Blog post for a Business Intelligence course, part of the AI for Business Master’s program:
The Lecture and Assigned Reading
The assigned article was “Big Data and Business Intelligence: Unveiling the Impact,” from the Decision Foundry. It outlined how traditional BI was once based on limited, structured datasets and static reporting and now has evolved into a system capable of processing massive and diverse data streams. The article, like the lecture, emphasized how Big Data = vast quantities of unstructured or semi-structured data. Business Intelligence (BI) = the framework that converts that information into usable knowledge. I appreciated learning the distinction between the two.
What stood out to me in the lecture and the reading was the difference between data quantity and data usability. Simply possessing large volumes of information doesn’t automatically lead to better decision-making. It requires an intentional methodology. I hope to learn the various methodologies in this class. Based on my previous experiences processing information, I envision we will cover a combination of analytical tools, data context, and social interpretation to turn information into actionable knowledge.
The article also illustrated how businesses apply these concepts. For example, Walmart uses weather and purchasing data to predict consumer behavior. I imagine them predicting how strawberry Pop-Tart sales increase before storms on the East Coast and changing something with shipping or making them more visible on the website. Netflix was also mentioned for integrating user preferences and behavioral patterns to refine recommendations. These examples demonstrate how BI intersects predictive analytics, behavioral science, and customer engagement.
A concept that stood out in the lecture was the idea of “N = all”. Traditionally, researchers have relied on sampling to infer broader trends. But in the age of Big Data, we increasingly have access to entire populations. This allows us to analyze trends across complete systems rather than estimates. This paradigm shift challenges the foundations of traditional research and opens the door to new kinds of precision in understanding behavior.
In agriculture, this concept could be impactful. Imagine a system that collects real-time climate, soil, and plant health data from every farm in a region. With enough data and the right BI tools, we could develop predictive models that guide irrigation schedules, forecast disease outbreaks, or identify emerging stress patterns before they become visible. In this sense, Big Data could democratize precision agriculture. Large corporate farms will likely lead this innovation. Time will tell if these new technologies can become accessible to small-scale growers. The first step will be collecting the data, which is something I fear is lacking currently in Ag.
However, I also think it’s important to consider the limitations of “N = all.” Just because we can collect data from everyone doesn’t mean we should or that it automatically leads to good science or ethical outcomes. This is where the article “Why Big Data Needs Small Data” by We All Count (2020) adds valuable nuance. The author argues that while Big Data offers breadth, small data offers depth and context. The article is from 2020, which is now dated, but it is a reminder that numbers alone can’t capture the human stories, local knowledge, or environmental complexity. For example, climate sensors might record soil moisture levels, but a farmer’s observation of subtle plant responses or microclimate variations might reveal an intuition no sensor can detect. For me, this intersection of quantitative and qualitative data mirrors what we often see in Extension work. There is a need to balance empirical measurement with human judgment.
In my work with Cooperative Extension, I see immense potential for AI and Business Intelligence tools to transform how we serve farmers. For instance, using data visualization dashboards could help visualize soil test results or track irrigation efficiencies. We have much of this data now, but sometimes the interpretation gets lost in the conversation. The key is ensuring that BI doesn’t just produce more data but delivers interpretable, actionable ideas.
The combination of Big Data and Business Intelligence represents more than a technological trend but a shift in how we perceive, analyze, and act on information. For agriculture, this means moving from reactive to predictive systems. Stakeholder trust is a crucial part of this and not something I’ve seen discussed in week 1. As the “Big Data Needs Small Data” article reminds us, we must pair analytics with context and algorithms with human understanding. I see the human understanding as the part that AI cannot currently replace.
In the future, I hope to explore how these principles can be applied in my work. As I continue in the AI for Business program, I’m inspired by how these tools can empower farmers to make smarter, data-driven decisions. I still have much to learn and must become comfortable with the concept of a Petabyte of data.
References
Decision Foundry. Big Data and Business Intelligence: Unveiling the Impact. https://www.decisionfoundry.com/business-intelligence-consulting/articles/big-data-and-business-intelligence-unveiling-the-impact/
We All Count. (2020, January 10). Why Big Data Needs Small Data. https://weallcount.com/2020/01/10/why-big-data-needs-small-data/