Data Management
Module 2 Session 5
1
Overview
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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
2
Objectives
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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
3
Epi-Info vs MS-Access
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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
4
What is “Data”?
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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
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plot
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cobwt
17.7
19.9
28.3
26.63
24.88
25.46
grainwt
811
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carbon
2.6
2.54
2.83
2.41
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2.47
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What is “Meta-data”?
Meta-data
 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”
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What is “Information”?
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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
7
What is Data Management?
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DM is concerned with “looking after” and
processing data – it involves:
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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
8
Why is DM important?
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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 :
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Improved processing efficiency
Improved data quality
Improved meaningfulness of the data
9
Data Management Problems
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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
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Activity 2
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Case Study Discussion
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In small groups discuss the two examples
Have you encountered similar situations in your
own workplace?
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Some steps in DM
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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
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Project Life Cycle
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From Problems to Knowledge
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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
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Scope of Data Management
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Non-Electronic Data
Management
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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
16
Electronic Data Management
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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
17
Documentation
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Documentation should be part of the project
planning:
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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.
18
Archiving
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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:
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Schedule for data sharing
Formal of final dataset
Documentation to be provided
Analytical tools to be provided if any
Mode of data sharing
19
Household Survey Archive
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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
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Browse to http://www.ubos.org/ on the web,
and follow the link to Survey Documentation
Or
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On the UBOS Resources DVD, browse to
drive:/UBOS-UNHC2-CD-image/index.html
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Online Survey Archive
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Online Frequency tables
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Activity 4
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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?
23
Building a DM Strategy
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Good DM is not something that will “look after
itself” or evolve is left long enough
A DM strategy requires:
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Commitment
Skills
Time
Money
24
Data Management Plan
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A DM plan will include:
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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
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For example can you still read 5¼” floppy disks?!
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Roles and Responsibilities
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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
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Hints on building a DM
strategy
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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
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Activity 6
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In groups discuss what would be a feasible
data management strategy in your workplace
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