Big Data Analytics in Emerging Market Countries

Stephen DeAngelis

May 20, 2014

Deloitte Consulting, LLP, analysts Jim Lee, Phillip Savio, and Carey Carpenter know that big data analytics can have a significant impact on a company’s bottom line. They report, “From public sector entities like Lincolnshire, which identified £24m in procurement savings, to retail giants like Tesco, which reduced £50m in excess inventory, organizations across the globe are achieving substantial impact by applying analytics to their operations.” They note, however, that those enterprises operate in developed markets. We know that big data analytics work in developed markets, so they ask, “What about emerging markets?” [“Supply Chain Analytics in Emerging Markets,” SupplyChainBrain, 21 February 2014] In some emerging market and almost all frontier market countries, lack of data is hindering the application of big data analytics to many of the challenges they face. Lee, Savio, and Carpenter explain:

“For instance, take a developing economy like Southern Sudan, where the country lacked the data to forecast vaccine needs to provide immunizations to its citizens. Or in Ukraine, where logistics monitoring and evaluation improved, but data limitations still led to occasional stock-outs. Public and private sector entities seek to make evidence-based decisions to achieve sustainable results. Yet in countries like these, insufficient data and inconclusive analyses are often the norm.”

Fortunately, data is starting to be generated, collected, and analyzed in developing countries thanks to the penetration of smartphones into these societies. Mark van Rijmenam reports, “The developing world is experiencing rapid growth in data creation. However, a large part of the data created in the developing world has a different origin than in the rest of the world: the developing world is progressing rapidly to the mobile era and is largely skipping the desktop and wired era. This requires a completely new approach, but also offers a vast range of possibilities to beat poverty.” [“How Big Data Can Help the Developing World Beat Poverty,” SmartData Collective, 2 August 2013] He continues:

“The United Nations also sees the possibilities of big data and in 2009 the U.N. Secretary-General Ban Ki-moon launched the initiative Global Pulse. Global Pulse serves as an innovation lab and they aim to raise awareness of the opportunities of big data and bring together different stakeholders such as big data scientists, data providers, governments and development sector practitioners. The objective is to help catalyse the adoption of big data tools and technologies and to help policymakers understand human well-being and emerging vulnerabilities in real-time, in order to better protect populations from shocks.”

The following video from Global Pulse demonstrates how big data analytics can be used in the developing world to help address serious social concerns.

 

 

Gary Drenik, President of Prosper Insights & Analytics, asserts that the power of big data analytics comes from the fact that such analysis can look back, look forward, and provide valuable insights. [“Going Beyond Big Data To Knowledge,” Forbes, 11 March 2014] He writes:

“Data is the starting point and basic building block in a knowledge-based organization. … Strategy requires a broader view of data. Strategy requires data that serves as fuel, but logic and experience still need to be applied to generate knowledge-based systems. Knowing not only what happened, but why it happened (diagnostic), what will happen (predictive) and how we can make it happen (prescriptive) is important for moving beyond big data to knowledge.”

Armed with the ability to look back, look forward, and obtain actionable insights, countries (and organizations) are in a much better position to pivot the direction they are heading as circumstances change. Ian Bremmer, President of the Eurasia Group, insists, “In this new decentralized global order, growth isn’t enough. A country also must have resilience — the power to pivot.” [“The Future Belongs to the Flexible,” Wall Street Journal, 27 April 2012] Without the right kinds of insight, a country (or organization) is essentially blind.

 

Not just organizations interested in development and poverty reduction are focused on developing nations. Many consumer packaged goods (CPG) companies are also interested in these countries because most of them are fostering an emerging middle class. This new global middle class represents billions of new consumers eager to improve their lives. And, with access to mobile technologies, they have been exposed to the wonders of the consumer world. These new consumers, however, do not represent a homogeneous group with members sharing the same tastes, lifestyles, or product preferences. That’s why big data analytics are essential for any company that wants to grow its business in emerging markets. And, as last year’s KPMG High Growth Markets Outlook Survey concluded, a lot of businesses want to expand into emerging markets. According to the survey, “69 percent of the respondents identified geographic expansion as the top area for increased spending, … with the majority focused on expanding into or among high-growth and emerging markets outside of the United States.” [“U.S. Companies to Increase Investment Across Broader Range of Emerging Markets, Study Finds,” SupplyChainBrain, 18 July 2013]

 

Since lack of data can be a problem, Lee, Savio, and Carpenter indicate that companies need to learn “to navigate the analytical limitations and constraints often encountered in emerging markets.” They offer a few suggestions for how to this:

Join the small data revolution. Success does not always hinge on a large scale, long-term investment in a data warehouse or big data. For a low- and middle-income country, it may not even be feasible. …

Engage in visual storytelling. … [Simplify] complexity through data visualization. …

Invest in the right requirements. … To unlock the value of analytics in such unconventional environments, one must re-think, re-tool, and re-evaluate traditional strategies around data and analysis. …

Think Big, Start Small. The use of small data has long been the modus operandi in low- and middle-income countries. … In public health and trade, the customer order, procurement, and shipment transactions are often captured through a mishmash of paper documents, files, or if lucky, an occasional database. These data siloes have large repercussions on reliability and reinforce the misconception that analytics in this context is a lofty and impractical goal requiring significant investment. Fortunately, analytics does not always entail the processing of terabyte- or petabyte-class data. In fact, in many cases it’s as simple as turning your mind to what the data can tell you. Quite often that’s not big data. The value of small data should not and cannot be overlooked. …

Employ Visual Advocacy. … Data visualization is a burgeoning area for the international development community, where it has been used to simplify data complexity while delivering impactful insight. …

Get the Requirements Right. Distribution operations in emerging markets are often charged with the responsibility of monitoring and evaluating supply chain performance. Data is collected, indicators such as stock-outs or fill rates are calculated, and information is ultimately communicated to donors, governments, and other partners — similar to how a company reports quarterly or annual performance to its investors. This information has traditionally been used to monitor performance, but can it inform strategies that drive sustainable development? As noted by Darin McKeever, deputy director at the Bill & Melinda Gates Foundation, ‘[Data] may get aggregated into mineable datasets, but the work of turning this data into evidence — that is information which is helpful in forming a conclusion or hypothesis — involves something more.’ In the context of a low- and middle-income country where a myriad of constituents, infrastructure constraints, and fragmented systems confound a supply chain, traditional performance indicators may not be sufficient to form the right conclusion. Operational performance, country conditions, and geopolitical climate are all common requirements to making supply chain analytics effective emerging markets, but what is often lacking is the projected return on investment. For a supply chain, this means making top line or bottom line growth requirements front and center. In the private sector, this helps determine corporate viability in terms of revenue and cost. In a developing country, it becomes an indicator for future sustainability.”

Lee, Savio, and Carpenter conclude, “Supply chain decisions affect more vital outcomes in low- and middle-income countries than one might realize. Demand forecasting and inventory planning influences the ability for national health systems to deliver life-saving drugs to its citizens. Logistics and distribution actions govern the prepositioning of food that is needed to address widespread hunger and malnutrition. Customs and transportation performance helps shape the economic growth and competitiveness of a nation. One can only imagine the sustainable impact that can be made if these decisions were informed by analytics.” In the information age, trying to traverse today’s changing landscape without analytics is like trying to find your way through the dark without a flashlight. Big data analytics are like streetlamps that can help you determine where you are as well as illuminate the road both behind and ahead of you.