Leveraging Big Data Analytics for Africa’s Sustainable Development

Leveraging Big Data Analytics for Africa’s Sustainable Development

Data analytics, a relatively new word, but old concept in the field of Data Analysis is more likened to a revolution which draws on existing and new sources of real-time information to fully integrate statistics into decision making, promote open access to, and use of, data and ensure increased support for statistical systems. Data analytics Leans on the assumption that the emerging concepts of “open data” and “big data” could be leveraged as part of the broader development data revolution.

While “Open Data” is data made open to the public mostly from (but not limited to) government, “Big Data” is large source of data classified based on velocity, variety and volume. This data is rapidly increasing and thus the need to leverage on her real-time nature of information flow to make smarter decisions.

Data has been reported to be the engine of decision making towards achieving the Sustainable Development Goals (SDGs); otherwise called: The Global Goals. Therefore, chances of achieving the Global Goals lies in how this data are analysed and interpreted. Accessing, using and benefiting from data is assumed and expected by a huge population in the world and this strongly includes minority and indigenous groups. Therefore, the data generated as well as the output remains of critical importance due to the immense impact it can have in policy making surrounding the SDGs which aims at leaving no one behind. When integrated and analysed into broader data use for decision making directed at SDG indicators, it can become an important asset. Thus, securing development that is more sustainable, socially inclusive and global.

Studies on African Countries have shown lots of deficits in data collection, not to mention big data. In fact, some countries are yet to see reasons for data collection, rather, they resort to making estimations. Thus, they possess a threat on data driven decision making  thereby leading to further error. As Center for Strategic & International Studies (CSIS) reported that a key stakeholders’ interview revealed that the United Nations Funds for Population Agency conducted a census in Myanmar, (although an Asia country but with typical characteristics of most African countries) and recorded 51.5million citizens as against 60 million which was earlier reported based on mere estimates. This shows a broad disparity which can mar or make effective policies. This is peculiar to most African countries (with emphasis on Nigeria) and the developing world at large.

The UN’s Secretary-General’s Independent Expert Advisory Group on a Data Revolution

for Sustainable Development (IEAG) has amplified efforts in calling for action to mobilise the data revolution for sustainable development. Numerous actors which includes member states have recognised the crucial role of increased support for strengthening data collection and capacity building and being committed to addressing the gaps in data collection for the targets of the 2030 Agenda to better inform the measurement of progress. This signifies the importance of drawing on new data sources to meet user needs. Sadly, recent studies show that African nations are still peddling the same boat in the sense that African government institutions as well as her private sector still show a strong lack of dissemination or sharing of data for the private sector, to inform their decisions. 

  •   Adopting the Data Revolution

Unlike the common perception of “revolution” as being relatively quick, broad and transformational, the data revolution is slow, country context specific but incremental.  It is not likely that African countries’ projects using data will get this right the first time. It will be a matter of testing, re-testing, adjusting and learning. The point here is not to experiment all day in boutique labs with little regard to impact, but rather to integrate experimentation and adaptation at the heart of how we implement at scale.

  • Domesticating the SDGs and Data Analytics

Nigeria is among the countries that has started assessing how to domesticate the SDGs’ targets in national development and poverty reduction strategies such as her National Economic Recovery Growth Strategy,

Also, Rwanda is also trolling the same path through domesticating the SDGs’ target into National programmes such as Vision 2020, the Economic Poverty Reduction Strategy (EPRS) and the Sector Strategic Plans (SSPs) and the District Development Plans (DDPs) at the local government level.  Few other African countries

Data Analytics would aid in tailoring community specific solutions to specific needs and challenges because data been collected via channels such as mobile devices, satellite earth monitoring systems, geographical information systems track real-time locations and time. This type of data (Big Data) offers a cheaper and more effective alternative to traditionally costly census and household surveys to gather information which will be valuable for urban, transportation planning, social intervention programs, monitoring demographic changes, flood, developing disaster risk reduction strategies amongst others.

While the subject of Big Data may seem an interesting area to explore, it is important to bear in mind that the application of big data in the policy making discourse must be harnessed with the ultimate objective of benefitting Africans and the Africa continent towards achieving the Sustainable Development Goals (SDGs).

This domestication process goes beyond an assessment of whether the SDGs’ targets are reflected in national development strategies. Instead, it entails utilising and integrating this data in implementation towards achieving the 2030 development goals.

  • National Development Strategies and Data Analytics

According to Deloitte report (2013), the utilisation of public data reduced death rates among cardiac patients in the United Kingdom and that the total economic value of these types of data just in the United Kingdom was around five billion pounds per year.

It is therefore apparent that if African countries adopt this pattern of data utilisation, it would yield more promising rewards and results, considering her double or quadruple populations compared to those in the United Kingdom.

National development plans should be convened to mobilise disparate parts of government around a unified data plan. Emphasis on data sharing linked to internationally recognised standards like the IMF would spur National governments in making efforts in considering sharing data.

Rezinde T. et al (2018) organised a semi-structured interview with 120 Rwandan establishments by institutional sector selected randomly in Kigali city. . The research revealed that majority of the respondents which comprised of decision makers did not understand the need to use statistics in their activities as well as decision making while others felt overwhelmed by the volume and complexity of the data

Their study also found that those who used statistics in running their businesses found them extremely useful and indispensable while those who did not use statistics to inform their decisions did not see any need for their use. They also reported a strong lack of dissemination/sharing by institutions, especially in the private sector, which uses statistics to inform others about their decisions. Reason being that private sectors did not feel comfortable sharing information that they found sensitive and which they felt the competition could use against them.

For instance, on Universal Primary Education- (Millennium Development Goals 2 and Sustainable Development Goals 4); The aggregate primary school enrolment rate for developing countries (majorly African countries) has been consistently over 90% while only 35% of developing countries were on track to achieve the universal goal by the deadline of 2015. In Tanzania, many children were unable to read or do basic arithmetic at grade2 level after completing the primary grades. While data of this nature may seem displeasing, institutions would need to be abreast that this sums up to identifying our shortcomings or strengths for our own good.

Also, a World Bank study shows that about half of the 155 countries (which comprised majorly of African Countries) lack adequate data to monitor poverty and, as a result, the poorest people in these countries often remain invisible. During the 10-year period between 2002 and 2011, as many as 57 countries (37 per cent) had none or only one poverty rate estimate. Lack of well-functioning civil registration systems with national coverage also results in serious data gaps.

If we do not leverage on Data; irrespective of being big or small, then how do we make effective policies for our development?

  • Case Study

Making cities and human settlements inclusive, safe, resilient and sustainable (SDG 11) is a crosscutting issue across the integrated 2030 Agenda. Most African countries are posed with a pressing desire to addressing this.

In Sri Lanka, LIRNEasia, a pro-poor, pro-market think tank, partnered with multiple telecom operators to gain access to historical and anonymised telecom network big.  Those operators offered access to Call Detail records including Calls, SMS and Internet and Airtime Recharge Records. Through SIM-movements data, new insights can be drawn regarding location and timeline of the population congestion, origin/home location and destination/work location and frequency and quantity of mobile interaction of users within the administrative boundaries of the city. The Colombo District was able to leverage on this real-time information to map out  three spatial clusters to develop policies on land use.

In Laos, the Japanese government initiated the Vientiane Bus Project in 2012. Due to the poor performance, decided to install data collection devices in 2016 for GPS tracking, pictures, passenger count, bus location, routes and traffic analysis in a bd to understand the buses usage to identify inefficiencies. This data was collected via wifi-sensor and this revealed that; Buses did not follow their designated routes, did not stop at designated bus stops, some buses were idle for long periods which cost the country a whopping $US13million per year.

Finding from this data clearly showed that reasons for increased deteriorating air quality, increased death tolls (1,000 deaths in the last year) as well as 15% increase in traffic-rated mortality which was the government’s priority.

  • Leaping through Big ‘Open’ Data Analytics

African Governments (with much emphasis to the Nigerian government) would have to strengthen her statistical institutional capacity. At the international sphere, Word Bank established a Trust Fund for Statistical Capacity Building (TFSCB) in 1999. This has funded more than 200 projects and had impacts in up to 199 countries. On a minor scale, if this is replicated, building capacities of sub-national institutions and partner organisations would strengthen domestication of the Sustainable development targets, reduce bottle necks and hasten development.

More involvement of Private Expert Organisations and think-tanks such as CSR-in-Action should be instituted in SDGs decision making roundtable.

African countries should call for support from international organisations and donors to support such a allocation of new resources for capacity development, especially in low-income countries, aimed at addressing barriers between people and data. Establishment of a SDGs data lab which focuses on visualising and analysing SDG data should be considered.

Lastly, technology companies such as Telecommunication and hardware companies would need to identify market opportunities in supporting sustainability initiatives and development efforts because strategically, their involvements would result in increased use of their products and services.

Embracing Big Data would not only aid achieving sustainable development in Africa but would solve Africa’s major problem- Unemployment because it would create a huge stress of jobs at numerous levels.

References
  1. Bizoza A. (2016), Where Rwanda needs to focus on in the new course of Sustainable Development Goals by 2030? Chronic Poverty Advisory Network Accessed on August 10, 2017 at: http://www.chronicpovertynetwork.org/blog/2016/3/3/sdgsseries-1-where-rwanda-needs-to-focus-on-in-the-new-course-of-sustainabledevelopment-goals-by-2030
  2. Brynjolfsson, E., L.M. Hitt, and H.H. Kim (2011), Strength in numbers: How does data driven decision making affect firm performance? Available at: http://dx.doi.org/10.2139/ssrn.1819486
  3. Center for Strategic International Studies (2017). Harnessing the Data Revolution to Achieve the Sustainable Development Goals- Enabling Frogs to leap. A report of the CSIS Project on prosperity and Development with the JICA Research Institute. Eyol Yayboke, Naohiro Kitano, Daniel F. Runde, Erin Nealer & Charles Rice.
  4. Davenport T.H. (2006), Competing on analytics. Harvard Business Review, 84(1): 98–107.
  5. Deloitte. Market Assessment of Public Information (London: UK Department for Business Innovation and Skills, 2013). https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/198905/bis-13-743-market-assessment-of-public-sector-informatiom.pdf
  6. Dennis, H. (2015), Leave No One Behind from goals to implementation. Available at: http://www.christianaid.org.uk
  7. High-Level Panel of Eminent Persons on the Post-2015 Development Agenda, A New Global Partnership: Eradicate Poverty and Transform Economies through Sustainable Development (New York: United Nations, 2013), 23. https://sustainabledevelopment.un.org/content /documents/8932013-05%20-%20HLP%20Report%20-%20A%20New%20Global%20Partnership.pdf
  8. Hiroko Maeda, MDG2: Accelerating Progress towards Universal Primary Education, “The Data Blog, May 20, 2015. https://blogs.worldbank.org/opendata/mdg2-accelerating-progress-towards-universal-primary-education.
  9. International Telecommunication Union (2016). Harnessing the Internet of Things for Global Development, https://www.itu.int/en/action/broadband/Documents/harnessing-IoT-Global-Development.pdf
  10. Jacques van der Gaag and Vidya Putch, “From Enrollment to Learning, “Brookings Institution, 2016. https://www.brookings.edu/wp-content/uploads/2016/06/01-enrollment-learning-van-der-gaag.pdf
  11. Joel, Gurin, and Laura, Manley (2015). Open Data for Sustainable Development. World Bank.
  12. LIRNEasia (2015). Mobile network big data for urban and transportation planning in Colombo, Sri Lanka” Data for Policy conference, Presenters: Samarajiva,R. Lokanathan, Accessible at: http://lirneasia.net/wpcontent/uploads/2013/09/Samarajiva_Cambridge_June15.pdf
  13. Myanmar’s Census Falls 9 Million Short of Estimate. “BBC News, August 29, 2014. Sec. Asia. http://www.bbc.com/news/world-asia-28990956.
  14. “The 2014 Myanmar Population and Housing Census. Republic of the Union of Myanmar. May 2015. http://myanmar.unfpa.org/sites/default/files/pub-pdf/Census%20Main%20Report%2028UNION%29%20-%20ENGLISH_0.pdf
  15. Philippines: Real-time Data Can Improve Traffic Management in Major Cities, “World Bank, April 5, 2016. http://www.worldbank.org/en/news/press-release/2016/04/05/philippines-real-time-data-can-improve-traffic-management-in
  16. Samarajiva,R . “Using mobile-network big data for urban and transportation planning in Colombo” LIRNEasia (2015). Accessible at: http://www.iesl.lk/Resources/Documents/My%20Docs/Event%20PDF/PL%20L%2016012015.pdf
  17. Segaran, Toby; Hammerbacher, Jeff (2009). Beautiful Data: The Stories Behind Elegant Data Solutions. O’Reilly Media. P257
  18. Statistical Capacity Building, accessed August 31st, 2018 http://worldbank.org/en/data/statistical-capacity-building
  19. Theogene Rizinde, Ferdinand Nkikabahizi2, Leonidas Babamwana3 and Josephine Umutesi (2018) Achieving the Sustainable Development Goals in Rwanda: The Role of Administrative Data Inclusion. East Africa Research Papers in Economics and Finance EARP-EF No. 2018:34
  20. United Nations (2014), p.6. A world that Counts: Mobilizing a Data Revolution for Sustainable Development by the Independent Expert Advisory Group on a Data Revolution for Sustainable Development. New York.
  21. UNECE Statistics Wikis.How Big is Big Data.
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