There are 17 Sustainable Development Goals and 169 indicators to measure them.

For each goal, these indicators will track achievements and shortcomings. So, it comes without saying: the accuracy of these indicators is of utmost importance in realising the SDGs.

The accuracy of the indicators is dependent on two specific factors.

First, the quality of the indicators themselves. Are they accurately measuring the complexities of individual countries? Second, the quality of data systems that are available in a country to collect and analyse reliable data.

A good data system will address both these issues to create the skeletal structure necessary for the success of the SDG’s.

Let me explain.

Goal 17 of the SDG aims to “Revitalise global partnership for sustainable development”. A major component of this goal will require individual countries, multilateral agencies and donors agencies to come together to create robust data systems.

The two major indicators which will monitor the progress in the direction of  Data, monitoring and accountability are

• By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts
• By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countries

The UN council in its meeting in Cape town discussed many of these and has come up with key strategies to help countries with their data system.

These strategies include strengthening national data systems, improving coordination between the national statistical systems, regional and international organisations active in the production of data and statistics for sustainable development.

A key component of the strategy includes modernising data systems of countries by encouraging changes in policy regarding data systems and standards.

Creating efficient and reliable data systems is both an expensive and an arduous task.

Besides the cost of setting up basic infrastructure such as computers, software, internet connectivity and buildings to house these components there is the additional cost of human resources. Data collection, analysis and management are extremely technical activities which require highly trained individuals to perform them efficiently.

Many low-income countries may not have these resources readily available and building these capacities will take both time, effort. Building a strong human resource component also has the additional cost of building institutions to train people for these roles.

In their report Data for Development The Sustainable Development Solutions Network (SDSN), estimates that a total of US$1 billion per annum will be required to enable 77 of the world’s lower-income countries to catch-up and put in place statistical systems capable of supporting and measuring the SDGs’. This will require donors to not only maintain their current contributions to statistics, of US$300 (approx.) million per annum, but go further by leveraging US$100-200 million more in Official Development Assistance to support country efforts.

These costs exclude the cost of monitoring and evaluation.

This is an expensive investment and in some ways, it may be difficult to justify spending this amount on data and statistics instead of spending it on intervention which directly help people. It is a difficult decision to make for any country given its strong ethical conundrums.

The final decision of making the choice will have to lie with the country and cannot be imposed from an external source. Yet, there is a need to have good and reliable data to measure change. and how can these two needs be harmonised?

One of the ways of doing this would be to emphasise the need of good quality data and establish the relation between data and good outcomes. This could be done by providing examples of successful data driven intervention within the country. In India in the state of Maharashtra a data driven intervention where every child was measured was successful in reducing stunting.

The other major issue with data is ensuring the quality of data when proper data systems and protocols are not in place.

If the quality of data is poor then the entire exercise of collecting and analysing it is of no consequence. Once a good data system is established and people are trained to manage data this issue will be inconsequential but in the mean time when none of these resources are available how can this be tackled?

In many low-income countries, the shortage of educated workforce means that the limited workforce is expected to perform addition roles.

For example, school teachers and frontline health workers are routinely expected to collect data for various national surveys which hampers their work. Collection of data requires visiting tough and inaccessible geographical areas. The damage done by this is the very people who need to improve SDG goals are not able to fulfil their work.

Meanwhile, simple solutions could help deal with data needs for SDG’s. This could be done including SDG indicators within the current national surveys of the country which would reduce some of the cost of data collection. Secondly, data from different existing sources such as school registers, health records could be triangulated to get the information required. Triangulation will allow for cross referencing and thus ensure the accuracy of data.

These are temporary solutions for the problem but eventually every country will need to work toward the more permanent solution improving their data systems. In the time required to build data systems some of these solutions will help countries tide across their data deficits.