sharing experiences with mobile devices data collection in uganda 0

SHARING EXPERIENCES WITH
MOBILE PHONE DATA COLLECTION
IN UGANDA
FLAVIA KYEYAGO OUMA

UGANDA BUREAU OF STATISTICS
14th October 2015

REGIONAL WORKSHOP & CONFERENCE ON THE USE OF MOBILE TECHNOLOGIES FOR STATISTICAL PROCESSES; UNITED NATIONS
CONFERENCE CENTER, ADDIS ABABA, ETHOPIA; 13-16 OCTONER 2015


CONTENTS





Introduction



Pre Mobile CIS issues



Design & Methodology



Data collection and Extraction



Lessons



Benefits



Challenges



Conclusions

.


INTRODUCTION


Mobile Data Collection (MDC) - use of mobile phones, tablets or PDAs for data collection.



Many platforms that can be used to design surveys to collect specific data i.e statistical
data, photographs, data from a preselection, voice recordings, GPS coordinates, etc.



Platforms vary in ease of use, cost, and features.



Some requirements that must be defined .


sample sizes, budgets, technology services



data quality requirements.



Variances in the interfaces, server side components like databases, data reporting and
management interfaces and available technology services and infrastructure



Mobile Data Collection Application Trends:


Development of Native applications installed on the data collection device



Use of USSD as the messaging framework to send the data to server via SMS



The use of the browser based software to collect and send data to an Application
server


INTRODUCTION


In 2008 , GOU, started a programme called the Community
Information System (CIS)



The main objective was to


collect Administrative data



empower communities to make informed decisions using
readily available up to date information.



The CIS was first implemented in 2009 in about 50 districts



Multisectoral approach and UBOS was in charge of data
processing



used paper based questionnaire and



a system for data entry was developed



However, there were many challenges experienced that
included technical and non technical issues that led to the
exercise stalling


Pre Mobile CIS issues










Infrastructure limitations no - In 2011, the growing use of mobile
electricity and room at Sub- phones pushed the IT team to
innovate and experiment the use pf
counties

mobile phones on the CIS project

Limited HR for entry even at
-The developed a web based solution
both Sub county & district
level
which could be accessed through the

web browsers that are native on the
Entry required long term
employment not sustainable mobile phone
Data delays and data
obsolete yet wanted real
time data for planning at
that level
lack of integration of the
data

-Was

done with the objective of
introducing the alternative of MDC
-Reduce

on

some
infrastructural limitations

of

the


Design & Methodology


The Web application was designed by the IT Team at UBOS using previous
experience



This web interface is accessed through phones with web browsers.



Why Web - web is ubiquitous in nature and can be accessed by any device,
anywhere, anytime



Scope: 5 Modules with about 25 questionnaires, that included administrative
data on health, education, financial institutions, general operations



Technology and Application: mobile device phones with sim cards, Designed
using HTML5, CSS, PHP and Java Script for the front end & Mysql for the back
end.



Server was configured at UBOS § IT team monitored data transmission,
aggregation and extraction




Design & Methodology


The conceptual stages involved


designing the form,



deploying the Form on the server,



deploying the form on the device,



collecting data, sending data to
the server and



downloading the data from the
server and analyzing the data.



the Client module - functionalities of
getting blank forms from the web server
to a mobile phone and also filling the
forms and sending the forms to the server.



allows for setting logical question flow–
thereby making non-applicable questions
hidden from enumerator,



Administration Module : for data
management , data reports, data
exportation, data visualization


Data collection & Extraction


Testing : 3 Districts (Urban/Rural)



Training : Done at the Sub county level

are able to submit the data and get a notification



Staffing

message that the data has been submitted.





Enumerators – CDOs – Parish and Village



Supervisors – District Planners & Population



Supervisors – UBOS



Rolled out to date in about 12 districts



access

to

the

application

is

done

through the browser, with user name &
Password




No data is stored on the phone.



Set validation checks are programmed into the
system for answers entered ( logic skips)



some data cleaning is already completed due to
these features built into the system



system is real time it allows for prompt review of
data quality and makes auditing much easier.



Data can be exported to different formats: CSV,
Ms Excel

Data is captured via the mobile client
and sent via the internet using mobile
data transmission technologies (edge or
GSM) to a central server at UBOS.

Validation is done on the phone before the data
is sent to the server.

Officers


Once a user has filled in the questionnaire, they


MCIS Project planning

Tasks
Project Planning

Duration
6 months

Proof of Concept (3 districts)

3 weeks

Design & Testing by the UBOS IT team

10 weeks

Deployment and Training

5 days

Data Collection

10 days

Generate Draft Data Collection Report

2 days


Lessons
Piloting and iteration are critical





  Security



Decide on the course of actions





target data collection efforts to the needs and



Project planning



The team should plan way in advance in

usage the CIS


eliminated the fears of the government

order to loose any time factors

officials


Technology and Team




Composition of the team ( IT & Statisticians) .



System should be fully developed



before the actual data collection exercise
where possible

 Training and Support




 



.

4 days of In-depth training of enumerators

Data integrity and security



Learning curve

and supervisors (questionnaire/System/Trial )



enumerators using the phone for data entry

and continuous support



For the development team


Benefits/ Results
Reduced time





Faster, received in real time



of data collection impacting on presentation of findings



the combination of Data extraction and data entry Processes



Provision of real time data and improved data monitoring process



Reduced cost


More innovation which has lead to more capacity built and Adoption





  More support from management, more awareness, training support

Sustainable system that can obtain data on a regular basis







reduced paper use , storage space and paper waste

.


Challenges


Fears to move from PAPI to CAPI – keep adopting and improving



Lack of Policy on Mobile phone use -



Training the CDOs – slow learning curve, emphasize key point & give support



Internet Connectivity


Poor network coverage

- change sim cards to the network that is available/

adding an offline mode .


Battery life


Phone batteries would not last the whole day


– charge with the local area centres and also some have backups and others would use their
phones.



 using the in-built touch keypad


size of keypad especially for a very long questionnaire was seen a problem



Errors



small keys -correcting mistakes -decimal point



Data sharing to other MDAs is not yet very feasible


Conclusions
Policy Issues
With

the
increasing
data
demands, NSOs should put in
place policies that support
mobile phones usage
Budgeting

and planning
such projects is important
Capacity

building

benchmarking
encouraged

for

and
should

Infrastructure issues



Expand the use of Mobile phones to



Push for more support and collaboration from
developing partners and TRIs



Do more research on the best platforms
(Cross sectional and long term surveys)



Distinguish factors responsible for error rates



Measure the CBA by carrying out the same
survey with both Paper & Mobile for
comparison purposes

Data Management issues




Network connectivity shortcomings –
consider using off line platforms



Research on mobile GSM Terminals
that can expand network coverage
(PPPs)
.

Management of the full data production cycle
to dissemination and archiving stages should
considered