Well, according to a study by Columbia Business School and the New York American Marketing Association, although nearly all (91 percent) of marketers value and want to make data driven decisions, 29 percent report that their marketing departments have “too little or no customer/consumer data.” Thirty nine percent of the marketers surveyed said their data is collected too infrequently and “not real-time enough.” Two in five marketers admit that they cannot turn their data into actionable insight and about an equal number (36%) report that they have “lots of customer data,” but “don’t know what to do with it.”

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How much data have you generated today?

In an increasingly digitized world, everything we do creates an electronic record—every purchase, every doctor’s visit, and every time we friend someone on Facebook.

As organizations continue to amass hundreds of terabytes of information, the need has arisen for more sophisticated software tools to mine and analyze this information, to help businesses better understand their markets and customers.

Enter "big data." According to the market research firm IDC, big data companies had sales of $3.2 billion in 2010. Total industry revenue is expected to reach nearly $17 billion by 2015, growing about seven times faster than the overall IT market.

The business analytics and enterprise software industry as a whole has remained steady over the past few years, growing 1.8 percent from 2007 to 2012, according to IBISWorld. However, its 36 percent profit margin, the firm says, has been largely fueled by the acquisition of companies offering big data services. IBM recently purchased several big data firms, including Netezza, which makes data warehouse appliances, for $1.7 billion. Other major acquisitions include Oracle’s reported $1.1 billion purchase of enterprise search company Endeca, as well as Hewlett-Packard’s $350 million purchase of data analytics platform Vertica last year. Large vendors have targeted smaller companies as a means to expand their own customer bases and product offerings.

As the consolidation trend continues, it might become more difficult to pry market share from these giants. But if a quick exit is top of mind, and you’ve got some pretty high-level degrees, such as computer science, software engineering, or market research, the burgeoning big data industry might be the place for your start-up.

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Robotics guru Masahiro Mori’s “uncanny valley” theory posits that as automated activity increasingly mimics human behavior there is a point just before the mimicked behavior is completely accepted where it triggers a strong negative emotional response. As our personal and business online lives become more dependent on an Internet that tries to divine our wants and needs and as we generate more and more information about our actions and personal preferences I think we are all collectively on the verge of reaching this “creepy” valley.

The most newsworthy indicators of this approaching “valley” are the backlashes to ad tracking software and those children’s game apps whose functionality is based on accessing “personal” information. However, an enormous number of other common Internet application functions are triggering a similar response.

For instance, LinkedIn and Facebook’s recommendations for people you might like to connect with always includes people you are close to, but this doesn’t mean that you don’t despise many of them or find the recommendations disturbing (the boss who fired you, the dreaded competitor, the ex-girlfriend, the deceased relative, etc.). Other types of personalization can backfire, too, like Amazon’s recommendations to your friends that you might like something when some of those things might reflect activity you prefer not to make public (e.g., books and videos on far too personal political or sexual topics).

Some of these things can be mitigated via some pretty simple functionality tweaks (i.e., LinkedIn allowing you to flag people with negative attributes or Amazon asking “Do you want to save this transaction to your personal preferences?”), but there is a far bigger question here: Because nobody ever actually reads the “terms of use” agreements to which they blindly agree, wouldn’t be a lot easier to use vendor relationship management (VRM) to make the results based exactly on the way you want your personal or corporate activity data used?

Doc Searls’ latest book, The Intention Economy, was just released last week and he makes a strong case for just this. The implications for going down this road are extremely profound for those of us in the information business. For one thing, company directories could disappear or mutate into permission-based publicly accessible databases. When will this brave new world emerge? The answer, like most things having to do with our networked world, is once again “sooner than you think.”

The buzz about crowdsourcing continues to get louder every day. The crowd can now, it seems, handle a range of tasks including journalism, design, and even filmmaking.

If you’ve been asked to look into crowdsourcing as a way to stretch your budget and haven’t tried it before, however, you’ve probably asked yourself whether the results of crowdsourced projects are close enough in terms of quality to justify the pursuit of the cost-savings they can potentially represent. Put another way: “Are the results of crowdsourced projects good enough for consumers with low expectations, but not up to snuff for discerning professional audiences?”

The short answer is that, while not all crowdsourced projects are created equal, projects for b-to-b audiences do typically require an experienced professional to manage the process and perform quality control on the results, even if the crowd did the “heavy lifting.” Crowdsourcing is indeed a very effective tool for collecting certain types of data, but it takes an experienced manager to determine which particular data points in a data collection project are good candidates for crowdsourcing. It also takes know-how to draft the worker guidelines and set up the checks and balances needed to yield quality data. Without this, a project can require so much quality control work that any cost savings from crowdsourcing are wiped out. Done right, the process can yield a perfect data set collected at a very low price. Done wrong, it’s easy to end up with a data set of dubious quality that needs long and expensive clean-up to get it up to minimally acceptable professional quality standards.

Do you have a data project you’re interested in crowdsourcing? Contact IEI for an evaluation and a ten-page white paper on our crowdsourcing methodologies. Both are free of charge but available to qualified inquiries only.

Consider just one of big data's outstanding virtues: its ability to drive innovation by providing
insights from data correlating customer behaviors, patterns and specific requirements -- including data that have traditionally been cast off as odds, ends and by-products of "analyze-able" data.

Big data enables the collection and analysis of offline and online data across touchpoints. Customers are generating more data about their buying behavior, likes and dislikes in more places than ever before. In addition to data stored in widely used CRM applications, untapped and tremendously useful customer satisfaction data exists in tweets, blog posts and other social media
content.

Using big data collection and analysis to capture behavior including preferences, product
selection and spending patterns across thousands of customer interactions enhances the ability to measure and impact customer satisfaction and loyalty in ways never before possible. Operational, financial and customer data across a business can now be integrated and processed efficiently to aid the identification and attribution of revenue drivers, yield actionable insights into product development and to provide deeper levels of customer engagement. Customer level revenue attribution, channel optimization, triggered marketing and marketing can occur more efficiently and reliably than ever before.

Big data is quickly becoming mainstream, and as a consequence, both the tools and associated pricing are becoming accessible to almost everyone. Earlier this week Google launched BigQuery which puts a powerful, straightforward and relatively low cost cloud-based data analysis in the hands of a broader category of companies.

Powered by Google's might, BigQuery's best application is interactive analysis of very large datasets. BigQuery is a SaaS program service that runs on Google infrastructure. It accepts CSV files uploaded from customers via an API. The API uses concurrent compressed streams which allow customers to upload several hundred gigabytes in minutes with analysis that Google estimates is "about 10 times faster than the speed of many corporate data systems." Uploaded data is secure, replicated across multiple data centers and can easily be exported. Data is accessed via group- and user-based permissions using Google accounts.

One advantage of Big Query is its straightforward nature and its ability to keep costs low. Cost is $0.12 GB per month for storage with a 2TB limit and queries are $0.035 per GB with a limit of 1,000 queries per day. Prices are negotiated beyond those limits.

As capital "B" Big Data increasingly becomes small "b" big data, we're going to see even more tools and services that will allow publishers to easily and cheaply find important new insights about their customers and their markets, and that's what makes big data a big opportunity and a big deal.

-- Nancy Ciliberti

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What made yellow pages arguably the most profitable form of publishing ever was the combination of monopoly-like characteristics, coupled with a product that worked extremely well for advertisers. But with every community sporting a yellow pages product, those monopoly-like characteristics became a two-edged sword because, while margins were wonderful, there was little room for geographic growth. For many years, yellow pages publishers contented themselves with eye-popping annual price increases in their home markets. After the AT&T divestiture, there was a huge effort to poach territory from other publishers, resulting in many people finding anywhere from two to ten different yellow pages on their doorsteps. That’s why when the Internet came along, what the yellow pages publishers saw was a way to vastly expand their territories. Forget adding a new market or even a new region. In one fell swoop, they could become NATIONAL. Nirvana!

In reality, this move to become national online publishers was horribly ill-advised. Yellow pages publishers know nothing about content. All those pesky names and address listings they publish? They simply buy them, and view them as a necessary evil, useful only for separating the display advertisements. And this national expansion overlooked the core dynamic of all yellow pages and buying guides: the advertising is the content. By extension, no advertising, no content. Yet with regional sales forces, these publishers had no capability to sell advertising on a national basis, so instead they offered a thin gruel of content: company name, address, phone and general category. Not exactly compelling even in the early dates of the web, and almost pathetic today. The Internet goldmine turned out to be a black hole.

That’s lesson number one. If you are going to produce an information product, that product needs to offer … information. And the competition is keen in the online world where information begets more information that a user can get elsewhere. That’s why the most successful information producers tend to keep a tight focus. Better, deeper, fresher. And the best data publishers are now wrapping software around their content to make it more useful as well. If none of this sounds like yellow pages to you, you’ve gotten my point.

The other useful lesson revolves around momentum. Until very recently, people pointed to the yellow pages as one print medium that while not thriving, was at least holding its own. Yellow pages, some believed, were different and immune to the forces of the web. It’s amazing how quickly that story changed.

That’s lesson number two. Momentum is a powerful thing. Couple it with a strong brand, years of measurable results, a largely unsophisticated advertising base (potentially as many as half of all local business do not yet have websites) and a saturation distribution strategy, and you can appear to defy gravity for a long time. It’s comfortable sticking with what you know, especially when the old way is more profitable than the new way. But when you’re defying gravity, reality will intrude unexpectedly and you’ll hit the ground hard. The smarter path is to engineer a soft landing, not to convince yourself that your market is somehow special and different.

To sum it all up: if you stay focused, dig deep and keep your feet on the ground (making stumbles is a lot less painful than crashing), you’ve already mastered some of the fundamentals to successful information publishing.

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I was once the publisher of the Standard Periodical Directory and that database of magazines, newspapers, journals, and newsletters had a category called “house organs” to cover the in-house publications of corporations and nonprofits. These publications were full of group shots at picnics and United Way events with helpful notices on changes to health care plans, recent births and retirements, plus a random assortment of articles put in the periodical by the volunteer staff. Little did I know at the time that perhaps the weirdest thing to come out of the digital revolution would be the elevation of these homespun house organs, via the miracle of social media, out of obscurity and onto center stage.

In-house publishing operations took their first steps into a larger digital world when corporate home pages emerged, typically handled by in-house marketing folks. These were full of quirky personality at first, but were soon homogenized as industry best practices and interactive agencies emerged. The implementation of social media was much more carefully managed than the Web 1.0 initiatives were, but P&G’s “everybody poops” theme for their Charmin Facebook site, Ragu’s site for mothers (because mothers buy tomato sauce?), and the like are showing an eerie similarity to the tone-deaf house organs of yore.

Like the funky home pages of the past, these corporate publishing forays are likely to mature into something less comical in the near future, but, strangely enough, they also could evolve into a major threat to the traditional media business. How, you ask? Big corporations support advertising-driven media. If those corporations could buy and own their marketplaces by creating single-sponsor media properties (Internet/Radio/TV/Events), or networks of them, that aggregate their target audiences then they are way ahead of the game. They have the money (“reach”), they have industry experts as employees (“personalities”), they are driving innovation in their respective fields (“news”), they are able to target their markets at increasingly granular levels (“narrowcasting”), and they can reward loyalty through targeted discounting (“coupons”).

The problem is that when corporations have produced media in the past they have failed miserably. The reasons don’t include the lack of money, but seem to boil down to a kind of navel-gazing marketing myopia that just falls flat with audiences, hence the need for advertising agencies to tell stories that make their products more appealing. Media companies have much broader perspectives and tend to be a lot much more savvy about what interests audiences, but their chronic lack of cash, slow decision cycles and failure to create innovative experiences for their audiences makes them vulnerable to their deep-pocketed customers.

Nike, for instance, spent $2.4 billion on advertising in 2011, but it shifted 40% of that budget away from mass-market TV and print ads and moved it into digital (web, social, email, etc.), and spent another 10% on the sponsorship of niche sporting events, product placements, and other non-traditional media. How long will it be before they field their own basketball team, own a beach volleyball league, or broadcast Brazilian regional soccer matches on their own network? Japanese baseball teams (not just the stadiums) are already named after the companies that own them and the move to single-sponsor media in the US has also been a long time coming. Will the rise of social media be the tipping point that finally gives corporations the direct line of communication with their end-users – without distributors, retailers, and media in the way – and thus makes media companies irrelevant?

Doctor says that after years of hoping the rise of the Web and digital media would not decimate the industry, followed closely by the hope that digital ad revenue would somehow arrive and close the gap, print executives are finally starting to understand that both of these hopes are futile:

Until recently, the holy grail was summed up in two words: replacement revenue. Now the jig’s up. No matter how fast you shovel digital dirt into the chasm of print loss, you can’t recreate the past; you can’t fill the hole.

Stack those digital dimes as fast as you can

John Paton, the CEO of Media News Group and a leading advocate of the “digital first” approach for newspapers, has said that the only possible response to the problem of digital dimes’ not making up for the loss of print dollars is to “stack those digital dimes” as fast as possible. In other words, accumulate as much as possible from as many sources as possible (while also reducing costs to try to stem the bleeding). In his Nieman post, Doctor notes that Meinolf Ellers, the managing director of German multimedia agency dpa-infocom, made a similar point at a recent conference of news executives:

What we all see — newspaper publisher or news agency — is that the bundle is eroding, losing its power. The more we see the bundle losing market share and reaching the end of its lifecycle, the more we have to work on smaller, fragmented products that, not each by each, but overall, can compensate. That’s the strategy.

This reminds me of a phrase that David Weinberger, a fellow at Harvard’s Berkman Center for the Internet and Society and co-author of the book The Cluetrain Manifesto, came up with to describe how the Web works: He called it “small pieces, loosely joined.” One of the things I took from this is the idea that the Web allows for individuals and small groups or entities to have almost as much power as — and in some cases more power than — established players. The barriers to entry, and the barriers to discovery, are so much lower now, thanks to the Web’s “democratization of distribution.”

We have seen the impact of exactly that phenomenon in the media industry in spades over the past few years, with the rise of digital-first entities such as the Huffington Post, TMZ, Politico and others, as well as the rise of individual media sources’ using social tools to become the equivalent of media entities in their own right or hybrids such as Andy Carvin of NPR and his one-man Twitter newswire model.

What will readers pay for other than just a paywall?

In his discussion of what media outlets can do to make a number of smaller bets instead of one or two big ones, Doctor refers to a number of things, including “in-sourcing” — using printing presses and distribution chains to provide services to others who need those skills — as well as providing marketing services outside the traditional newsprint platform. These are also things that Paton has focused on while trying to remake the Journal-Register Co., a chain of papers he took over after it emerged from bankruptcy.

But the things that really interest me are the ones that fit the kind of “velvet rope” model I have argued for as an alternative to a hard paywall around content: the ones that encourage a kind of membership approach, where new features or ways of packaging content or experiences related to that content are offered to readers. So live events, for example, which both the Texas Tribune and the Atlantic have been using to their advantage, or e-books, which are a different way of packaging content, can be remarkably profitable, even if that content has already appeared on the Web for free.

Unfortunately, many traditional media companies simply don’t have the kind of culture that allows for random experimentation or rapid iteration and prototyping: in other words, a startup culture. Some papers such as the New York Times have a skunkworks or research lab, and others such as the Washington Post have experimented with new features such as the Trove recommendation engine or the Facebook social reader. But many of these still feel like afterthoughts or side projects rather than a coordinated plan of attack on multiple fronts. The ones that are trying the hardest always seem to be the digital natives, or the ones with the gun to their head.

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Oracle’s problem is that relational doesn’t go far enough in the era of big data, which requires the handling of unstructured, semi-structured and all manner of information from Twitter, Facebook, server logs, you name it. And, for once, Oracle can’t jam all that stuff into the relational mold. Instead, it launched a Cloudera-based Big Data appliance, in January.

While the pricing was low compared to what analysts expected, it’s still a big “scale-up” sort of appliance. List price for the Big Data Appliance is $450,000 plus another $54,000 for hardware maintenance and $36,000 for operating systems maintenance. That’s a lot less than the Oracle Exadata appliances, but it’s still a significant outlay. You can buy a ton of standard X86 boxes — the sort of gear typically used for big data crunching –for that kind of money.