Sharing in Private

While there are many, many B2C ratings and review sites where consumers rate and otherwise report their experiences with businesses, there are relatively few B2B sites where businesses rate other businesses. There are multiple reasons for this, but prime among them is that while businesses tend to have a strong interest in using this kind of information, they typically don’t want to supply this kind of information. In short, they see competitive advantage in keeping their vendor experiences confidential.

One fascinating example of this in the legal market is a company called Courtroom Insight. Originally founded with the simple and reasonable idea of creating a website where lawyers could rate expert witnesses (experts hired by lawyers to testify in court), the company hit this exact wall: lawyers didn’t want to tell other lawyers about which experts they did and didn’t like.

Rather than close up shop, though, Courtroom Insights pivoted, in an interesting way. It discovered that large law firms were very sloppy about keeping records of their own expert witnesses. So, Courtroom Insights built a database of expert witness from public sources and licensed data. It then went to large law firms an offered them an expert witness management database. Not only could lawyers search for expert witnesses and verify their credentials, it could flag those experts they used, along with private notes that could be shared freely within the law firm, but not externally.

This pivot created a nice business for Courtroom Insights but it wasn’t done. Since all of its large law firm clients were sharing the same database, but also individually flagging the experts they were using, could Courtroom Insights convince them to share that information among themselves? Recently, they offered this “who’s using who” data to its clients on a voluntary, opt-in basis. And it worked. While not every client opted in, enough did so that Courtroom Insights could make another level of valuable information available.

While this is just my personal prediction, I think Courtroom Insights will ultimately be able to offer the expert witness ratings that it originally tried to provide. How? By using the protected space of its system to let lawyers trade this high-value information with each other. It will probably start small: perhaps lawyers could click a simple “thumbs up/thumbs down” icon next to each expert that could be shared. But I also suspect that if Courtroom Insights can crack the initial resistance to share information, the floodgates will open, because lawyers will realize they are communicating only with other lawyers, and because the benefits of “give to get” information exchange becomes so compelling.

The Courtroom Insights story provides a fine example of the power of what we call the Closed Data Pool in our Business Information Framework. Sometimes data that nobody will share publicly can in fact be shared among a restricted group of participants, with of course, a trusted, neutral data publisher making it all happen.

Top Level Domains/Low-Level Trustmarks

If you’re not immediately familiar with the term top level domain (TLD), think of “.com” and “.net” and “.edu” – they are all top-level domains, along with hundreds of others, and by the way, they are not limited to three characters anymore.

In the early days of the Internet, domain names were free for the asking, and I stocked up on quite a few for no other reason than a gut feeling they had some value. I did ultimately sell a lot of them, including several Fortune 500 companies who bought their corporate names back from me. By the time I realized there might be a bigger opportunity here, the rules of the game changed and big companies that had previously shown up with checkbooks now showed up with lawyers. Ah, well!

But for all my domain name hoarding, I couldn’t ever get domains names with the “.edu” TLD because they were reserved for schools. Similarly, “.net” was reserved for Internet Service Providers back then, and “.org” was reserved for non-profits. These distinctions were widely understood back then, and even today, I hear people telling me some organization “must” be a non-profit because it has a “.org” domain name. Old naming conventions die hard. More importantly, people are hungry for trustmarks.

But TLDs were never great trustmarks, for two reasons. First, validating an organization’s credentials before handing out a domain name is hard and expensive work. Second, domain names don’t sell for a lot, so you can only make money with volume. The pickier you are, the less money you make.

Despite this, the non-profit sector is now pushing the “.ngo” TLD. Think of it as a do-over of the “.org” TLD, because the operator of the domain is trying to limit sales to non-profit entities with the explicit hope that the TLD will become a trustmark over time. Similarly, the AICPA, the big association of certified public accountants, is in a fierce battle to control the forthcoming “.cpa” TLD, again with the hope it can restrict its use to certified public accountants and build it into a trustmark.

My view is that TLDs make for poor trustmarks. The economics make it hard to enforce standards, and there are too many sleazy operators in the business that drag down the credibility of TLDs across the board. The need for online trustmarks remains high. Who better than data companies to seize the opportunity?

Survey Says ... It Depends

The data for data products can come from a wide array of sources. Traditionally, datasets were compiled through primary research, usually via questionnaires or by phone. There is alsosecondary research, where staff gathers data using online sources. There are also public domain databases that can be leveraged. We have also seen a rise in technologically-driven data gathering, such as web harvesting. And a growing number of data publishers license third-party data to augment their data gathering. Almost anything goes these days, and the savviest data publishers are mixing and matching their collection techniques for maximum effectiveness. (a topic that will be addressed at the Business Information and Media Summit in November. )

This brings me to a question I have been asked more than a few times: can survey data be turned into a data product? When I talk about surveys, I mean the types of surveys most of us do routinely: you ask, say, 20,000 restaurant owners to answer questions about their businesses and the market generally, and if you’re lucky, you’ll get 1,000 responses. My take? While a survey does in fact generate data, I don’t think a survey automatically qualifies as a commercial data product. The reason is subtle, but important.

Much of the value of a data product is in its granularity and specificity. Typically, a data product focuses on organizations, individuals or products and attempts to collect as much detail as possible on each unit of coverage, as comprehensively as possible. Most surveys, by contrast, are anonymous by nature and hit-and-miss in coverage. Using our earlier example, a survey of restaurants might well be useful and valuable if it didn’t get any response from Taco Bell operators. A restaurant database without any listings of Taco Bell locations would have no credibility.  Since most surveys promise anonymity to increase survey participation rates, only aggregate reporting is possible. From my perspective, surveys of this type are useless as data products.

But not all surveys are the same. Some surveys ask respondents to list the vendors they use, or which of a specified set of companies they like the most and the least. Surveys where you ask the anonymous respondent to list or opine on specific companies or products actually can yield a very compelling type of commercial data product. That’s because the companies or products that come out of the survey effort are not anonymous. If the owner of the Blue Duck restaurant tells you that she likes National Restaurant Supply, you’re developing lots of valuable data about National Restaurant Supply that you can publish, even while keeping Blue Duck restaurant anonymous. Your survey data can report on attitudes or adoption or market share of specific products or firms and compare them and rank and rate them. That’s very valuable because the data are highly proprietary, difficult to collect and actionable.

My bottom line on surveys is that “traditional format” surveys with anonymous submissions and aggregate reporting are truly surveys, not data products. But if your survey asks respondents to tell you how much they use or like specific companies or products – you’ve got yourself the makings of a data product!

Bigger Is Not Always Better

One key dynamic of the data business is that the strongest businesses serve single, tightly-defined markets, typically a single vertical market. The result is that the market opportunity tends to be smaller, but it is much easier to stay close to and defend.

The problem for data publishers attempting to build products with horizontal coverage across multiple markets, or who want to play in large consumer markets, comes down to a very simple reality: it’s hard to be everything to everybody.

It’s instructive to look at some of the reasons why it’s so hard to achieve long-term success with broad-based data products:

Lowest common denominator: In order to operate efficiently, broad-based data publishers typically have to collect fairly standardized and fairly shallow data across multiple vertical markets. This creates an opportunity for other data publishers to “slice and dice” these publishers, peeling off the largest and most profitable vertical sub-markets, and serving the same need with deeper and more tailored data.

Greater incentive for competitors: If you achieve any level of success with a horizontal, broad-based data product, you’ve not only identified a big market need, you’ve identified a big market opportunity as well. That means it may well be worth it for a competitor to invest significantly to steal market share or push you out entirely. Contrast this with successful vertical market data publishers, where the small scale of the market is one of their best protections. Competitors typically can’t financially justify trying to push their way into small vertical markets.

Turning an ocean liner: In addition to being a juicy competitive target, an established broad-based data publisher typically succeeds because it has built an operation that over time becomes very difficult to change for technical and business reasons. That means it will be at the mercy of such forces as new technology, shifts in user preferences and new business models, and just a few competitive successes can break the momentum and market dominance of the incumbent data provider. Moreover, the incumbent data responder is only able to react slowly, if it can react at all.

Too cool for school: While some broad-based data publishers become exposed because they can’t react quickly, others expose themselves by innovating so aggressively they get ahead of their markets and their customers. In a relentless quest to stay relevant and ahead of the competition, these publishers roll out features and functionality that their customer often don’t understand or even want, adding complexity to the user experience while muddying the core value proposition.

Platform envy: Perhaps encouraged by the spectacular success of Amazon, it’s easy to take the view that your data product can become a data platform, a way to distribute all kinds of data, products, whatever. That’s a big leap technologically, and while platforms are enticing to publishers, they almost inherently mean diffused focus, thus opening opportunities for competitors to enter the market with more focused products.

The most successful data publishers and products I see these days tend to serve one market and serve it extremely well. As long as these businesses stay close to their customers, evolve their products regularly and prudently, and offer good customer support and fair pricing, they can be enormously profitable while remaining largely immune to competition. That’s why in the data business at least, bigger isn’t always better.

The Roomba Ruckus

Roomba, the robot vacuum cleaner that took advanced technology and applied it to the consumer market by trying to eliminate the lowly task of vacuuming, has been in the news recently. Apparently, its devices suck up more than the dirt in your home: they are sucking up data about your home as well. And Roomba is starting the think about selling this trove of data.

There are several aspects to this development that merit discussion. First, of course, there’s the privacy issue. Roomba was forward-thinking to the extent that it buried appropriate language in its privacy agreement that allows it to do pretty much anything to the data it collects. However, that language wasn’t prominent and was written in legalese. In short, while it may be legal for Roomba to sell customer data, it wasn’t up-front and transparent with its customers.

Right now, most pundits are saying that convenience trumps privacy every time. That may be true currently, but I expect consumer attitudes will begin to shift as the nature and extent of furtive data collection fully penetrate the collective conscience.

Exactly what data does Roomba collect and how valuable is it? I have said many times that not all data are valuable, and while Roomba certainly has a trove of data, I am not convinced it is a treasure trove of data. Many articles on the subject talk breathlessly about this goldmine of “room geometry” data. Specific potential uses (of which very few are mentioned – a big clue right there) are such things as designing speaker systems. Sounds legit, but can Roomba tell you the ceiling height of the room? Can it tell you what rooms play music now? There are lots of clues that these data may not in reality be all that useful.

And who would buy these data? The articles are equally breathless on this subject, suggesting that of course Amazon would want it. Others suggest Apple will snap it up, and perhaps Home Depot as well. If you step back, all you see is a list of big companies with products for the home.

The increasingly common view that every company, including manufacturers, is expected to have a data strategy, is trendy, silly and will ultimately collapse. Not all data are valuable, and having huge quantities of not-valuable data doesn’t change that fact. And when you consider that to gather these data you risk a privacy backlash and reputational damage, companies (and those who fund them) will ultimately start to realize that not all data are created equal. Only a fortunate few can casually generate high-value datasets, and even then, it’s not cost or risk free. My prediction: Roomba won’t be cleaning up with data anytime soon.