– within the UN 2030 Agenda Framework for the Sustainable Development Goals
The proliferation of a dense ecosystem of technologies and a radical change in the volume, variety, quality and speed of data generated on people, governments, economies and the environment has led to a salvo of new information (and storage, access and analysis techniques) without precedent in history (UNDP, 2016). Data is rapidly transforming society and the way the world operates. It requires huge opportunities – as well as challenges – to improve the livelihoods of people around the world (UNDP, 2016). Access to data and data analytics has the potential to radically improve service delivery, public administration and accountability of governments and businesses (Sachs, 2015). According to the UNDP, governments around the world are estimated to have already posted over one million datasets on the internet (IDRC, 2013). However, only a small proportion of these datasets come from developing countries. This highlights the lack of information in some parts of the world, despite data depletion in other parts (UNDP, 2016).
New data collection and monitoring technologies are rapidly becoming available. These innovations will dramatically enhance the ability of national statistical offices and the international community to monitor the effects of development programs, as well as inform their planning and implementation.
“The vibrant data revolution movement should seize the opportunity to radically strengthen national statistical systems in the region, focusing on fundamental issues of the political economy that have slowed down data progress for decades.” United Nations Report on Sustainable Development, 2015: P8).
Critical data for global, regional and national development policy-making is still scarce. Many governments still do not have access to adequate data on their entire populations. Although most data is usually public, it is not always easy to assess, and mining it for relevant insights may require technical expertise and training. Making good use of big data will require the collaboration of various actors including data scientists and practitioners, leveraging their strengths to understand the technical possibilities as well as the context in which insights can be applied in practice (Maroof, nd ).
Effective use of big data would require changes in the decision-making process, which usually relies on traditional statistics. Given the high frequency of big data, a more responsive mechanism will need to be established that allows government to process information and act quickly in response (Maroof, nd).
Furthermore, it must be recognized that data is a prerequisite for delivering the 2030 agenda for Sustainable Development. The Development Cooperation report (2017) produced by OECD highlights the importance of data for development because of the quality, timely and disaggregated data that are essential for the ultimate achievement of development goals – improving people’s wellbeing and fighting poverty.
Investment in statistical systems needs to become a strategic priority for developing countries such as Guyana and development cooperation providers alike. Development cooperation can help developing countries produce and use more and better data in a responsible and transparent way for good policy outcomes (OECD, 2017).
The OECD has introduced six concrete actions that can bridge the data divide for sustainable development:
1. Make laws, regulations and statistical standards suitable for evolving data needs.
2. Improve the quality and quantity of data funding.
3. Promote statistical capability and data literacy through new approaches.
4. Increase efficiency and impact through data compacts or other coordinated country-led approaches.
5. Invest in and use country-led outcome data to monitor progress towards the Sustainable Development Goals.
6. Generate and use better data to help understand the overall state of SDG funding.
National Government Actions:
* Increase national budget allocations for the development of national statistical systems.
* Maintain a high-level commitment to participate in the data revolution and monitor SDGs by improving coordination across government and opening datasets related to the SDGs.
* Carry out an assessment of current capacity to meet SDG monitoring expectations and integrate needs into NSDSs.
* Prepare robust NSDSs or SDG-aligned national road calls to strengthen country-specific capability to monitor the SDGs. Clear maps, along with realistic budgets, are needed for domestic assistance and to coordinate with donors.
* Incorporate new data collection tools and technologies into SDG monitoring frameworks, such as ground observations, geospatial mapping, new sensors, and mobile-based data.
* Participate in ongoing global processes, including the Statistical Commission, IEAG-SDG, and dialogue on a new global partnership, to ensure that each country’s unique perspectives and needs are well addressed through global cooperation and action.
Guyana is about to embark on an era of transformative development of its economic landscape and is maturing to become the leading investment destination in Latin America / Caribbean and South America, if not the Western Hemisphere.
As a signatory to the UN 2030 Agenda on Sustainable Development Goals, it is essential that the development path taken by the policy maker is aligned with the SDGs. Guyana has a huge gap to fill where data-driven policy is concerned – there is a lack of data collection to assist in this. The data is not necessarily available, but rather, there is a lack of concentration because most agencies are still largely manual.
For example, the Guyana Finance Authority collects large amounts of data but these data are collected and published. It is essential, therefore, moving forward that data for development planning needs to be linked to a national digital transformation strategy and / or ICT strategy that needs to be implemented quickly. More so, agencies like the Guyana Bank, and the Guyana Finance Authority that sits on an abundance of data, and the Guyana Office of Statistics, need to work together on this.
About the Author: JC. Bhagwandin is an economic and financial analyst, lecturer and business and financial consultant. The views expressed are his own and do not necessarily represent the views of this newspaper and the organizations it represents. For comments, please send to [email protected]