The Role of Data Amidst Economic Uncertainty

The Role of Data Amidst Economic Uncertainty

In the current millennium, pandemics, the explosion of new technological tools, climate change, conflicts, and economic shifts have all become commonplace. According to estimates from the International Monetary Fund (IMF), the World Uncertainty Index (WUI) reached its highest recorded level in Q1 2020 (Ahir et al., 2020; International Monetary Fund, 2023), and it has remained significantly higher than in the 90s.

Amidst all these changes, one particular and constant topic has emerged as highly relevant and prominent in most companies: data. Take a moment to consider this: How many companies had a dedicated data team before 2010, and how many of them allocated significant resources to it? Not many. However, since 2010, the demand for data-related initiatives has grown exponentially, increasing by nearly 80% year over year (Feng, 2021).

While establishing direct causation between economic uncertainty and the increasing demand for data remains elusive, the prevailing trends indicate that companies recognize the power of data in predicting, understanding, prioritizing, and preparing for unexpected events (Smith, 2023). If companies were ships, data would serve as a map that, when placed in the hands of the right people, could guide them toward success while imparting wisdom and fortifying the crew for future similar events.

In line with this analogy, the precision and significance of this map are directly tied to the expertise of the data team that crafted it, and the effectiveness of utilizing that data rests upon the capabilities of the ship’s captain.

However, this raises an important question: What if the leader doesn’t want to use the data? What if the map is confusing? What if the decisions of the captain disregard the information on the map? All of these questions can be condensed into one:

How to Create Useful Data?

Aside from the technical requirements and knowledge, there are some essential questions that every data team member should always keep in mind while working even on the smallest task:

  • What business problem am I addressing? 
  • Am I providing comprehensible and meaningful insights? 

These two questions apply from the simplest spreadsheet to the most complex models you can think of. Every action is triggered by a question, and each question posts a challenge that goes way beyond the lines of code needed to solve it.

However, delivering useful data does not always mean that the information will be used to make better decisions. More often than not, executives don’t trust the data (Curry, 2022) and usually make decisions purely based on their intuition. There are many important variables to consider when trying to use data successfully, but arguably the main 3 factors are:

  1. Data-Driven Culture

    This is probably the most challenging and slowest process of all. For data to be relevant, trust and willingness are needed from all the members of the company, which in many cases, implies a paradigm shift from the traditional way of doing things. Moreover, for this to succeed, trust is key and same as with personal relationships, it requires a continuous and constant effort, and it can be shattered by the slightest mistake.
  2. Multiple Perspectives

    It is not coincidental that data teams tend to have a wider range of educational backgrounds than most other positions. It is normal to find teams with people that have studied social sciences, business, economics, engineering, or even medicine. This means that every team has people with many different ways of solving the same problem, and they complement each other by creating new and innovative solutions together. Putting their own opinions into action and perspective often helps to find the best results.
  3. Communication

    A good data-team crew understands that, no matter how complex the process is, the result must always be as simple and understandable as possible. Sometimes, even though the answers lie within the data, a reader (even an expert) might struggle to find them just because of how the data is presented. Doing this requires more creativity than most people would think. Data, by itself, means nothing. There’s always the need for full context and an interpreter to make sense of it; This is where the true power of data resides.

Society is constantly going through a transformation process, and the usage of data appears to be here to stay. Although future possibilities of this tool are unbounded, there’s still a cultural paradigm shift that needs to accompany it towards a data-driven world. As Fussichen aptly puts it: “Investing in data and analytics isn’t just about technology. It’s about creating a culture of curiosity, adaptability, and resilience. It’s about making data a language everyone in your organization speaks and understands. When we achieve that, uncertainty doesn’t seem so daunting, but instead becomes a landscape of opportunities.” (Rehana, 2023). In the middle of this process, companies are getting to understand that the correct use of data is key to succeeding in an increasingly uncertain and competitive world.

Will the importance of data keep growing in the future? We can’t know for sure, but we can say that data-driven companies will be more likely to thrive after any crisis.

Fabian Sanchez

Sr. Data Analyst


Ahir, H., Bloom, N., & Furceri, D. (2020, March). 60 Years of Uncertainty. International Monetary Fund (IMF).

Curry, D. R. I. (2022, February 22). 75% of Executives Don’t Trust Their Data. Big Data.

Feng, J. (2021, February 10). Data Science Job Market Shrinking as Data Engineering Grows Exponentially, New Study by Interview Query. News Releases.

International Monetary Fund (IMF). (2023, June 20). World Uncertainty Index (WUI): Global. World Uncertainty Index (WUI): Global.

Rehana, S. (2023, May 24). Maximize Efficiency & Identify Opportunities: How Data & Analytics Can Help Your Business Thrive During Uncertainty. Blog.

Smith, R. F. (2023, January 20). 5 ways software companies can thrive amid economic uncertainty. Enterprise Analytics.


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