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Working With Data In Latin America: Challenges And Strategies

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Guest post by Geoffrey Michener, CEO and Founder of Dataplor, a company that helps global companies succeed in emerging markets by providing hand-collected and triple-verified micro-business data.

The collection of qualitative and quantitative market data provides critical insights around the globe. Governments, investors, and citizens benefit from the continuous supply of this forward-thinking information. According to ESOMAR and BDO, the global market research industry grew 6% in 2017, reaching a value of $76 billion. Around the world, quantitative research generated 81% of all spending, while qualitative research accounted for 14%, a 1% decline from the previous year. The remaining 5% of industry spending was seen across other research methods.

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Companies operating in the market research industry offer an essential service to those that rely on accurate data. The enterprises that use this information are often looking to generate helpful insights such as market competition, size, and consumer behavior, amongst other metrics.

While this process is reliable in developed economies, data collection in emerging regions like Latin America presents unique challenges. It’s important to analyze the obstacles associated with data collection in emerging economies, and what companies can do to overcome them.

Emerging Market Data Challenges

As venture capital continues to flood Latin America, a growing number of companies will need reliable, accurate market data. According to the Latin American Private Equity & Venture Capital Association (LAVCA), venture capital funding in the region nearly doubled in 2018. Reaching a record $1.98 billion, last year’s numbers dwarf the $1.14 billion of capital investment seen in 2017. With this trend forecast to continue, it will become increasingly critical to overcoming existing data collection hurdles.

And while these hurdles are many, some remain more problematic than others. Typically, external influences, such as government and corporate interests are hardest to overcome when collecting emerging market data.

Biased Data

There are several ways data can become biased; however, politics remains the primary influence in emerging markets. Unlike developed regions, many emerging markets lack the strong institutions that operate beyond government reach. As such, there are fewer opportunities to challenge information disseminated by government bodies, leaving data susceptible to political interference. Also, many organizations in developing regions pursue stakeholder-driven agendas, resulting in further opportunities for political bias.

Erroneous Data

Erroneous data can result from many scenarios. Whether incomplete, inaccurate, or irrelevant, corrupt data is of little value. As a result, companies are unable to rely on this information to establish plans or implement long-term objectives. Even worse, businesses may unknowingly act on erroneous data, resulting in faulty strategies and lost capital. In some instances, data can be intentionally altered to present a false reality. For example, back in 2013, the International Monetary Fund issued a “declaration of censure” against Argentina over the quality of its inflation and GDP growth data. This action represented the IMF’s harshest reprimand up until that point.

Outdated Data

Although quantitative data is meant to generate leading indicators, a lack of timeliness may diminish the value of these metrics. Because data is often released on a lag in emerging markets, companies operating in these regions often make decisions based on old information. As such, all companies must account for lagging indicators when formulating a corporate strategy. Executives must be aware of broader economic conditions, industry trends, and government regulations to avoid negatively impacting profitability.

Interpreting Emerging Market Data

Most companies cannot independently influence overall market conditions and trends. As such, businesses must learn to adapt to navigate the pitfalls of emerging market data successfully.

Ensure Quality

To make optimal business decisions, companies must work with quality data. For companies to effectively navigate emerging markets and assess risk, minimizing uncertainty during the decision-making process is crucial. One example of poor quality data relates to inflation figures from Argentina between 2007 and 2016. Following changes to the government’s methodology, many around the globe began to discredit government estimates. As a result, many Argentinian companies began to reference private market data when generating their inflation numbers.

The challenge in these scenarios is figuring out how to uncover low-quality data. For many businesses, this can be done by drawing parallels to regions with similar economic and demographic fundamentals. By cross-referencing neighbouring national data, inconsistencies are easier to spot.

Determine Definitions

In countries around the world, national definitions are often challenging to navigate. Slight wording variations or lifestyle differences can result in entirely different market research results. In emerging regions like Latin America, these instances can be further exacerbated by diverse socio-economic conditions.

The Future of Data in Latin America

As Latin America evolves to reach new levels of maturation, existing obstacles will become less prevalent. However, in the interim, it remains crucial for businesses to manage the shortcomings of emerging market data proactively. To overcome the pitfalls of biased, erroneous, and outdated information, companies need to develop effective management strategies. Further, market researchers and industry stakeholders must make data accessible by sharing knowledge.

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