The Information Tsunami: Big Data

Chaos vs. Meaning

More data does not automatically mean better decisions. The hard part is turning information into understanding.

Revised October 1, 2026

Collecting data is easy to mistake for understanding something. A business can know when you opened an email, which product you hovered over, and how long you hesitated before abandoning your shopping cart. It may still have no idea why you left.

That gap is the useful starting point for Big Data. The interesting question is not how much information we can accumulate, but what we can reasonably learn from it.

Chaos vs. Meaning

Big Data refers to more than a very large spreadsheet. The NIST definition connects the scale, speed, diversity, and variability of data with the need for systems that can store, process, and analyze it efficiently as those demands grow.

A sales database might organize transactions into familiar rows and columns. Other sources arrive as customer messages, audio recordings, images, video, or streams of readings from sensors. Bringing them together involves more than finding somewhere to put them.

The information may use different units, cover different periods, or describe the same person under several identifiers. Some records may be missing. Others may be wrong. A larger collection can give us more opportunities to discover a useful pattern, and more opportunities to mistake a mess for one.

The value lies in the relevant, coherent information we can derive from the data, and in whether that information helps us make a better decision. A warehouse full of numbers is still a warehouse. It does not become an explanation because the rent is impressive.

Put the Question First

Imagine a company trying to understand why deliveries keep arriving late. It could collect every available detail about its customers, vehicles, suppliers, and employees. Or it could begin with a question: where does the delay enter the process?

That question suggests what to examine: order times, warehouse processing, supplier arrivals, route conditions, and delivery records. It also suggests what to compare. Are delays concentrated in one region? Do they begin before the truck leaves? Is the promised delivery window realistic?

The example is hypothetical, but the distinction matters. Data collection should serve an investigation. Otherwise, the dashboard becomes an expensive way to watch the problem continue.

The Cloud Is Infrastructure, Not Insight

Cloud services can provide storage and computing capacity for this work. They are one infrastructure option; Big Data can also be processed on an organization’s own systems or through a combination of approaches.

Moving information to the cloud does not clean it, explain it, or decide which question deserves an answer. Nor does it make existing databases obsolete. Structured records remain useful, often precisely because somebody has already done the unglamorous work of organizing them.

The choice of infrastructure and the quality of the analysis are related decisions, but they are not the same decision.

Useful Applications, Ordinary Limits

The appeal becomes clearer when we describe a task rather than promise a revolution:

• Customer experience: compare transaction records with support messages to investigate where a service is failing.

• Supply chains: examine orders, stock levels, and transport records to identify recurring bottlenecks.

• Energy use: compare meter readings with occupancy and operating schedules to investigate unusual consumption.

• Fraud detection: flag unusual transaction patterns for further review.

These are possible uses, not guarantees. A suspicious payment may be legitimate. An apparent improvement may reflect a change in who was measured. Two trends moving together do not, by themselves, establish that one caused the other.

Where data describes people, there is another question to ask before celebrating its usefulness: should we be collecting and using it this way? Access to information does not settle questions of privacy, permission, or fairness.

More Is a Starting Point

Big Data can expand what we are able to examine. It cannot relieve us of deciding what matters, checking the quality of the evidence, or recognizing the limits of an interpretation.

A small, reliable dataset that answers a clear question can be more useful than an enormous collection assembled without a purpose. The ambition should be to turn the information tsunami into something we can navigate, rather than congratulate ourselves on getting wet.

What does Cloud Computing mean?



Revised in October 2026.

When we hear the terms the cloud or cloud computing, we tend to think vaguely of the Internet. That is not entirely wrong, but the two are not the same thing.

The Internet had existed for decades before people began routinely talking about “the cloud.” What changed was not the network itself, but increasingly what we were asking the network to do for us.

For years, using software usually meant installing it on a particular computer. You bought a program, loaded it onto the machine, and stored the files you created locally. The computer contained both the application and, in most cases, the work produced with it.

Cloud computing began loosening that relationship.

Applications, storage, processing power, and other computing resources could increasingly be provided remotely. Instead of asking What software is installed on this computer?, users could simply open a browser, sign in, and access a service running somewhere else.

The change can seem almost trivial because it happened gradually. Email moved into browsers. Documents could be stored online rather than on a hard drive. Music libraries became accessible from multiple devices. Businesses began using software without installing and maintaining it on every employee’s computer.

What disappeared was not software, but the requirement that the software, the data, and the user all occupy the same machine.

SaaS, PaaS and IaaS

Cloud services are commonly divided into three broad categories.

• Software as a Service (SaaS) is the form most ordinary users encounter. The provider runs the application, while the user accesses it through the Internet. Web-based email, collaborative office software, and many business applications follow this model.

• Platform as a Service (PaaS) provides developers with an environment in which they can build and run applications without managing all of the underlying infrastructure themselves.

• Infrastructure as a Service (IaaS) goes one level deeper. Computing power, servers, networking, and storage can be rented as services rather than purchased and maintained as physical equipment.

The categories differ, but the underlying idea is similar: some part of computing that once had to exist locally can now be provided remotely and on demand.

That is why the cloud is more than another name for the Internet.

The Internet is the network.

The cloud is what became possible when storage, software, and computing power no longer had to live on the machine in front of you.

Millennials and Multiculturalism

Revised in October 2026.

It may seem obvious, but it is worth paying attention to a simple dynamic: majorities shape trends, and trends often contain the seeds of their own perpetuation.

There is much discussion about the multiculturalism characteristic of the global era, and globalization itself provides an obvious explanation. Technology allows people separated by thousands of miles to communicate almost instantly and at negligible cost. Music, food, fashion, language, and ideas cross borders faster than ever before.

But that explanation can obscure another force operating much closer to home.

In the United States, one of the world’s major producers and exporters of popular culture, Millennials were the most racially and ethnically diverse adult generation in the country’s history when this article was originally written. In 2014, 43 percent of Millennial adults were nonwhite. At the same time, Census projections anticipated that the United States as a whole would become a country in which no racial or ethnic group constituted a majority around 2043.

Those figures naturally attracted the attention of brands and marketers. Diversity could be measured demographically: how many Hispanic consumers, how many Asian consumers, how many members of one group or another?

But demographic arithmetic captures only part of what happens.

Culture does not spread in a one-to-one relationship with ancestry. One additional Thai-American consumer does not simply mean one additional consumer of Thai food. People introduce one another to foods, music, customs, aesthetics, language, and ideas. Preferences migrate across demographic boundaries.

That creates a multiplier effect.

A generation that grows up with greater diversity does not merely contain more cultural backgrounds. Its members are also more likely to encounter those backgrounds through classmates, coworkers, friends, partners, neighborhoods, media, and increasingly through digital networks.

Culture therefore becomes harder to map neatly onto race or ethnicity.

Marketing strategist Eddie Yoon made a related argument in the Harvard Business Review: companies often make the mistake of treating culture as if it followed demographic categories automatically, when people's cultural affinities are also expressed through what they choose to consume and participate in.

Someone does not need to belong to the ethnic group that originated a cuisine to love its food, belong to the community that created a musical form to make it part of their identity, or share the background of a cultural tradition to participate in it.

This is where demographic change acquires consequences that simple population percentages cannot capture.

Greater diversity changes not only who is in the market, but also the network through which tastes move. People influence other people, who influence still others. What begins as demographic change can therefore become cultural change at a considerably larger scale.

Companies that continue to imagine consumers primarily as the predictable product of demographic categories risk misunderstanding that process.

The multicultural market is not simply a collection of increasingly diverse groups standing beside one another: it is what happens when those groups continually influence one another.