The Dos and Don’ts of Working with Emerging-Market Data

The Dos and Don’ts of Working with Emerging-Market Data

The Dos and Don’ts of Working with Emerging-Market Data

Executives are usually taught that data is an objective and critical input for strategic planning and operations. Applying this, however, is much easier said than done — especially among companies operating in emerging markets.

Emerging-market data can be challenging to work with due to significant data gaps, biased data, and outdated or incorrect numbers. Of course, these issues can cause a headache for any company, in any market. But because they are so prevalent when it comes to emerging-market data, the challenges are exacerbated. They can lead executives to make misguided investment decisions, and put a company’s reputation and jobs at risk.

As multinational companies seek to strengthen their emerging-market portfolios, they must have a better understanding of what their data can and cannot do, and when to trust it. The margin for error in business has become substantially smaller as global growth slows.

Based on my research at Frontier Strategy Group, I’ve found that companies can avoid the pitfalls of evaluating emerging-market data, and successfully use it to support strategic decisions, if they follow a few key do’s and don’ts.

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Emerging markets’ data have considerable gaps. To understand a product’s sales potential, executives want industry-specific indicators. For example, if a company sells toothbrushes, it uses toothbrush sales forecasts to determine yearly targets. However, the more specific the information, the less likely it is that data will exist, especially in less developed markets.

Yet companies still expect their data providers to offer this industry-specific information. In order to bridge the gap between what companies need and what data are available, data providers sometimes use models to create the data, which can lead to serious data quality issues. And executives are often not cautioned to take those models with a grain of salt.

For example, the alcoholic beverage company Beta decided to prioritize Iran as an opportunity market for its drinks. According to available data, Iran’s alcohol consumption was comparable to that in India. However, alcohol consumption is illegal in Iran. The numbers were based on outdated statistics from the 1970s, when Iranians, prior to the Iranian Revolution, were emulating Western lifestyles. Growth forecasts were placed on top of these outdated numbers, without taking decades of conservative rule into consideration. As a result, Beta had to restart its market prioritization process without the erroneous industry-specific data.

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The solution is to be creative in gathering data. Finding the right proxy indicators can help fill in gaps. For instance, in order to understand potential demand for cables in Kenya, companies should not try to look for an indicator that specifically quantifies cable sales; instead, they should draw on macroeconomic numbers for government spending and investment in infrastructure, where such cables could be in demand.

Data is biased, particularly in emerging markets. Data can be biased for a variety of reasons. First, organizations tend to use local governments’ historical data, which typically reflects whatever agenda the government may have. While developed markets have stronger institutions that can challenge government estimates, this often is not the case in emerging markets, leaving data more susceptible to political interference.

Furthermore, many organizations have stakeholder-driven agendas that occasionally add political bias. For example, as Greece returned to financial crisis in 2015, the IMF forecasted 2.5% YOY GDP growth in its April World Economic Outlook. (Greece’s actual 2015 growth was -.8%.) While it was clear that the Greek economy would not grow, the IMF was involved with the country’s bailout program, giving it a disincentive to forecast recession.

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