The current Industrial Revolution—in which market research plays an active role—relies on a series of factors without which it could not be sustained. Data and information serve as both key inputs and outputs of this process. However, it is evident that the volume of these inputs has grown exponentially over the years. In the past, reliance on analog tools like paper and ink meant that the volume of figures was quite limited, constrained by the physical media themselves.
Today, the ability to store databases in virtual environments has largely eliminated the need for vast physical storage spaces and the lengthy timeframes previously required for data retrieval and analysis. Conversely, we now find ourselves in a scenario—once merely imagined at the end of the last century—that takes on new meaning now that it is a reality: the sheer magnitude of data inputs. With this in mind, we outline four key lessons learned from having access to Big Data.
Capture. We live in a world where leaving a digital footprint is unavoidable. Most of our daily activities involve interacting with at least one device connected to the internet or local storage. Consequently, an staggering amount of numerical data and information is collected every second, allowing us to identify consumption patterns and market segments with a level of detail that might be unsettling to some. Therefore, it is worth familiarizing oneself with the best tools for working with this data in market research.
Storage. Although it may seem to contradict our earlier statements in this blog post, the reality is that both physical and virtual storage spaces are finite. In the physical realm, servers housing databases are often located in specific countries, causing latency issues when accessing or using the data from distant locations. In the digital realm, we must consider storage capacity limits and the lifespan of the devices used for this purpose, such as USB drives and external hard drives.
Cleaning. We have already established that the databases accessible today consist of a vast number of records. Often, these datasets are so extensive that they require programming languages or specialized software to handle the sheer volume of cases. The challenge today is no longer simply amassing the greatest number of scenarios to feed robust statistical models and calculations; rather, the most valuable and strategic task is identifying the specific data that truly describe a given phenomenon.
Training. We are currently immersed in the process of feeding machine learning tools—specifically Artificial Intelligence systems. A significant step forward in this revolution involves incorporating substantial human input to create analysis and generation platforms capable of producing results that are as accurate as they are human-like. This latter aspect is currently the subject of ongoing legal, ethical, and moral debates that have yet to be resolved.
Big Data may have lost its position at the center of public attention, yet it remains the driving force behind many processes and tools currently under development. Over time, we will likely amass even larger datasets, prompting us to question whether collecting such a massive volume of inputs is truly necessary. Consequently, we now rely on the judgment and expertise of analysts to find the “needle in the haystack” within the vast sea of numbers available to us today.
At Acertiva, we have witnessed this evolution firsthand over the years. Drawing on two decades of industry experience, we offer a team of analysts who understand this landscape and provide solutions tailored to your specific needs. We maintain a presence across LATAM through our operations in Brazil and Mexico. Contact us today, and let’s start writing your next success story.
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