improved processing efficiency
Improved data quality
Improved meaningfulness of the data

10. Data Management Problems
Lack of skills – inability to use software or set up data checking procedures
Multiple copies of files
No one with responsibility for checking data
No clear policy on archiving or making data available
Lack of documentation
Multiple entry of the same data
Hand pre-processing of data

11. Activity 2 Case Study Discussion
In small groups discuss the two examples
Have you encountered similar situations in your own workplace?

12. Some steps in DM Designing field data collection sheets
Collecting data with appropriate quality control
Checking raw data
Data entry and organisation of computer files
Backup of files
Processing of data for analysis
Checking of processed data
Archiving data and meta-data for future use

13. Project Life Cycle

14. From Problems to Knowledge
Formulate (fm) the objectives
Develop (dv) the protocol
Design (ds) the observation units
Collect (coll) the data
Compile (cm) data into well-structured datasets
Query (qy) to select subsets
Analyse (as) the data
Publish (pb) the results

15. Scope of Data Management

16. Non-Electronic Data Management
Handling of questionnaires in the field (both completed and blank)
Storage of questionnaires – protection from weather, termites, etc.
Movement of the questionnaires – who has access to them
Editing and coding
Scanning – is this an option? – Budgetary implications

17. Electronic Data Management
Designing data entry system
Data entry – including double entry
Data cleaning – consistency checks
Data security – regular backups – where are backups stored?
Storage – how safe is your data?
Documentation

18. Documentation Documentation should be part of the project planning:
File Structure – how will the data be organised
Naming conventions – for files and variables
Data integrity – what checks are in place
Dataset documentation – how will this be produced
Variable construction – what variables will be constructed following data collection; how will these be documented
Project documentation – how will you document decisions taken on field procedures, coding, etc.

19. Archiving
Many funding agencies now specify that the data be made public at the end of the project
Plans for archiving should be included in the project proposal and must be fully costed
Proposal should include:
Schedule for data sharing
Formal of final dataset
Documentation to be provided
Analytical tools to be provided if any
Mode of data sharing

20. Household Survey Archive
Data from the Uganda National Household Survey 2002/2003 are available online
The online archive was created with the International Household Survey Network Microdata Management Toolkit
Either
Browse to on the web, and follow the link to Survey Documentation Or
On the UBOS Resources DVD, browse to drive:/UBOS-UNHC2-CD-image/index.html

21. Online Survey Archive

22. Online Frequency tables

23. Activity 4 In groups discuss the steps involved in data management
Identify who commonly undertakes which task in your District Office
What does each task involve?
What resources – skills, equipment – are needed?
How are paper records stored in your workplace?

24. Building a DM Strategy
Good DM is not something that will “look after itself” or evolve is left long enough
A DM strategy requires:
Commitment
Skills
Time
Money

25. Data Management Plan A DM plan will include:
Clearly defined roles for staff
A regular backup procedure
Details of data quality checks
Including DM on the agenda of project meetings
Procedure for upgrading software
Details of how archive is to be produced
Details of how the archive is to be maintained
For example can you still read 5¼” floppy disks?!

26. Roles and Responsibilities
Organisers – handle raw data on a daily basis. They set up data filing systems, enter and check data and maintain data banks
Analysers – analyse and interpret data, reducing raw observations to useful information
Managers – responsible for providing an enabling environment for the first two groups and ensuring all commitments to stakeholders are met

27. Hints on building a DM strategy
Document current procedure
Seek consensus
Establish a data management forum
Standardise – use similar DM plans for all projects
Obtain funding – include DM plan in project proposals and budget for it

28. Activity 6
In groups discuss what would be a feasible data management strategy in your workplace