“Game Over” — When Data Systems Fail
Anyone who has played Super Mario Brothers on the Nintendo Entertainment System (NES) can relate to the dread of seeing the “Game Over” message when the NES characters run out of lives. The good thing is you can restart the game, and newer versions of NES enable you to start from where you left off instead of from the beginning. Nintendo intentionally designed the entertainment system and the games to be flexible, allow for failure and be used by children of all ages, making NES one of the oldest and most successful entertainment systems on the market. The hardware and software have evolved and advanced over time to accommodate user preferences and enhance user experience. A successful fit-for-purpose data system like NES considers the various types of users and their needs, their capacity to use, maintain and interact with the system, and the availability of quality data to feed the system. NES is also scalable, flexible and free from a single point of failure, so “Game Over” is really just a euphemism for a fictitious character’s death — you have opportunities to fail many times, bring your character back to life and start from where you left off.
In a real-world context, creating a data system that incorporates all of the NES design features described above is even more critical, especially when human lives are at stake and there is no starting over. The reality is that data systems are frequently designed without considering these fit-for-purpose elements that result in real-life “Game Over” scenarios. Consider the case of the Indonesia Tsunami Early Warning System (InaTEWS), established in 2018 with aid from donor countries after the devastating 2004 tsunami that killed almost 250,000 people. The InaTEWS system failed due to fundamental design flaws and oversights. First and foremost, the government did not maintain the donor buoy system that would have dispatched advanced warnings based on data gathered from deep sea sources. One costly oversight was the lack of adequate backup or alternative communications to alert the population of the impending tsunami. Finally, Indonesian government efforts had mostly focused on post-earthquake relief, while paying little attention to pre-disaster anticipation. While early warning systems are improving, the lack of foresight and investment in their design can lead to much greater losses, and these problems exist in both developing and developed countries (think Hurricanes Katrina and Sandy).
Building and strengthening data systems in low- and medium-income countries (LMICs) presents additional challenges. Lack of funding, political will, trust in data and capacity to develop and sustain well-designed data systems in developing countries impedes progress towards meeting the Sustainable Development Goals (SDGs). The Ebola and COVID-19 pandemics have painfully underscored the consequences of limited investments in data systems. The World Health Organization (WHO) estimates that two-thirds of all deaths in low-income countries and most middle-income countries go unrecorded. Civil registration and vital statistics data systems are critical to effective disease surveillance and are the root of good governance. They are often the best — and sometimes the only — source of data to monitor many of the SDG targets.
The stakes are too high to ignore the need for investing in fundamental, fit-for-purpose, well designed data systems in LMICs. These systems can help speed progress in meeting the SDGs — without them, progress will falter and “Game Over” may become a common reality.