When I said in my last post that 'One that will be interesting to the general public,
administrators, or researchers, and "must read" for MPT, Ooredoo, and
Telenor our mobile communication providers if they haven't done so is the Mobile Data for Development Primer by
Global Pulse ...', for the must
read part I had in mind the young local staff and not those of managerial
or professional levels, or the expatriates. I suppose ordinary folks could
never be in their league, at least for the time being.
However, when I looked up just now about Ooredoo and Telenor
in Myanmar they seem to have been doing something more than providing telecom
services. I found Ooredoo launching Myanmar Maternity application “maymay” and Telenor
launching "Telenor Light Houses" (Community Information Centers).
The figure below is from 'Using mobile data for development' report available at:
It is good to see that Telenor has been active in the use of
mobile data for epidemic surveillance and disease containment and information
strategies.
We have seen that Mobile
Phone Network Data for Development published in November 2013 by Global
Pulse is a primer on how analysis of Call Detail Records (CDRs) can provide
information for humanitarian and development purposes. It is essentially an
advocacy document for the utilization of metadata from mobile phone calls and
messaging for development. In a nutshell, "the document explains three
types of indicators that can be extracted through analysis of CDRs (mobility,
social interaction and economic activity), includes a synthesis of several
research examples and a summary of privacy protection considerations".
The 'Using mobile data for development' report, produced by the Bill
& Melinda Gates Foundation in conjunction with strategy consulting firm
Cartesian and published in May 2014, covered the same themes as the Global
Pulse Primer, but in considerably more detail. Additionally the first two
chapters give (i) the situation of the adoption and usage of mobile phone in
the developing world, and (ii) what data are captured by the mobile data
systems and how they could be interpreted and used. These would be most useful
to analysts and researchers.
The table of
contents of this report gives a good idea of what to expect:
1. Executive summary
2. Adoption and Usage
of Mobile Phones in Developing Countries
□
Mobile
Adoption Rates Are High in Developing Countries
□
Rates
of Mobile Ownership and Usage Are Equally High among the Poor
□
Usage
Patterns in Developing Countries Differ from Those in the Developed World
The report says that people in developing countries use more
voice and SMS text as compared to data. However in Myanmar because of lack of
alternatives most of us have to use mobile phone for internet connection and so
our data usage could be markedly higher than others. I guess most of us who own
a desktop or a laptop would be using smart phones as wifi hotspots to connect. Before
mobiles and cheap SIM cards became available, I used the slower than slow
prepaid dial-up connection at home which was useless for purposes other than
reading email. If I need to visit the Internet cafes, their speed was not too
much faster. Currently the Wimax and other options are too expensive. While
ADSL by MPT is quite good it is not widely available. I lived in downtown
Yangon and yet when I applied for ADSL, I was told that it is not available for
the whole of my township because some kind of equipment or facility is not
there.
The fact is that if we can't have some reasonably priced broadband
option like cable networks becoming available, we would still be with a virtual
connectivity drought. Meanwhile, MPT, Ooredoo and Telenor should make the data
rates cheaper too and what is better than letting the market take care of it,
for example, by promoting cable companies and startups.
For a people denied of the advantage of good and cheap mobile communication until recently, we would like to see how we compared with others. Unfortunately we aren't there.
I don't know if there is good data on that for Myanmar.
Anyway, from the wiki page 'Demographics of Burma' we take the percent of
population 15 years and over of 72.5% as the proportion of adult population (consistent
with figure-1) and using a growth rate of 1.07% and the total population as of
2014 census at 51,419,420 we calculated the 2012 population. With the estimate
of number of mobile phones as 5,400,000 for 2012 (wiki page, Telecommunications
in Burma; which is quite rough) we get an estimate of ownership among adults of
about 15%.
3. What Is Captured
in Mobile Data Systems?
□
How
a Mobile Network Functions
□
Mobile
Data Captures a Wide Range of Customer Behaviors
Under this topic the three most important type of
information captured are:
(i) "Location
and mobility: Location is tracked passively when users’ phones interact
with towers in each cell cite they visit, and actively, each time a user
initiates a voice call, SMS, or other transaction."
(ii) "The social
network: Calling and SMS patterns create a lens into a person’s social
network, including who they communicate with, how long, and how often. Further,
in many emerging markets the originator of a phone call pays for the minutes of
the call; even understanding who someone calls vs. who calls them can give a
sense of social stature among a social network, important for marketers seeking
to reach nodal hubs of influencers."
(iii) "Recharge
and purchase history: Patterns of recharging minutes and purchases of VAS
(value-added services) can give insights into an individual’s economic
circumstances and the financial shocks or difficulties they face."
□
How
Mobile Data Is Captured From User Interactions
□
How
Mobile Data Can Determine a Person’s Location
□
Gauging
the Availability and Accessibility of Data
□
Understanding
and Interpreting Different Kinds of Mobile Data
There is great potential in that "signal
characteristics such as attenuation and signal distortion can be measured to
provide an indication of local ecology, rainfall patterns, civil construction,
etc. Figure 11 explains how attenuation from radio signals was used to collect
a large amount of accurate and timely rainfall data."
4. Present and
Possible Applications
□
Potential
Insights from Mobile Data
□
Mobile
Operators Are Beginning to Exploit Mobile Data
□
Current
Uses of Mobile Data in Development Programs
□
Future
Opportunities to Leverage Mobile Data
□
Should
Mobile Data for Philanthropic Use Be Free?
"The United Nations Global Pulse has put forward the
idea of 'data philanthropy,' where operators would have a duty to share data
for certain limited uses when the public good is urgent and clear. Global Pulse
argues that these cases actually make business sense ... "
5. Regulatory
Landscape and Data Privacy Considerations
□
Regulatory
Regimes Are Becoming Clearer and More Standardized
6. Considerations for
Data Sharing
□
Sensitivities
around Mobile Data Access
□
Commercial
Sensitivities around Data Access
□
Public
Opinion Plays a Role
□
Approaches
to Protecting Data Privacy
7. Conclusion
"Mobile data has
enormous potential to support development efforts and through this to improve
the lives of poor people around the world. ... Mobile data offers larger and
more representative samples, in near real-time, and at far lower costs than
alternative means of data gathering. Indeed we believe the opportunities to
leverage these data sets for development goals are only starting to be
explored. ... There is an opportunity to learn from the best examples that have
been demonstrated to develop a foundation for broader use of mobile data ... ".
It seems clear that mobile
CDR data has enormous potential for development applications. To enrich and
accelerate research efforts, third party and government data sets will have to
be combined with those of the mobile CDR data. That means those areas can't be
neglected and they need to develop along with big data. To be able to generate
useful CDR data, mobile communication service penetration into the population
and rural areas have to be sufficiently high, though urban related, or some
other applications may be feasible earlier. At present, big data requires big
hardware and perhaps big brains and to round them up—big money. So, it will
again be the old story: when some opportunity opens up, a handful of people who
could take advantage of the situation would take all. But when you are lagging
behind and you want to leapfrog, you would need more than the critical mass of
smart people needed just to keep the machines running.
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