In that capacity, I learned to use the Bloomberg terminal that sits on some 315,000 desks in the financial industry and that makes the company all its money. And I learned early on that practically every move terminal users make is tracked and recorded. (Also, it goes without saying but I’ll say it anyway, this website is owned by News Corp., which owns Dow Jones, which is a competitor to Bloomberg.)

In a Sunday editorial on Bloomberg View, the company’s equivalent of an Op/Ed page, Editor-in-Chief Matt Winkler wrote, “Our reporters should not have access to any data considered proprietary. I am sorry they did. The error is inexcusable.” He opens the piece by quoting from a section of his book, “The Bloomberg Way”:

“The appearance of impropriety can be as damaging to a reputation as doing something improper. Because we hold others accountable for disclosure, we expect the same of ourselves. While disclosing errors of judgment may be embarrassing, the sooner the lapses are reported, the sooner there is nothing more to say.”

This raises the question: How fully will Bloomberg News disclose what it admits to be an “error of judgment” that is “almost as old as Bloomberg News.” How many reporters used the Z function — a software command that displays whether or not a customer is logged in and which functions he or she has been using the most — over the many years it was available to them? Chances are that Bloomberg has the data on precisely how often it was used and by which reporters. It could with some effort call in a third party to perform a detailed audit on this, and then disclose the findings of that audit to clients and the rest of the world.

I’ve asked Bloomberg about this. Spokeswoman Lauren Meller didn’t have an immediate answer. If I get one I’ll post it here.

If you’re going to properly understand the controversy that has emerged about the company in recent days, you need to understand the basics of the terminal itself. Bloomberg is at its very heart a financial data software company. In executing a “function” on its terminals, which are seen as status symbols of the financial industry, you type a command, usually one to four letters, and hit the Go key, which replaces the Return key on the conventional keyboard. When looking up, say, the price and fundamentals of Apple shares, you type AAPL, hit a key labeled Equity to indicate the first four letters are intended to indicate a stock ticker symbol, and then hit Go.

During the year I worked there I never heard about the so-called “Z function” at the heart of the current controversy, but its existence isn’t surprising. If you haven’t been paying attention, here’s what it’s all about. All 2,000-odd reporters at Bloomberg News have these terminals on their desks and use them to conduct research, report, write and publish their stories, and to communicate within the organization and without.

The Z function, now disabled for newsroom employees, showed when clients were and were not logged in to the system. When someone hadn’t logged in in a while, an attentive reporter might see that as a tip that the person was changing jobs or had left a firm, and then the reporter would start asking questions. Stories about executives moving between firms tend to be popular among terminal clients. It also showed what other Bloomberg functions these clients had been using, but in a non-specific way that wouldn’t show what stock or bond or other matter they might be researching or which news stories they had been reading. Bloomberg reporters are also said to have had access to transcripts of customer service calls clients made seeking help with functions.

Bloomberg is and has always been a “big data” company. The newly fashionable idea that you can learn a great deal and thus improve a software application by analyzing the big mass of data gathered about how it is used and where users run into problems has been been at the core of Bloomberg’s operational philosophy from the beginning.

Employees know from the moment they join the company that the amount of time they spend at their desks is logged. Building security systems are linked to the terminal and an access badge. When you “badge in” at any Bloomberg office around the world, this occurrence is logged. If you’re a Bloomberg employee based in New York and happen to be visiting London or Tokyo, your arrival and departure times are tracked, as is the amount of time your terminal is idle, should you be out gathering news or taking a lunch break.

I never experienced this first hand, but I heard privately shared tales from colleagues about their annual performance reviews, and discussions would at times turn to how well they used the terminal to do their jobs. As I was first joining, a friend who had worked at Bloomberg for a while told me that during one such conversation, he was mildly scolded for using Yahoo Finance to look up some bit of financial data — terminals also have Web browsers — rather than the terminal itself.

I point out this conversation for a reason. In its quest to make its products better — certainly a logical goal — Bloomberg clearly tracks how often its clients use its many functions. If that’s true, then it logically follows that it tracks how its reporters do the same thing. Historical data on the use by reporters of the Z function exists and can be examined.

As an organization, Bloomberg News is journalism as re-imagined by Fredrick Winslow Taylor, the philosophical father of factory automation. Its reporters are routinely gauged on how often they log scoops that appear on the terminal’s news service. Taylor believed that by analyzing work, the “One Best Way” to get it done could be found. As the founding editor of Bloomberg News, Winkler always struck me as an avid student of “Taylorism.” Every bit of data that can be gathered is analyzed to make the news gathering process better and more efficient. Indeed, Winkler’s 360-page book seems almost Taylor-inspired.

Scoops and other distinctive stories are further categorized into a taxonomy using an internal nomenclature, and the best of those are singled out in what’s known as “Matt’s Note,” a weekly memo from Winkler. An MMWin, or a market-moving win, is a story that beats a similar story by a competitor by several minutes and that after publication causes the market to react in some way. A Follow is when a competitor writes a story that follows up on a Bloomberg scoop. And there are many others.

All of these are thought to be tracked and used to evaluate a reporter’s performance every year. That means there’s data on how many reporters used the Z function and about whom, and probably data as well correlating to the published stories that resulted.

With at least two large banks complaining, and now two government entities — the Federal Reserve in the U.S. and the European Central Bank — asking questions about all this, you can expect concerns about the lines between Bloomberg’s business and news operations to persist.

Actian CEO Steven Shine told AllThingsD that the combined company will have revenue north of $150 million and 450 employees around the world.

It’s Actian’s third significant acquisition since 2011. In January, it paid $162 million for Pervasive Software, a publicly held software company. Late last year, it paid $37 million for Versant, beating out a bid from another company.

Financial terms were not disclosed, since both companies are private. But the deal marks an exit for ParAccel’s investors, including Amazon, MDV, Bay Partners, Walden International, Tao Venture Partners and Menlo Ventures, who had put in a combined $64 million since its founding in 2007. The most recent capital injection was a $20 million venture round led by Amazon that was announced a year ago.

ParAccel specializes in high-end databases, and has seen a handful of its primary competitors, like Vertica and Aster Data Systems, acquired by the likes of Hewlett-Packard and Teradata, respectively. Its Analytic Platform brings together an analytic database along with features to extend it and integrate it with other technologies for running big-data analytics. Its customers include Amazon, Royal Bank of Scotland, OfficeMax and MicroStrategy.

Amazon uses ParAccel’s technology in its RedShift cloud-based data warehousing service, while MicroStrategy uses it to power a business intelligence product.

Shine said that as more companies begin to struggle with their big-data and analytics problems, their choices first seem limited to large vendors like IBM and Oracle and EMC’s Greenplum. “When you look at companies that are focused purely on data, you see the behemoths, and most of those drag hardware along with them,” he said. “And then you look down below and see a lot of Hadoop spinoffs.”

Companies are looking for help in getting all the various threads of gathering, managing and analyzing big troves of data and then turning it all into useful business intelligence, Shine said. And they also want to do it in the cloud, and that’s where he sees the opportunity.

Actian was born as a database product called Ingres inside CA Technologies, one it acquired in the 1990s. In 2004, CA decided to turn it into an open-source product. And in 2005, during a fit of streamlining, CA’s Ingres assets were spun out as a privately held company, majority owned by the private equity firm Garnett & Helfrich Capital, and became Ingres Corp. It changed its name to Actian in 2011.

If truth be told, Jerry Yang never really disappeared from the Silicon Valley scene, even though he did leave Yahoo rather suddenly just over a year ago — resigning from the board and all other positions at the iconic company he co-founded with David Filo in 1995, and then going very quiet.

When I met him last week at the airy and newish office space of Ame Cloud Ventures, off Camino Real, he politely declined to talk about that Yahoo tenure and departure, although Yahoo and he are inextricably linked in the history of tech.

That’s because the former Chief Yahoo has moved onto the next stage of his career, which perhaps could be called Jerry 2.0 — a term he’d hate (and give me a hard time for using).

Still, in many ways, Yang has launched a new digital life by focusing on what made him interested in tech in the first place: Entrepreneurs.

“I feel like the thing I missed the most is what really early entrepreneurs were doing,” he said of his latest efforts, which have been well known among techies, even if Yang has never been one to toot his own horn much in general. “I’m not sure at all that I’m any good at this mentoring/investing business — that’s why I’m using my own money, and that’s why it’s not a career.”

Yet. Yang is calling his nascent investment business — he’s more than an angel, but not quite a VC — a “work in progress” that might morph into something more.

“There are no LPs — just me, myself and I,” said Yang. “I invest in things for the long term and have a long horizon and the flexibility.”

That said, via Ame — which means rain (雨) in Japanese and happens to be the acronym of the names of his wife and kids — Yang has already invested in about two dozen startups in which he has typically puts in $100,000 to $500,000.

Explaining the name, Yang said: “Without rain, there is no life.”

And it’s clear that Yang has been very busy dousing the startup sector with a wide range of interesting investments, including:

• dotCloud, an application platform for developers
• AeroFS, a private file syncing and sharing service
• Impermium, an Internet security offering
• Jetpac, a travel app for the Apple iPad
• Lex Machina, IP litigation data and analytics
• Tomfoolery, which is aimed at improving mobile enterprise apps

Yang said what informs his investment choices centers on the activity around mobility, sensors, cloud and big data that is enabling the next generation of computing.

“The main investing premise is the idea that devices are more and more network connected,” said Yang, who noted that data that is being collected is now at another order of magnitude than ever before. “The cloud has become the next-generation supercomputer, and the smartphone has provided the revolution to spur its use.”

To select from the companies he sees, Yang has only one young associate, Nick Adams, who codes, helps on deal mechanics, interacts with entrepreneurs and also has had extensive experience in Asia.

That has been important, since Adams also leads business development for China’s Cloud Valley, which is run by Edward Tian, one of Yang’s strategic partners there. It was with Cloud Valley that Evernote, the hot productivity app in which Yang is also an investor, partnered to create a business there.

Still, Yang is not completely alone. He has weekly meetings with another former Yahoo, Ash Patel — who started the $10 million micro-venture fund Morado Ventures, which means “purple” in Spanish, and has a lot of ex-Yahoos as investors — as well as individual angel and former Yahoo CTO Farzad Nazem.

The trio trade ideas and deal flow, sometimes making bets together and sometimes not. Most of all, they leverage their time in the tech sector, both good and bad.

“I might not have better ideas, but I think my experience is unique and helpful [to entrepreneurs] and there is a value to my network,” said Yang. “I think what I have to contribute, besides money and a network, is that I am very candid about the experience I have had.”

That is indeed the case, because it is clear that Yang has a lot of wisdom to impart from his long and eventful history at Yahoo, as well as his stature as one of the Internet’s most important pioneers.

And, having covered the often circumspect Yang for much of that time, I would have to say that these days he looks about as energized, excited and enthusiastic as I have ever seen him.

“I’m truly humbled by the talent that’s out there, and at the same time recognize it’s a very crowded space,” said Yang. “It is not a career yet, but I’m having a lot of fun, and we’ll see.”

Mozilla’s recent announcement to block all third-party cookies by default in the new version of Firefox has sparked renewed interest in these topics, and as you would expect, opinions vary wildly depending on perspective and priorities. Reactions from those representing the ad industry predominantly have been critical, while many privacy advocates have applauded the decision. My reaction was one of frustration for several reasons, but one in particular that is relevant to this discussion — my company builds and sells a product that is used by advertisers and ad agencies to “track” what is happening online.

Common Theme, Different Issues

To adequately discuss privacy, you must first define what it is you’re discussing. Currently, there are two major movements being debated, both of which involve the way Web browsers and providers of online content (predominantly publishers and advertisers) exchange and store information.

First, the Do Not Track (DNT) HTTP header was designed in conjunction with the World Wide Web Consortium (W3C) and is intended to provide consumers with a standard way to indicate to Web applications, digital advertisers and publishers they do not wish to have their behavior tracked across Web properties. All major browsers currently support this feature and the industry is moving toward adopting it as a standard; however, last year Microsoft announced that beginning with Internet Explorer 10, it would enable DNT by default, subsequently causing widespread confusion since the header was designed to be opt-in, not opt-out. For this reason, the vast majority of publishers and advertisers currently ignore all stated DNT preferences, including Google, Facebook and Yahoo.

Mozilla’s recent announcement involves a separate debate about whether or not browsers will accept third-party cookies. For the uninitiated, cookies are small files that include data that allow your computer to interact with the websites you visit. Third-party cookies traditionally are set by advertising companies and analytics firms, such as mine, to help understand what is happening on a website over a certain period of time. Prior to Mozilla’s announcement, only Safari blocked third-party cookies by default among the major browser providers. However, Mozilla’s market share (approximately 20 percent according to Net Applications) is much larger than Safari’s and represents “critical mass,” thus the renewed interest in this topic.

Why We Track

Companies track information online for different reasons. In the analytics world, our business is one of scale. Unlike behavioral targeting, our business model is predicated on the ability to identify correlation across millions of advertising “events” and making recommendations based on huge data sets across large-scale media campaigns.

Typically, advertisers purchase huge inventories of ads across a large number of sites their desired audiences frequent. Each of these ads has a different price associated with it, which increasingly is determined by demand and effectiveness. To make sure they get what they pay for, advertisers often choose to work with an analytics firm to better understand which ad campaigns and channels (search, display, etc.) are more effective and why. As a simple example, browser cookies allow us to determine which advertising is resonating with consumers and how many times that message should be displayed before it gets annoying and loses effectiveness, allowing advertisers to avoid waste and poor experiences.

Unintended Consequences

By most estimates, the first online ad appeared roughly 20 years ago. As a technology, cookies have been used for almost as long. They also serve as a fundamental component that underpins the economics of the open web. Small and upcoming publishers rely on the data provided through third-party advertising technologies to quantify and price the “real estate” accompanying their content, often selling ad inventory as part of large-scale networks that provide reach and attract larger advertising customers. Without these networks, it’s difficult to see these publishers attracting ad revenue necessary to compete and offer high-quality, ad-supported content. Paywalls will also become more prevalent for those publishers that have sufficient resources and audiences to support them.

Also, by eliminating the ability for publishers/advertisers to price and purchase higher-quality placements based on relevance, you can expect a return to “spray and pray” tactics where ad inventory prices plummet and the total number of ads displayed and purchased rises drastically. In other words, if you think some ads are disruptive today, get ready to see much more aggressive tactics employed.

Lastly, while cookies are a relatively mature technology with existing browser controls, industry opt-outs and comprehensive privacy policies, there are alternate tracking approaches that don’t have the same level of consumer control. These alternate methods — including device fingerprinting, flash cookies, local storage, etc. — represent the Wild West of tracking technologies. They are very difficult to detect, lack proper controls and can be abused by fringe tracking companies. None of them represent progress or improvement for the customer.

Choice

The advertising community takes privacy seriously and we agree with the Interactive Advertising Bureau (IAB), National Advertising Initiative, Digital Advertising Alliance and other organizations advocating for the continued development of existing consumer-choice mechanisms. We welcome participation from Mozilla and other browser vendors that seek to deliver easy and transparent privacy solutions to consumers; however, these need to be developed in concert with both advertisers and publishers to be effective.

Paul Pellman is the CEO of Adometry, Inc., a marketing analytics provider that generates insights about the performance of marketing campaigns through combining and interpreting advertising data from online and offline channels. Prior to joining Adometry, he was executive vice president of marketing for Hoover’s.

That was a point that IBM CEO Ginni Rometty made today in remarks at the company’s investors day in San Jose, Calif. IBM is known for making numerous acquisitions over the years, a recent example being StoredIQ.

What does IBM look for in an acquisition? Rometty boiled it down to three questions the company asks before every deal: “Does it extend a capability we have? Does it have scalable intellectual property? Can we extend it to 173 countries around the world?”

In a perhaps not-so-veiled shot at rival Hewlett-Packard, given its combined $16 billion in write-downs on the acquisitions of EDS and Autonomy last year, Rometty said IBM feels strongly that “companies get in trouble when they acquire something that takes them into a new space.”

But just as important to Big Blue’s success in recent years has been its decisions to get out of certain businesses. Examples include the PC business, which it sold to Lenovo in 2004, and, more recently, its retail point-of-sale business, which it sold to Japan’s Toshiba last year.

“Over the last decade, we have divested $15 billion worth of revenue,” Rometty said. “If we had not, we would be a larger company, but we would also be a lesser-margin company, and we would have capabilities that our clients would be less interested in.”

Rometty also said that IBM expects to continue its big bets on technologies like Big Data and analytics. “Data will be the basis of competitive advantage for every company, for every industry in the coming decade.”

To that end, she said that IBM now expects revenue from business analytics to account for as much as $20 billion in annual revenue by fiscal 2015. The prior target was $16 billion. And if Big Blue hits that goal it would amount to a doubling of analytics revenue from 2010.

Big Blue took a serious plunge into the mobile business today by teaming up with AT&T, launching new services for its clients and making it easier for its customers to develop software for mobile platforms. The initiative has been dubbed MobileFirst, and brings what IBM does best, including analytics, application development and cloud services, into a batch of services that make it easier for customers manage and secure the devices that employees use on the job, and to more easily develop applications they use to reach out to their own customers.

It’s a strong signal about IBM’s priorities going forward that, among other things, could indicate a preference for making acquisitions of companies in the mobile space in the future: It has already bought 10 mobile companies in the last four years. Prior strategic bets like this include Web commerce and data analytics. It’s also something IBM has been quietly doing for some time: It said it has already helped 1,000 companies get their mobile act together.

As for AT&T, the two are now partners on the mobile software development front. AT&T uses IBM’s Worklight mobile application development platform — Worklight was one of those 10 mobile acquisitions — to access AT&T APIs in the cloud, speeding up development of apps. Expect IBM to say more about this at the Mobile World Congress in Barcelona next week.

Bluefin Labs

As Business Insider reported, Twitter is buying Bluefin Labs, the social analytics company.

My understanding is that the deal isn’t done yet, but it will be soon. When it is, Twitter will likely end up spending more than $70 million for the company. Bluefin raised a reported $20.5 million over its four-year life, so at least some of its investors, which include Softbank and Time Warner, will see a respectable return.

This isn’t an “acqhire”: Twitter is buying both Bluefin’s team and its technology, and will end up turning its Boston headquarters into a Twitter outpost.

Selling this early, at this price, wasn’t part of Bluefin’s plan. The notion was to create a full-fledged rival to Nielsen by starting out with social analytics, aimed at telling programmers and marketers what people were saying about them when they watched TV. Eventually they were supposed to add more verticals.

But people familiar with the company said that it has had trouble peddling its analytics services to big brands, so selling now makes sense.

And it makes plenty of sense for Twitter, because it gives the company a turnkey research operation, which it will need if it wants to fulfill its enormous revenue and valuation expectations.

Twitter was telling outsiders it expected to generate $350 million in revenue last year, and $1 billion this year. So it’s already selling lots of advertising. But it’s in a very similar position to Facebook a few years ago: Marketers are willing to take a flyer on Twitter ads, because lots of people are using it and it seems interesting, but they’re not quite sure what they’ll get out of it.

At least part of Bluefin’s job will be help close those sales, and get them to buy again by proving out Twitter’s value to both programmers and advertisers. You can think of it as an analogue to the well-respected research teams that already exist at the big TV networks — except the TV guys have decades of tradition and research backing up their pitches. Twitter’s job is much harder.

Meanwhile, Nielsen is trying to do the same thing. Last year, it acquired SocialGuide, a small social analytics company. And in December, Nielsen and Twitter announced a research alliance,  so it will be interesting to see what happens there.

The deal also creates a question mark for Trendrr, another analytics company that has gotten some attention from advertisers and programmers. Bluefin had a lot more funding and marketing horsepower behind it, and concluded that it was better off selling. So Trendrr will have its work cut out for it if it wants to stay solo.

Last year was a year when the phrase “Big Data” was all over the place. Dig through the troves of data your business generates, the thinking goes, and some useful business intelligence falls out. That, at least, is the idea, and there are numerous companies — some startups, some big and established players — trying to build business plans on different aspects of that idea.

But in the course of being reduced to a simple buzzy phrase, Big Data as a concept implies some expectations, some realistic, some undoubtedly not. The research house Gartner has a phrase for this tendency as well: The Hype Cycle.

The Hype Cycle goes like this: A new technology that promises to fundamentally “change everything” gets talked up incessantly in the press and at industry events and often also in research reports. At some point the chatter peaks, and expectations reach a fever pitch. Soon, maybe a year or two after it all started to build and some money has been spent and everything that was supposed to have changed for the better actually hasn’t, the narrative focus turns negative. What seemed so brilliant and earthshaking 18 months ago, seems in restrospect to have been an ill-advised waste of time, money and attention.

This is what Gartner calls the “Trough of Disillusionment” phase of the Hype Cycle.

Gartner analyst Svetlana Sicular argues in a blog post that Big Data may have reached that point. She has been “hearing from people in the center of the Hadoop movement,” the open-source technology central to companies like Cloudera, Hortonworks and MapR. She also presents a video of a Hadoop gathering called Elephant Riders where reps from these three companies are debating its current state. (It’s about 90 minutes and if you’re so inclined, you can see it here.)

By Gartner’s standards, the trough of disillusionment may indeed have arrived, though you certainly wouldn’t be able to tell from the level of investment interest in companies like Cloudera, which late last year raised a massive $65 million round of funding.

One source of that disillusionment, she writes, is that companies are struggling with a basic problem: What questions do you attempt to answer with your data in the first place? “Several days ago, a financial industry client told me that framing a right question to express a game-changing idea is extremely challenging,” Sicular wrote. “First, selecting a question from multiple candidates; second, breaking it down to many sub-questions; and, third, answering even one of them reliably. It is hard.”

Hadoop doesn’t exactly make that process any easier. Once you’ve decided to use it, getting anything useful out of it requires some pretty specialized knowledge and training, and finding the right people to do that isn’t easy. But the industry is beginning to respond to that need: Startups like Mortar Data have sought to make Hadoop more readily accessible to mainstream programmers, while another called Trifacta makes the resulting data easier to manipulate.

And versions of Hadoop itself are getting incrementally easier to work with. Hortonworks, for example, recently released HDP 1.2, a new version of the open-source platform, but also Sandbox, a set of training tools that lets developers play around with Hadoop and get a feel for its use.

I talked with Hortonworks CEO Rob Bearden recently, and he said that, in 2011, companies had no idea what Hadoop could be used for, then spent 2012 experimenting with it, and now want to get some real-world value out of it in 2013. “This year, all the technology is coming together in a way that is consumable,” he said. “In the last quarter of last year we saw a lot of interesting production environments. Now the objectives are becoming clear for getting useful in 2013.”

The next milestone in the Hype Cycle, Sicular writes, is negative press. Eventually it’s followed by a period called the “Slope of Enlightenment,” and finally the “Plateau of Productivity.” It’s nice to know there could be a positive conclusion to all this somewhere down the road.

The Philadelphia-based company, which applies image recognition to track pictures posted on Pinterest on behalf of brands, has now raised $3 million in Series A funding led by NEA with First Round Capital and MentorTech Ventures (the three also provided seed funding for the company, back in 2011, when it was doing something completely different, an “Airbnb for parking and storage” called Storably).

Curalate’s investors, of course, aren’t putting more money in based on the name alone. They’re pleased to see that in the year since Curalate launched it has signed customers including the Grammys, Whole Foods and Gap.

Curalate provides both analytics and marketing tools. It tells brands which pinned content referred what traffic, and helps them run Pinterest contests.

CEO Apu Gupta says the company’s approach of using image recognition is better than those of keyword-based competitors, because only 10 percent of Pinterest posts include the name of a brand explicitly. Plus, as much as 48 percent of the most popular Pinterest pins are linked to expired pages on retailers’ sites.

“Our software reads the pixels in the image,” Gupta said. “We ignore the URLs, we ignore the words, and we say, let’s match up these images like a giant game of memory.”

Okay, but Pinterest itself just introduced commercial accounts in November and, at the time, the company’s platform manager, Cat Lee, told AllThingsD that the company definitely planned to go more deeply into analytics. The company also doesn’t have a clearly defined platform strategy or formal relationships with many of the people in its ecosystem, including Curalate. Isn’t competition from the platform itself a big risk?

Gupta and his new board member, Patrick Chung of NEA, had a few answers for that. First of all, if you look to Twitter as an example, even though it offers basic analytics, there has still been room for Radian6 and Buddy Media.

Secondly, as Chung put it, Curalate doesn’t just exist flat on Pinterest’s platform — it “creates a layer of data that doesn’t belong to anyone but Curalate and its customers.” And third, Gupta said, Curalate will soon integrate Instagram tracking and analytics.

Overall, it was a very appy holiday season for the mobile industry, according to Flurry, which is known for producing reports based on the information it gathers through the thousands of developers who use its analytics tools.

In its report today, Flurry said that more than 50 million iOS and Android tablets and smartphones were activated from Christmas to New Year’s Eve, and 1.76 billion applications were downloaded worldwide during that period. That’s a huge jump from last year’s figures.

In comparison, Flurry estimated last year that 1.2 billion apps were downloaded worldwide across both Android and iOS.

Here is a chart comparing the holiday week to the same number of days earlier in December:

The year-over-year progress shows just how commonplace app downloading is around the globe.

In fact, this year Flurry said that a number of weeks since late November delivered more than a billion downloads, which just last year was considered record-shattering. This year, it is forecasting that a billion weekly downloads could become a regular occurrence.

“Looking forward to 2013, Flurry expects the trend of one-billion-download weeks to become the norm, and that the industry will surpass the two-billion download week during Q4,” wrote Flurry marketing VP Peter Farago in a blog post.

Most of these downloads are still occurring in the U.S., where there is widespread adoption of smartphones and tablets. During the week, the U.S. downloaded 604 million applications, or 34.3 percent of the total. China is not exactly a close second with 183 million downloads, and the U.K., Germany and France are much further behind.

Based on Flurry’s previous reports, one of the most popular app categories is games. And based on evidence from at least two game companies, that continued to be the case this holiday season, as people picked up their phones and tablets for entertainment during their time off.

Disney Mobile said game downloads jumped 98 percent year over year to more than 15 million in 2012, from Dec. 22 to Dec. 28. It benefited from new titles such as Where’s My Holiday? and Nemo’s Reef and Monsters climbing the charts. Separately, Storm8, which publishes such titles as Dragon Story, City Story and Jewel Mania, reported that it had two million game downloads on Christmas Day, an increase of four times compared to an average Tuesday.

Williams, who founded Blogger and Twitter, and is currently working on a new publishing platform called Medium, said he had been disappointed to see some of the companies that he’d invested in “pivot too early” rather than sticking with what they set out to do.

Projects that are worthwhile often don’t work right away, Williams noted in a conversation at Message Bus’ Serendipity Series. He urged start-ups to be willing to “fight the dragons.”

“I see this mentality that I think is common, especially in Silicon Valley with engineer-driven start-ups who think they can test their way to success. They don’t acknowledge the dip. And with really hard problems, you don’t see market success right away. You have to be willing to go through the dark forest and believe that there’s something down there worth fighting the dragons for, because if you don’t, you’ll never do anything good. I think it’s kind of problematic how data-driven some companies are today, as crazy as that sounds.”

It’s true that it’s increasingly possible for young tech companies to measure themselves. With tools like Mixpanel, Chartbeat and Optimizely, you can know not only how many people visit your site or download your app, but how likely people who signed up on a particular day are to come back, or what pages people are visiting at any particular moment. You can endlessly test which button should go where, and what color it should be.

In a lot of ways, that’s a good thing, because it moves people away from so-called “bullshit metrics” that only really help with bragging rights and toward objectives that will help their businesses be more purposeful.

But all that capacity to instrument and analyze and optimize can be overused. If the possible outcomes are set before the experiment begins, there’s probably not much room for creativity.

Or, as Williams noted, the data can make it look like something’s not worth doing, even when it is.

(Image courtesy of Shutterstock/Dm_Cherry)

Image via Kronick

Still, I think we can all safely agree that both data and algorithms are absolutely necessary to complete any analytical project. But, regardless, the real point of any successful analytics project is to help an organization achieve a specific business goal. In that light, I hope we can also agree that marketing success is actually driven by four primary considerations: business acumen, data, algorithms and operations. Let’s take a look at each.

Business Acumen

By getting caught up in the data and the math, we can easily forget that analytics projects live and die on business knowledge. Analytics projects, therefore, must always begin with a clear business goal. What does this campaign seek to accomplish? What activities or actions does the marketer wish to encourage or measure? What does the organization already know about key prospects? And what pitfalls will marketers need to anticipate and avoid?

The answers to such questions will influence the other three analytics drivers. For example, digital media optimization models require a “dependent” variable (data), which is often expressed in terms of converters and non-converters. Naturally, the stated business goal will drive which users are deemed “converters.” If some arbitrary mismatch exists between goals and definition, the campaign may very well fail.

Data and Algorithms

I linked these two drivers together to emphasis the point that data and algorithms must be used in tandem. In truth, data scientists spend much of their day employing and refining algorithms. Yet I can’t quite accept Mr. Torrance’s example concerning how best to select a marriage partner if the goal is to produce tall and healthy children. The simple algorithm, he says, might be to marry the first suitor who’s over six feet tall. You could add more data, such as a threshold for strength, to get better results, he says. But for best results, a better algorithm is what’s needed. He writes:

“Measure the height of the first third of the people I see, and marry the next person who is taller than all of them. This algorithm improvement has a good chance of delivering a better result than just using more data with a simple algorithm.”

I am not so sure I agree. Without a doubt, a perfect algorithm that considers height alone will select one of the tallest suitors in the community. But is that sufficient to achieve the stated goal of tall and healthy children? What if that marriage partner happens to have a transmittable genetic condition, or is horribly grumpy? Wouldn’t knowing more about the partner (i.e. more data types) lead to a healthier life for all involved?

Of course, we can’t assume that the more data you have, the better off you’ll be. Useful information that you didn’t have before (orthogonal data, in analytics speak) trumps unlimited data for the simple reason that not all data are created equally. Here’s an example of how that’s the case:

Let’s say you’re building a model that will help you find likely prospects for a new luxury sedan. Now let’s say your analytics model begins with one known input: Household income. Given a choice between additional data and a better algorithm, which should you choose? Additional information — purchase intent — say, will tell you something you didn’t know before. It will be useful therefore to know if a user is interested in purchasing high-end vehicles. But, given a choice between a better algorithm and adding new data such as individual assets under management, the better algorithm may be the best approach. Purchase intent represents new information and insight directly relevant to the business goal. But another measure of affluence? Not so much.

Operations

All the best data and algorithms will be for naught if analytics isn’t fully embedded and widely distributed within appropriate marketing systems, so that the analytics can be directly leveraged whenever and wherever it’s most needed. At eXelate, we recognize that platform flexibility is a critical component for realizing digital media success via cross channel marketing execution.

One last point I’d like to make has to do with the value of contextual and behavioral data. In his article, Mr. Torrance writes, “At Rocket Fuel, we’re big believers in the power of algorithms. This is because data, no matter how rich or augmented, is still a mostly static representation of customer interest and intent.” As far as I can tell, Rocket Fuel therefore sees all data as contextual data, limited to the time of the marketing event.

While it’s of course possible to ignore the time component of data, doing so throws away vital information that an appropriate algorithm could easily digest. In a very simple example, treating someone as “auto intender” or not is far less powerful that tracking a user’s behavior over time to better understand how their intents and interests evolve. That is, unlike contextual data, behavioral data is by its very nature directly linked to longer-term patterns of user behavior. As a result, behavioral data are far better at driving long-term value across a variety of campaigns, especially branding efforts that address personal aspirations.

In a 2011 article entitled “Consumers Are People Too…,” I argued that when asking which data are better, behavioral or contextual, the only right answer is both. And therefore, I reject any absolutes, because marketers clearly will benefit from a variety of data types.

In conclusion, if you’re asked to pick between more data or a new and better algorithm, which should you choose? The proper response is that business acumen, varied information, great algorithms and operations are all critical to the success of an audience model.

As SVP, Analytics, Kevin is responsible for leading the vision and execution of the eXelate’s analytics and data optimization activities. Kevin has a BA in Russian Language and Eastern European Studies from the University of Illinois at Urbana-Champaign, an MA in Medieval History from The Ohio State University and an MA in Applied Statistics from the City University of New York-Hunter College.

Creative Commons image via toolmantim

But honestly, Web sites that require you to repeatedly click through to see additional pages are often just crappy. Download counts can be easily inflated if an app developer is willing to pay. Registered user counts don’t show whether something is the next big hit or a passing fad.

Analytics company Mixpanel and its investor Andreessen Horowitz are trying to persuade the tech world to be more honest with itself by reporting numbers that are far more informative: Engagement and retention. (Yes, those happen to be the very things Mixpanel measures, but as I’ve written before, they make a lot of sense.)

Some people call page views and the like “vanity metrics,” but Marc Andreessen and Mixpanel founder Suhail Doshi have decided they want to raise the shame level by calling them “bullshit metrics.”

Andreessen told me in an interview last week, “People think they’re richer if they have Zimbabwean dollars than U.S. dollars.”

“We and other investors need to get more vocal,” Andreessen said. “Page views and uniques are a waste of time.”

Andreessen said his firm won’t throw start-ups out the door if their pitches include bullshit metrics — but it’s perhaps something they might consider.

And it’s not only start-ups that try to push big numbers over reality; Google famously obfuscated Google+ user counts as it was getting its social efforts off the ground.

The problem is, once one company reports a bullshit metric — like how many registered users it has — its competitors don’t want to appear smaller by reporting something real — like how many monthly active users it has. So the bullshit metric shaming needs to be a broader movement for it to work.

(To which I say, where are the bumper stickers?)

“We can do better as an industry. We should do better because collectively we’re not benefiting — we’re all just fooling each other,” Doshi wrote in a blog post he’s publishing today.

Bullshit metrics do a disservice to the companies reporting them, Doshi said. When companies focus on numbers that are not meaningful, they aren’t optimizing their efforts for what could really help their business.

As an alternative, Doshi proposes — and he is not the first to do so — that every business has a natural goal that correlates with its success. For instance, Yelp benefits most when it has more reviews, and Instagram when it has more photos uploaded. Measuring that “one key metric” can lead to insights that are particular to that business, and optimizing for it can give the company an edge versus competitors that are not so fine tuned.

Here’s Doshi’s blog post:

Bullshit metrics

Every day feels the same. A fledgling startup tries to appear like the up-and-coming market leader while the market incumbent aims to protect its dominance. It has become exhausting to keep up with how fast everyone seems to grow: 100,000 new users per week here, 20 billion monthly pageviews there, and let’s not pass up a watershed moment like 3 million members total. These are the industry’s most praised metrics.

Sadly, we haven’t moved forward over the past decade despite our whole industry becoming smarter about how it measures and analyzes data. Companies still pitch investors with a cumulative user sign up graph, sell advertisers on how many pageviews they get, and bamboozle reporters with the biggest numbers they can find regardless of whether they correlate to success. We can do better as an industry. We should do better because collectively we’re not benefiting–we’re all just fooling each other.

So, enough with the bullshit metrics.

New metrics for the new world

The internet has evolved in the past decade. Websites are no longer static; they are dynamic. Businesses strive to build products instead of websites because they are more rich, more engaging, and more sophisticated. The web 2.0 era was characterized by its use of AJAX which made websites feel like applications instead of web pages. The concept of pageviews is dying. This is far more pervasive within applications we use on our mobile smart phones. Companies are still pressured to tout pageviews as a metric for success to entice advertisers to spend their dollars with them. And yet, users are not generating more pageviews while flipping through photos on Facebook. Extrapolating this idea further makes you consider what signups really mean. Facebook has done a great job with this. They undoubtedly passed their first billion signups long ago, but they’ve made a point to only talk about getting to their goal of 1 billion users who have been active in the past 30 days.

With any change in an industry, we need a mental model of mapping the old way of measurement to the new way.

Actionable metrics

Companies need to start using a new set of metrics that don’t simply make them feel good. They should use actionable metrics that provide insight, provide guidance, and help businesses make better decisions. I fundamentally believe that actionable metrics give companies a competitive edge.

Today, engagement is the better page view. Engagement is a specific action a user takes and it’s something specific to each business. Twitter can measure engagement by number of tweets and YouTube by the number of views a video receives. For Yelp, an actionable metric would be the number of reviews posted because more reviews likely lead to more users that find more value out of Yelp. More users means more reviews–a network effect. For Instagram, a mobile photo sharing application, an actionable metric would be the number of photos uploaded daily rather than the number of users that signed up and never did anything. Engagement helps us understand how actively people are using our products, whereas a pageview could simply be someone showing up to the product and leaving confused or unimpressed.

The most important metric today is retention. It tells you how often a customer comes back and uses your product again. Retention is an objective way to measure how valuable customers find your product so you can improve it if it’s not valuable enough. The best venture capital firms in Silicon Valley are beginning to expect this metric. Companies are starting to include this metric in their pitch decks to provide transparency into whether they can actually get a high percentage of their 100,000 newly signed up users per day to come back weeks later. The truth is, if less than 10% of people come back, then people don’t find the product valuable. To make matters worse, the percentage of people that come back as time goes on usually declines. Gaming best characterizes this decline because consumers eventually get tired as the replay value of a game diminishes. Now, only a small percentage of the original people who signed up are staying engaged. This particular situation is a reason the number of sign ups a company has attained is not an actionable metric.

But the most important thing is that companies find a specific, actionable metric they can focus vigilantly on.

Your One Key Metric

My experience (I am the co-founder of Mixpanel, an analytics company that helps companies like Airbnb, Khan Academy, Dictionary.com, Match.com, and Path) has shown that companies should start by tracking a single actionable metric that they can literally bet the company on. I call this their One Key Metric (OKM). Companies choosing their OKM realize they must pick an actionable metric because pageviews or sign ups aren’t harsh enough and don’t correlate highly enough with the success of their business.

The best part of OKM is that companies can measure other things related to it, to understand how to improve it. In the case of Instagram, their OKM is likely to be photos uploaded. They can measure the percentage of people that download the app and uploaded a photo, the percentage of people who come back and upload a photo a week later, the growth rate of photos uploaded month after month.

Understanding your OKM often leads to deeper, more valuable questions. The answers often surprise businesses and guide them better than before.

Cutting through

Despite the internet’s evolution, bullshit metrics perpetuate a constant cycle of poor understanding. Let’s strive to understand how our businesses are doing and to pick better metrics–the harsher, the better. Let’s stop fooling ourselves with numbers that don’t represent reality. And let’s push the industry forward as a whole because collectively we’ll all benefit.

Since DataRaker is relatively small and privately held, financial terms aren’t being disclosed. By my count, and it may be wrong, this is Oracle’s ninth acquisition of 2012.

DataRaker supplies cloud-based analytics software to electrical and gas utilities that are using smart meters. It combines data gathered from those meters with data gathered from older legacy systems and provides analysis that helps control costs, saving both customers and the utility money.

Here’s Oracle’s announcement.

Oracle Buys DataRaker

Adds Cloud-based Analytics Platform to Oracle Utilities Solutions to Transform Big Data into Actionable Intelligence

Redwood Shores, CA – December 13, 2012

News Facts
Oracle today announced that it has entered into an agreement to acquire DataRaker, a provider of a cloud-based analytics platform that enables electric, gas and water utilities to leverage vast amounts of data to optimize operational efficiency and improve the customer experience.

Leading-edge utilities are investing in infrastructure to collect massive amounts of data from millions of distributed smart meters and sensors, and they require modern technologies to analyze and understand the insights provided by this data.

DataRaker’s proven solutions enable customers to gain immediate benefits from smart devices, by transforming meter, customer and network data into insights that can have a dramatic impact on organizational performance.

The combination of Oracle and DataRaker’s cloud-based solutions is expected to provide utilities with the most complete solution to harness the benefits of utility Big Data to improve operational performance and enhance customer experience.

More information on this announcement can be found at http://www.oracle.com/DataRaker
Supporting Quotes

“Big Data created by smart meters and sensors has presented utilities with an enormous opportunity to improve operations and deliver better customer service by acting on the unique insights that can only be found by understanding the massive amounts of data coming from their customers and networks,” said Rodger Smith, Senior Vice President and General Manager, Oracle Utilities. “With DataRaker, Oracle can provide customers a complete and integrated set of products to further unlock efficiencies and create data insights that maximize business value.”

“Oracle’s proposed acquisition of DataRaker represents a strong endorsement of DataRaker’s proven, scalable and high-performance platform. We are excited to be a part of Oracle, and look forward to combining our resources to drive new innovations that will continue to deliver value to customers,” said Rick Brakken, CEO and Co-Founder, DataRaker.