28 Sep 2015

The Big Data Challenge in Emerging Markets

Many commentators confidently predict that Big Data will soon become the basis of all organizational decision-making. But some parts of the world still lag behind.

Whilst analysts predict that emerging markets will be producing the majority of the world’s data by 2020, at the present time these regions offer limited access to digital data. Many low and middle income countries have a long way to go until they can realise the benefits of using their data to improve public policy and business strategy.

Big Data offers huge advantages to any decision maker. For business enterprises, it offers the chance for data-driven decision making that could improve business operations and cost-effectiveness. For policy-makers there are also opportunities to make smarter choices when it comes to areas such as health and infrastructure. Emerging markets could be the ones that benefit the most from Big Data as they are likely to see the greatest gains.

Not only are these countries starting from a low level of information-based decision-making, they also tend to be experiencing rapid development of their infrastructure and healthcare systems. Improving their use of Big Data offers them the opportunity to do this with a higher level of efficiency.

There’s an unprecedented amount of digital information available at the present time. Learning how to store, dissect and analyse Big Data is a major organizational and political challenge even in established markets with well-organised administrations. In emerging markets, the barriers to Big Data and its analysis are likely to be greater. Infrastructure, such as uninterrupted power supplies, and the reliability and speed of communications, are particular challenges to overcome.

Patchy availability of data may also be a concern, as emerging markets are often characterised by gulfs on inequality of digital access. Sections of the population may be barely recorded in digital data, particularly in remote or rural areas. Records may be kept in paper form, if at all, which makes it hard to generate raw data for analysis without huge initial costs. In India many people are without birth certificates, with under 60% of births currently being recorded, to use just one example of the challenges faced.

When Big Data is being used by policy makers, for example health care workers calculating supplies of vaccines required, those who are most in need can easily be excluded from digital analysis. It tends to be those with the smallest digital footprint that are often the most in need. This means the poorest, remotest areas are the ones about which the least data exists, which increases the challenges of serving these populations.

Consultants at Deloitte recently compared the experiences of two countries trying to calculate how many doses of vaccine would be required for their populations. In South Sudan, a country still in a low level of development, demographic data just wasn’t available to assist calculations.

In Ukraine, a country in a relatively advanced state of development, the demographic data was more available but still limited. Ukrainian policy-makers were able to calculate how many doses were required, where, and at what time, but gaps in the data still led to occasional problems with stock. Those in South Sudan had much less data on which to build calculations and this led to greater problems with ensuring supplies were adequate.

Many lower income countries often rely on small amounts of decentralised data, which may or may not be digitised, for all kinds of decision making. In a situation like this it’s advisable to make best use of the small amount of data that is available, making it as accurate and reliable as possible and transferring into a digital format if required.

Small steps such as these help build the data picture and prepare ground for a later transition to big data which will inevitably involve large investments in technology. Organizations such as the World Bank have also made some steps towards creating data for emerging markets and making it widely available. Some major companies have made advances by working with paper records and social media reports in order to build the best picture they can of particular regions.

Big Data skills gap

Availability of skilled and experienced workers to manage Big Data is a problem worldwide. It’s thought that by 2018 the United States could be facing a shortfall of between 140,000 and 190,000 workers with the skills and experience required to manage big data.

The US also lacks a further 1.5 million managers and analysts who will be capable of data-based decision making – that’s according to research by McKinsey. With Big Data skills shortages already a concern in established markets, the issue is likely to be more severe in emerging market economies.

In established markets it’s generally the case that Big Data workers start life handling more traditional data such as databases. Emerging markets don’t have such a strong small data background so fewer workers are available to make the transition. In established markets it’s also common for workers to be poached from other backgrounds, such as the STEM disciplines, to apply their analytical and data mapping skills to Big Data. Data science is also being added to the curriculum at many global universities. In the emerging markets it’s unclear how the skills gap will be addressed.

Emerging markets: the future of Big Data

According to Gartner data, companies are predicting that between 40 and 60% of their revenue growth is likely to come from the emerging markets over the next ten years. This offers an incentive to make sense of Big Data from these markets. Some commentators have attempted to estimate the amount of Big Data that will be generated by 2020 in regions currently described as emerging markets.

One senior figure from EMC’s data division suggested the figure could be over 60% of the world’s data. It’s thought that China will produce 22% of this. This suggests emerging markets could leapfrog established markets in terms of the volumes of data that they generate.

What this means for global marketers

Marketers are increasingly reliant on data to make decisions. In developed countries, they face the challenge of extracting valuable information from increasingly larger data sets to predict consumer behavior. The challenge in emerging markets is often a lack of centralised data. This places foreign businesses that don’t have a track record of operating in these markets at a huge disadvantage to local competitors.

Businesses need to be aware that more data is not necessarily better and that by starting with a highly localised approach and collecting data from local consumers themselves or by partnering with firms with local expertise, they’re more likely to develop successful strategies for global expansion.

A study by EMC estimates that only 5% of the data being generated by people and machines is currently being analysed. This presents an opportunity like no other with the potential to create new lucrative revenue streams and real, incremental growth for businesses in both developed and emerging markets.



 
 

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