MIDLANDS STATE UNIVERSITY
Faculty Of Arts
master of arts in monitoring and evaluation
Name : Munyaradzi Zinyama
Reg : R154048v
Module : Monitoring and Evaluation Data Management
Module code : 701
Lecturer :MR. SISIMAYI
Question : With the aid of Data analytics cycle. Discuss the concept of data
management in project Monitoring and Evaluation.
Introduction .
The purpose of data analysis in monitoring and evaluation is to transform raw data
into useable information by interpreting data to generate information useful for
decision making. The data analytics cycle shows the various stages in which raw
data is transformed into usable evaluation data .Data analysis is defined by
International Red Cross (2012) as the process of converting collected (raw) data into
usable information. On the hand, Kumar R (2004) defines it as the computation of
certain measures along studying the tabulated material in order to determine inherent
facts or meanings. Another definition is provided by Kothari (2004) who view it as
the manipulation of qualitative and quantitative data by summarising, performing
calculations, comparing, and using other data analysis methods to describe,
understand and see patterns of relationships among data groups and between data
over time. Kothari (ibid) goes further to state that in the process of data analysis in
a general way involves a number of closely related operations which are performed
with the purpose of summarising the collected data and organising these in such a
manner that they answer theresearch question(s) or (provide answers to the
programme/project objectives and indicators).It involves breaking down existing
complex factors into simpler parts and putting the parts together in new
arrangements for the purpose of interpretation.
Discovery
This is the first stage of the data analytic cycle. It involves collecting from various
data sources. In this stage problem identification is done which is crucial for
monitoring and evaluation because to properly analyze M&E data we need to fully
understand the problem being addressed by the intervention and its context .In this
stage we identify various data sources that are available and needed to provide the
raw data that can be used for monitoring and evaluation .This stage includes the
formulation indicators and tools that will be used in the collection of the data and
also the formulation of hypothesis or evaluation questions that will be used to test
the data discovered .
Data preparation
In the second stage of the data analytic cycle the collection, processing, and
cleansing of collected data is done. During this stage, data is collected from the
various data sources using different data collection methods. This stage is important
in data quality assurance because it makes sure that all the data quality parameters
are met this include timeliness , completeness , accuracy , duplication
and
consistency are met by identifying outliers and anomalies in the data and sorting
them out .
Model planning
In this stage we identify methods, techniques and workflows to follow in the
analysis of monitoring and evaluation data. This stage also involves data
warehousing and the categories to sort the data in categories that will make it easy
for analysis. This stage examines the relationships within information.M&E issues
on intervention can be understood by logically classifying the data into such
categories as the process and effects of an intervention. Tables and flow-charts can
lend their usefulness to identify those categories and explain the relationship
between them.
Model building
In this vital stage of the data analytic cycle we develop testing , testing and training
data sets .The stage involves identifying tools which are appropriate for running the
models and the models are executed . At this stage this is when we make sense of
the evaluation data .Such analysis is used to convey readers to those especially (those
who might use the qualitative data) the whole picture of the intervention including
what is happening in the area, analyzing data to assess whether and how the program
has achieved its objectives , how the stakeholders are perceiving the intervention,
and in what specific activities or events are being implemented. Therefore the
relevance of data analysis in M&E can not be overemphasized here.
Results communication and publication.
This stage determines whether the project was a failure or not by comparing desired
outcomes or targets by the results. In this stage Monitoring and Evaluation
information is disseminated to various stakeholders this means the data must be
articulated to meet the various information needs of stakeholders. Data Presentation
presentation of data is a key aspect of analyzing of data which seeks to effectively
present M&E data so that it highlights the key findings and conclusions and to makes
such data or results more illustrative. Since there are varied consumers of this data
it is critical that it packaged with its ultimate user in mind and as such there is need
to ensure that the visual presentation of data is simple and easy to understand. For
example, use of pie-charts, histograms and pictographs. A useful question to answer
when presenting data in M&E is, “so what?” What does all this data mean or tell us
– why is it important? It is important to narrow down answer to the key conclusions
that explain the story the data presents and why it is significant. Some other key
reminders in data presentation include: Make sure that the analysis or finding you
are trying to highlight is sufficiently demonstrated. Ensure that data presentation is
as clear and simple as accuracy allows for users to easily understand. Keep your
audience in mind, so that data presentation can be tailored to the appropriate
level/format (For example, summary form, verbal or written).Avoid using
excessively technical jargon or detail.
Operationalize
This last stage is important in the monitoring and evaluation process because the use
of evaluation information makes the process relevant and in this stage the
information that is produced in the data analytics cycle is used in operations .
Reference
Kothari C (2004) Research Mthodology-Methods and Techniques 2nd Edition New
Age International Limited Publishers New Delhi
Kumar R (2004) An Introduction to Social Research-A Step by Step Approach New
Jersey:Prentice Hall