随着自媒体的出现，专业记者将不再需要的议论甚嚣尘上。如今数据新闻又催生了一些新的报道者，比如“程序员记者”，即计算机程序员因为其技术的使用，成为了报道者，比如美国芝加哥论坛报之“犯罪新闻”(Crime in Chicagoland)网页的程序员；“统计师记者”，即懂得统计的工作人员当上了信息报道者，比如博客“FiveThirtyEight” 博主内特·斯弗(Nate Silver)。②那么专业记者还有存在的必要吗？当然还是需要的，只是对专业新闻工作者的要求变得更高，他们不仅要理解统计、设计，而且要有新闻专业的敏锐性。
Big data this, big data that. The popular Silicon Valley buzzword has become so ubiquitous — the term was even added to the Oxford English Dictionary last year — that it seems to mean everything and nothing at the same time.
Big data will transform industries! Change the way we work and live! Alter the future of computing as we know it! (Imagine the confusion among executives who feel competitive pressure to actually incorporate the concept into their business.)
The benefits of big data are still nebulous for some, but the marketing industry faces a slightly clearer proposition. For marketers, harnessing complex data sets about a target audience could produce more effective campaigns and measurable competitive advantage. Which may be why more than two-thirds of organizations expect to ramp up spending on data management services in the next year, according to a recent report from Forrester Research, and 41% expect to increase spending between 5% and 10% in the next year.
“It’s not really a question of big data as much as it’s a question of the right data,” Forrester principal Sheryl Pattek says. “It’s about turning data into insights that you can act on to drive business.”
Marketers are not quantitative experts — not quite in the way that a data scientist is, anyway. Yet they are expected to justify every dollar they invest and make data-driven decisions on brand strategy. Digital marketing involves more channels, platforms, and audience segments than ever before. Analytics tools to track web and social trends only scratch the surface of the intelligence that can be gleaned. Is there a better — organized, structured — way to integrate it all for a better view?
Ad-tech companies like Rocket Fuel, a Redwood City, Calif.-based startup using artificial intelligence to determine advertisement placements online, are working to help marketers automate data for optimal ad delivery and use predictive algorithms to inform future campaigns.
“The idea is that this parallel processing with machines gives you far greater power than you could ever have by trying to guess your way into making a decision,” says Eric Porres, Rocket Fuel’s chief marketing officer.
Toshiba is one of the companies that enlisted the five-year-old company to help drive sales for its Kira Ultrabook laptop computer. Using its platform, Rocket Fuel discovered that business travelers and wine connoisseurs were most likely to purchase the device. By orienting its ad spend to this group, Toshiba achieved an average 8:1 return on ad spend — in other words, $8 in revenue for every $1 in ads spent.
“Not only does online offer these tantalizing clues into human behavior,” Porres says, “but it’s also a way to shift how marketers see their issues of communication and strategy married to their goal and objectives.”
AirPR is another tech company working with marketers to improve their results. The San Francisco-based startup recently rolled out Analyst, a platform that uses machine statistical analysis to measure return on investment of public relations and marketing spends. The firm, a type of Match.com marketplace for PR, is betting that it can help marketers understand the quantitative value of PR campaigns and more quickly eliminate programs that aren’t working.
Core to the platform is what AirPR founder Sharam Fouladgar-Mercer calls “PR intelligence,” a sort of competitive analysis that rates large companies like NYSE Euronext and small shops like the web development company Wix against their competitors for factors such as brand awareness.
“Data alone is not enough,” Fouladgar-Mercer says. “Companies that cut through the fat to showcase what data is important for decision making will help elevate the entire ecosystem.”
Forrester’s Pattek, who regularly works with CMOs on strategy, says that a benefit of data-driven marketing is in transitioning an entire organization — not just the marketing team — to understanding how to use data to win and retain customers.
“Data has allowed CMOs a seat at the executive table to really talk in business terms through these non-monetary models,” Pattek says. “But they have to be comfortable with it first.”
The future of cloud computing is the availability of more computing power at a much lower cost.
Cloud providers Google, AmazonWeb Services (AWS) and Microsoft are doing some spring-cleaning, and it’s out with the old, in with the new when it comes to pricing services. The latest cuts make it clear there’s a new business model driving cloud that is every bit as exponential in growth — with order of magnitude improvements to pricing — as Moore’s Law has been to computing.
If you need a refresher, Moore’s Law is “the observation that, over the history of computing hardware, the number of transistors on integrated circuits doubles approximately every two years.” I propose my own version, Bezos’s law. Named for Amazon CEO Jeff Bezos, I define it as the observation that, over the history of cloud, a unit of computing power price is reduced by 50 percent approximately every three years.
I’ll show the math below, but if Bezos’ law reflects reality, the only conclusion is that most enterprises should dump their data centers and move to the public cloud, thus saving money. Some savings occur over time by buying hardware subject to Moore’s Law, plus the fixed cost of maintenance, electrical power, cooling, building and labor to run a data center. In the end, I’ll show how prices are reduced by about 20 percent per year, cutting your bill in half every three years.
How we got here
Google was first to announce “deep” cuts in on-demand instance pricing across the board. To make the point that cloud pricing has been long overdue, Google’s Urs Hölzle showed in March just how much cloud pricing hasn’t followed Moore’s Law: Over the past five years, hardware costs decreased by 20 to 30 percent annually, but public cloud prices fell by just 8 percent annually:
Having watched AWS announce, by my count, 43 price cuts during the past eight years, the claim of merely a 6 to 8 percent drop for public cloud seems off. (That would be a 2 percent reduction 43 times to get an 8 percent trend line.)
Nevertheless, applying a Moore’s law approach to capture the rate of change for cloud, one would hold constant the compute unit, while the gains are expressed in terms of lower price. Thus, Bezos’s law is the observation that, over the history of cloud, a unit of computing power price is reduced by X percent approximately every Y years.
A bit of digging on Amazon’s Web Services blog shows how Amazon determined the percentage in computing power (X) and time period (Y) on May 29, 2008. The data from 2008 and the Amazon EC2 Spot Instances on April 1, 2014, shows that in six years, similar compute instance types have declined by 16 percent for medium instances and 20 percent for extra-large instances. Assuming a straight line, the pricing would have tracked as follows:
For the AWS public cloud, X = 50 percent when Y = 3 years, supporting my claim: Bezos’ law is the observation that, over the history of cloud, a unit of computing power price is reduced by 50 percent approximately every three years.
Clearly, cloud, as opposed to building or maintaining a data center, is a much better economic delivery approach for most companies.
And how can an enterprise datacenter possibly keep up with the hyper-competitive innovation from Amazon, IBM, Google and Microsoft? Enterprising tech pros know how this is going to play out. They’re way ahead in asking: “Why should we continue to saddle our company with a huge cost anchor called a datacenter or private cloud?”
It looks as though being a cloud provider isn’t going to be like a retail business when it comes to profits, but it may be too early to tell. It’s a bit like the x86 server business IBM recently sold to Lenovo. There will likely be innovation above the core cloud platform for a long time, which might alter the profitability outlook.
Opinions aside, the math doesn’t lie. It’s not question of if we’re moving to the cloud but how — and when.
HPCC可以管理、排序并可在几秒钟内分上亿条记录。HPCC提供两种数据处理和服务的方式——Thor Data Refinery Cluster和Roxy Rapid Data Delivery Cluster。Escalante表示如此命名是因为其能像Thor(北欧神话中司雷、战争及农业的神)一样解决困难的问题，Thor主要用来分析和索引大量的Hadoop数据。而Roxy则更像一个传统的关系型数据库或数据仓库，甚至还可以处理Web前端的服务。
LexisNexis CEO James Peck表示，认为在当下这样的举动是对的，同时相信HPCC系统会将海量数据处理提升到更高高度。
Imagine for a moment the following scenario: You and your fellow members of the senior leadership team are gathering in the conference room for a regular meeting with the CEO. She starts talking about a recent global economic forum where there was a lot of buzz about a technology that promises—or threatens—to turn the business world upside down. She turns to the group, with particular glances toward the chief marketing and chief technology officers, and asks, “What’s our strategy for dealing with this? Are we ready?” During the next few weeks and months, task forces form, new people are hired, and the organization strains to digest the implications of this technology revolution and make the most of it.
The year: 1993. Because for the moment, I’m not talking about “big data”; I’m talking about the Internet. In 1997, digital advertising in the United States cracked the US$500 million mark. A decade and a half later, it was more than $10 billion.
Here’s another example: When point-of-sale transaction data first became available in the late 1980s, marketers could finally know with some certainty their market shares, the prices consumers were paying, and what percent of sales were on deal—all things we take for granted today. And going back to the Mad Men era, think of the revolution in marketing triggered by pioneers like Robert K. Merton and Daniel Yankelovich when they invented, respectively, focus groups and consumer segmentation.
My point is that big data is just the latest in a series of technology revolutions that have changed the nature of business, in particular customer-facing activities such as innovation and marketing. Our reaction to it should be informed by all we’ve learned from past revolutions, which for me boils down to two main points: Don’t miss the boat, and stay focused on solving core business issues.
The reason I emphasize not missing the boat is that big data isn’t just a matter of more information and better analytics, but a true paradigm shift toward more data-driven decision making. This means extracting insight from the full range of available data. My belief is that the emergence of big data as a major topic is causing increased attention to all kinds of data—including old-fashioned, created data and experiments; digital data; transaction data; and unstructured data. And that’s a good thing. The ability to collect, harmonize, process, interpret, and act on all your data, big and small, will become a core enterprise capability. But it has to live at the center of business decision making—it won’t (and can’t) be relegated to the periphery, performed by insights specialists and third-party vendors. Missing the boat means delaying this inevitable journey.
What can companies do to get on board? The largest and most sophisticated companies, such as Walmart, have actually acquired analytics companies to bring a scalable capability in-house. But there are other, more gradual ways to get started. One large bank used a creative combination of internal and external data sources and advanced analytics to dramatically decrease credit card fraud. A telephone handset manufacturer created a crowdsourcing platform to allow customers to discuss its products, and the platform automatically generated product improvement ideas based on text analysis. An insurance company held a series of daylong cross-functional brainstorming sessions that including marketing, strategy, and IT to identify high-value problems where practical big data solutions could be piloted.
Once a company has embraced data-driven decision making as a new paradigm, it will get the maximum return on its investment by focusing on the most important issues. Big data has gained traction in large part for the many new opportunities it offers to optimize routine marketing activities such as targeting digital ads and improving conversion on e-commerce sites. But these should not be the only, or even the primary, uses of your data. The core issues businesses face haven’t changed: understanding consumer/customer needs, developing and refining value propositions, building strong brands that consumers care about, and creating win-win relationships with channel partners. In these and many other areas, data, wisely used, can open up new markets. Think about innovations such as Nike FuelBand, which creates new consumer experiences by collecting and sharing data about physical activities. Or American Express Merchant Financing, which uses advanced analytics to provide qualified merchants with quick and simple access to cash for their business needs. Game changers like these create value for customers and companies alike. Identifying and building them should be the primary focus of data-driven capabilities.
原作者是大卫·米尔，原文标题为：Big Data: Lessons from Earlier Revolutions，译者：王钰,via：大数据观察