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February 9th, 2023
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New Gartner report dissects the hype around big data technologies
Big data can add another line to its ever-expanding resume: It now has its own hype cycle. Stamford, Conn.-based consultancy Gartner Inc. recently released the Hype Cycle for Big Data, 2012 – its first hype cycle report devoted solely to the topic.
The hype cycle includes a heady definition as well as a listing of 47 big data technologies and terms, which include everything from the data scientist and cloud-based grid computing, to predictive analytics and open government data. Those technologies are spread across Gartner’s hype cycle spectrum, which documents a technology’s journey from over-promotion and disillusionment into maturity and mainstream adoption.
GARTNER’S DEFINITION OF BIG DATA
Gartner defines big data as:’high volume, velocity and/or variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.’
“It’s hard to call big data one single technology,” said Hung LeHong, a Gartner research vice president, in a recent webinar. “It’s actually a concept, a set of technologies.”
That set of technologies, mostly clumped in the early stages of the cycle, has become increasingly complex, as business analysts clamor for more insight from their data and vendors slap a “big data solution” sticker onto more products. This year, Gartner decided to document the market hype in black and white, especially as businesses are able to conduct inexpensive big data experiments using cheap servers, open source technology and the cloud, according to the report.
While the hype cycle visualization is a prominent feature, the document is mostly devoted to a breakdown of each technology in a one-to-two page mini-analysis by Gartner researchers.
Is a hype cycle deserved?
“The concept of big data — it has a weird duality,” said Mark Beyer, research vice president and big data expert for Gartner. “It’s very real, but it’s also hyped.”
According to Beyer, big data is real because of technological advancement and timing. Four major factors that influence technological development — memory, storage performance, processing capacity and network speed — doubled in size or speed around 2009. It is not unusual for these influencers to experience a growth (or speed) spurt, but they operate on separate cycles and tend to hit those milestones at different times. What’s unusual is for all four to get bigger or faster simultaneously, as they did in this case.
Second, IT departments were receptive to the improvements. The technology in these departments swings between two extremes: advances that enable better, faster performance, and equipment that becomes easily inundated by data demand, Beyer said. In 2010, users began asking for more data, pushing the IT pendulum into the overwhelmed and into the market for ways to alleviate the pressure.
The drive for more data and the technological advances to support that demand aligned at just the right moment, but it’s also created an insatiable hunger — a hype factor — that has taken the industry by storm.
“The hype part is — so what?” Beyer said.
While the alignment of all of these factors is rare, it’s not new. The industry has been here before in the late 1970s/early 1980s, again when the Internet was created, and it will be here again in 15 to 18 years, Beyer said. These periods were and will continue to be “big data moments” that challenge the industry to leverage technology and construct new ways of collecting, storing and analyzing data.
While some analysts believe the current big data technologies will become ubiquitous in the next couple of years, Beyer has a different perspective. “They will become subsumed,” he said. These technologies, in other words, will disappear from sight because they’ll have been absorbed so completely within the traditional IT environment.
And, according to the report, “[W]hen the hype goes, so will the hype cycle.”
More than one hype cycle
In mid-August and for the second year in a row, big data also made an appearance on Gartner’s Hype Cycle for Emerging Technologies. Last year, the consultancy listed big data as a “technology trigger,” the first of the hype cycle’s five phases. This year, big data has progressed into the second phase known as the plateau of inflated expectations, which contains the cycle’s highest peak and tends to be marked by the media’s changing tone from success stories to failures.
Gartner’s placement of big data on the emerging technologies hype cycle has some experts disagreeing.
“[B]ig data is being a bit overhyped by many in the market,” said Bill Franks, author ofTaming the Big Data Tidal Wave and chief analytics officer for the data warehouse appliance vendor Teradata, based in Miamisburg, Ohio. “However, I don’t think it is being set up for the typical fall.”
Tony Cosentino, vice president and research director for San Ramon, Calif.-based Ventana Research, concurred.
“Does every technology need to fall down before it succeeds?” Consentino asked.
But experts didn’t question the placement alone, they also questioned big data’s inclusion on the cycle in the first place, one of the very reasons Gartner chose to create an independent report for big data.
“It’s like asking people in 1995 if they think that this newfangled ‘Internet’ thing is inflated or not,” said Laura Teller, chief strategy officer for the Jersey City, N.J.-based Opera Solutions LLC, an analytics platform provider. “The problem is that big data alone will not improve business.”
Cosentino agreed: Big data is going through an evolution, one that started with the question of “what” and has grown into the question of “so what.” That answer, he said, lies in analytics.
“To use the oil analogy, we’re moving from the drilling of the oil to the refining and distribution of the oil,” Cosentino said. “I’m not sure if it was possible one hundred years ago to overhype the discovery of oil, and I’m not sure it’s possible to overhype the discovery of big data today.”
Read article at source – myagiledata.curatasite.com
