Implementing Big Data for Developing Nations

Executive Summary Brief:  Implementing Big Data for Developing Nations

I) Background:  An Economic Development Tool for Developing Nations

        As members of the international development community, for five decades Progress International has dedicated itself to assisting advancement of developing nations in pursuit of basic, standardized quality of life for all people. Though proven successful, as times change, we must recognize persistence in our current methods greatly limits our potential future impact as an organization. The entire world is moving into the technological realm at unprecedented speeds. Many like-minded organizations have addressed similar concerns with a forward-thinking solution through the implementation of big data analytics into daily operations and initiatives. To clarify, big data analytics refers to the use of massive data sets to derive insights and trends pertaining to a given field; this could include human behavior and quality of life issues. Consider the volume of data available in open sources that cannot possibly be processed by human researchers fast enough for benefit. Recognizing the unlimited potential in big data analytics, this technology is being used in various forms by organizations such as The Black Monday Movement in Uganda, the GDELT Project and The United Nations (U.N.) (Rockefeller/Bellagio, 2014). Applying a targeted, customized big data program to complex problems has helped many organizations accelerate, enrich, and reduce costs in current initiatives.

II) Key Findings

• Big data analytics can be used for the analysis of problematic situations in developing nations and the prevention or reduction of such scenarios.
• U.S. Geologic Survey big data model using Twitter post information helped confirm location and magnitude of earthquakes with 90% validity (Letouze, 2012).
• Retrospectives conducted by Harvard and MIT researchers show a big data model could have provided geographic forecasts of the 2010 Haitian cholera outbreak with high accuracy (Letouze, 2012).
• A key predictor of potential for the implementation of big data models is the penetration of smartphones in developing areas; this measure ranges from ~40 phones per 100 citizens (Tanzania) to 96 per 100 citizens (Botswana) (Apenteng, 2014).
• Developing nations with high Internet usage, such as India or Brazil, are ripe to benefit from the implementation of big data models (Apenteng, 2014).
• Turnkey-ready big data analytics software exists in open source and at the enterprise level from market leaders such as IBM, SAP, Microsoft, and many others.

III) Recommendations for Management

        Given accessibility to clean water is both a basic human right as defined by the U.N. and aligns closely with our values and mission statement, I suggest we leverage the power of big data analytics to improve our current efforts in this area. We can accomplish this through social media. Because social media and other digital communications tools generate volume, velocity, variety, variability and complexity in its data, we can use big data analytics to construct social and economic outcomes with increased self-awareness among people of developing nations (Attachment 01) (Kshetri, 2014). Specifically, I suggest we build a global model that scours social media and popular web-based news outlets for content, posts, and information that references emerging limited clean water accessibility in specific geographies. This model would locate and aggregate data through targeted keyword searches for terms such as ‘water quality,’ ‘disease,’ ‘access,’ etc., to help us better focus our organization’s resources rapidly.