Why Big Data Isn’t Just About Size: Misconceptions and Real Value

Since early 2013, the hype around big data has swept across IT and even traditional manufacturing, bringing buzzwords like "Internet thinking," "open," "EB/PB," and "precision marketing" that often leave people confused about what big data truly is.

Remember the knowledgeable "Meng Bo" from the previous "Meng Bo Scenario" series? After fans praised it, he returned with a bold new angle—examining big data through the lens of a garbage bin.

The author, a self‑declared skeptic, uses the garbage‑bin analogy (inspired by detective Li Changyu’s habit of deducing daily events from his trash) to illustrate how big data records every facet of human society—from historical artifacts to everyday objects—waiting to be discovered and utilized.

Two Common Misconceptions about Big Data

1. The “big” in big data means massive volume. Many assume it refers only to petabytes or exabytes, but history shows humans have imagined enormous numbers for millennia (e.g., 10¹² in ancient Indian texts). Size alone does not guarantee usefulness; value comes from analysis and insight.

Even the universe’s unimaginable numbers (e.g., a "great kalpa" spanning countless worlds) illustrate that sheer magnitude can be conceptually overwhelming and practically irrelevant without meaningful interpretation.

2. Authority definitions of big data. Companies like AWS and IBM craft definitions that serve their products. IBM describes big data with four V’s, yet such structured frameworks often miss the nuances of unstructured data. A better approach is to consider the user’s value perspective rather than rigid characteristics.

Different observers extract different insights from the same data—just as Li Changyu can infer his wife’s daily activities from a trash can, while a wanderer might only see food scraps.

Two Insights about Big Data

1. Value varies per person. If the world were a giant data set, the human brain acts as the algorithm, and our senses are input/output tools. We only care about information that serves our personal goals, ignoring irrelevant details—much like caring only about the taste of a street vendor’s pancake.

Thousands of algorithms exist, but the choice depends on the value each individual seeks; the ultimate aim is to obtain the desired “item.”

2. Relative dynamic correlation. The true worth of big data lies in uncovering hidden relationships among massive, heterogeneous data sources. The classic “butterfly effect” illustrates how tiny changes can amplify into significant outcomes.

For example, Alibaba analyzes merchants’ recent 100‑day transaction data to identify potential financial risks, while telecom operators accumulate years of network and user behavior data—forming a “gold mine” of insights.

In today’s era, changing the mindset is as crucial as changing tools; extracting value from seemingly redundant data—whether a trash‑can clue or a subtle pattern—defines the real power of big data.