Chapter 1: The Persistent Relevance of Big Data

Big data remains a critical topic in today’s analytics landscape. However, it is essential to recognize when and why specific data formats generate greater value. Many students in our supply chain program pursue a minor in Business Analytics, where we focus on a specialized approach known as Association Analysis, part of our Business Data Mining curriculum.

Wide data involves collecting information from various sources about specific entities, such as products or customers. For instance, if you're gathering data about customers, you might create a table with limited columns focused solely on demographic and contact information. In today’s world, that approach falls short. Customers often want more comprehensive insights, such as their interactions with customer service or feedback on products. This evolving need illustrates the characteristics of wide data. Traditional tables with fixed columns are insufficient because customers may have different numbers of service calls or product reviews.

1. Data originates from diverse sources, including demographic data, service logs, and product reviews.
2. Not all data is structured; for example, product reviews often contain unstructured information.

Small data, on the other hand, represents a more nuanced understanding of individual entities. For instance, if you are investigating why a particular product is underperforming, traditional analyses might consider factors like seasonality, location, and competition. However, examining the specific moments when product X is excluded from a customer's shortlist can reveal valuable insights for manufacturers. Understanding competing products that are shortlisted allows suppliers to employ strategies like competitive priming or targeted promotions to influence purchasing decisions. Small data can often reveal unique insights that broader data sets might obscure.

Our Analytics Major delves deeper into these concepts. We cover unstructured data, streaming data, AI-driven insight extraction, and cloud-based analytics, enabling comprehensive analysis not only of wide and small data but also of big data. Streaming data, which continuously evolves, requires analytical techniques that adapt quickly to changing information. We introduce foundational techniques in our minor program, while our major offers an in-depth exploration.

Chapter 2: The Role of Advanced Analytics

The first video discusses common pitfalls in big data models, providing essential guidance for effective data utilization.

The second video offers a comprehensive guide to fixing declining YouTube views through effective analytics strategies.