A concept of enduring utility rarely emerges from the market research business, but the Gartner hype cycle is an exception that proves the rule.
It is a graph that describes the life cycle of a technological innovation in five phases. First, there is the “trigger” that initiates the feverish excitement and leads to a rapid escalation in public interest, which eventually leads to a “peak of inflated expectations” (phase two), after which there is a steep decline as further experimentation reveals that the innovation fails to deliver on the original — extravagant — claims that were made for it. The curve then bottoms out in a “trough of disillusionment” (phase three), after which there is a slow, but steady, rise in interest (the “slope of enlightenment” — phase four) as companies discover applications that really do work. The final phase is the “plateau of productivity” — the phase where useful applications of the idea finally become mainstream. The time between phases one and five varies between technologies and can be several decades long.
As the “big data” bandwagon gathers steam, it is appropriate to ask where it currently sits on the hype cycle. The answer depends on which domain of application we are talking about. If it is the application of large-scale data analytics for commercial purposes, then many of the big corporations, especially the Internet giants, are already into phase four. The same holds if the domain consists of the data-intensive sciences such as genomics, astrophysics and particle physics: The torrents of data being generated in these fields lie far beyond the processing capabilities of mere humans.
However, the big data evangelists have wider horizons than science and business: They see the technology as a tool for increasing our understanding of society and human behavior and for improving public policymaking. After all, if your shtick is “evidence-based policymaking,” then the more evidence you have, the better. And since big data can provide tonnes of evidence, what is there not to like?
Which is why it is pertinent to ask where on the hype cycle societal applications of big data technology currently sit? The answer is phase one, the rapid ascent to the peak of inflated expectations, that period when people believe every positive rumor they hear and are deaf to sceptics and critics.
It is largely Google’s fault. Four years ago, its researchers caused a storm by revealing (in a paper published in Nature) that Web searches by Google users provided better and more timely information about the spread of influenza in the US than did the data-gathering methods of the US Centers for Disease Control and Prevention. This paper triggered a frenzy of speculation about other possible public policy applications of massive-scale data analytics.
“Not only was Google Flu Trends quick, accurate and cheap, it was theory-free. Google’s engineers didn’t bother to develop a hypothesis about what search terms — ‘flu symptoms’ or ‘pharmacies near me’ — might be correlated with the spread of the disease itself. The Google team just took their top 50 million search terms and let the algorithms do the work,” economist Tim Harford said.
Thus was triggered the hype cycle. If Google could do this for flu, surely it could be done for lots of other societal issues. And maybe it can, but in this particular case, the enthusiasm turned out to be premature. Nature recently reported that Google Flu Trends had gone astray.
“After reliably providing a swift and accurate account of flu outbreaks for several winters,” Harford said, “the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak, but when the slow-and-steady data from the [US government center] arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two.”
So what went wrong? Simply this: Google does not know anything about the causes of flu. It just knows about correlations between search terms and outbreaks. Yet as every high-school student knows, correlation is quite different from causation. And causation is the only basis we have for real understanding.
Big data enthusiasts seem remarkably untroubled by this. In many cases, they say, knowing that two things are correlated is all you need to know. And indeed in commerce that may be reasonable. I buy stuff both for myself and my kids on Amazon, for example, which leads the company to conclude that I will be tempted not only by Hugh Trevor-Roper’s letters, but also by new releases of hot rap artists. This is daft, but does no harm. Applying the kind of data analytics that produces such absurdities to public policy, however, would not be funny. Yet it is where the more rabid big data evangelists want to take us. We should tell them to get lost.
Two sets of economic data released last week by the Directorate-General of Budget, Accounting and Statistics (DGBAS) have drawn mixed reactions from the public: One on the nation’s economic performance in the first quarter of the year and the other on Taiwan’s household wealth distribution in 2021. GDP growth for the first quarter was faster than expected, at 6.51 percent year-on-year, an acceleration from the previous quarter’s 4.93 percent and higher than the agency’s February estimate of 5.92 percent. It was also the highest growth since the second quarter of 2021, when the economy expanded 8.07 percent, DGBAS data showed. The growth
In the intricate ballet of geopolitics, names signify more than mere identification: They embody history, culture and sovereignty. The recent decision by China to refer to Arunachal Pradesh as “Tsang Nan” or South Tibet, and to rename Tibet as “Xizang,” is a strategic move that extends beyond cartography into the realm of diplomatic signaling. This op-ed explores the implications of these actions and India’s potential response. Names are potent symbols in international relations, encapsulating the essence of a nation’s stance on territorial disputes. China’s choice to rename regions within Indian territory is not merely a linguistic exercise, but a symbolic assertion
More than seven months into the armed conflict in Gaza, the International Court of Justice ordered Israel to take “immediate and effective measures” to protect Palestinians in Gaza from the risk of genocide following a case brought by South Africa regarding Israel’s breaches of the 1948 Genocide Convention. The international community, including Amnesty International, called for an immediate ceasefire by all parties to prevent further loss of civilian lives and to ensure access to life-saving aid. Several protests have been organized around the world, including at the University of California Los Angeles (UCLA) and many other universities in the US.
Every day since Oct. 7 last year, the world has watched an unprecedented wave of violence rain down on Israel and the occupied Palestinian Territories — more than 200 days of constant suffering and death in Gaza with just a seven-day pause. Many of us in the American expatriate community in Taiwan have been watching this tragedy unfold in horror. We know we are implicated with every US-made “dumb” bomb dropped on a civilian target and by the diplomatic cover our government gives to the Israeli government, which has only gotten more extreme with such impunity. Meantime, multicultural coalitions of US