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.

Data as the Decider

I have discussed before how data providers can leverage their central, neutral market positions to collect highly valuable data that otherwise couldn’t be collected. Examples abound of data providers that have convinced companies to provide them with their information crown jewels – sales data, pricing data and the like – in return for getting it back (on a paid or unpaid basis) in aggregate, anonymized form. Fundamentally, the companies realize that their data, no matter how sensitive they consider it to be, has even more value to them when combined with or compared to a larger set of similar data. These situations are wonderful opportunities for data publishers, and they are cropping up more and more as companies get better about organizing their internal data and then become more sophisticated about how to optimize it.

But there is a level above this enviable market position. It’s when data actually starts to drive commercial transactions. I have worked with companies whose data products actually drive the bonus compensation of salespeople and managers across entire industries. I have seen data products that are used to set valuations of companies for sale. And of course, there are industry giants such a Nielsen, with its well-known television ratings that drive billions in ad dollars.

The commonality among this rarified group of data providers is that their data is survey-driven. These companies leverage not only their neutrality and impartiality, but they are gathering data that no individual organization could easily or credibly collect on its own. In many cases, these data companies are gathering customer and user experiences and actions.

Yes, for the right kind of opportunity, a simple survey can be turned into an extraordinarily valuable data product. Again, the key drivers of such opportunities are: 1) a need to gather customer/subscriber/user opinions/ratings/activities; 2) the information is difficult for industry players to gather themselves; and 3) the need for trust and objectivity in the collected data.

It may sound hard and complicated, but in the right situations, a well-executed survey can be the path to a very valuable data franchise.

Good Databases Are More Than Just Good Data

We can look to the UK for a case study of how a government agency, after several tries, couldn’t build a user-friendly data product, creating a giant opportunity for a for-profit data company.

The story begins with a regulatory agency called the Financial Conduct Authority (FCA) that among other duties, registers and regulates financial advisors and advisory firms. The FCA has a searchable database on its website, but like so many government websites, it is optimized for one purpose: checking the registration status of a known individual or firm. As a tool to assist you in identifying an advisor to help you with your investments, it’s pretty useless.

In recognition of this shortcoming, the FCA called on a quasi-governmental organization called the Money Advice Service (MAS) to help build a better adviser database, and MAS accepted the challenge. I took a look at this website when it first launched, and though I saw some design issues, it had potential.

But even though MAS nominally had the freedom to build a creative database with almost any business model behind it, its need to avoid controversy ultimately resulted in a very limited and timid product. And when, unsurprisingly, there wasn’t a lot of revenue to be had with such a product, MAS buried the database three levels down on its website and moved on to greener pastures.

With two free databases of financial advisers out there, you think there wouldn’t be much opportunity left for anyone. However, a company called Unbiased saw things differently, and said there was indeed an opportunity … for the right product.

Unbiased has been a big hit in the marketplace, and the way it differentiated itself from the free government services with the same basic listing data holds lessons for us all

• Greater visibility – Unbiased wants to be found because its business model depends on driving lots of traffic to its participating advisers
• Deeper data – ratings, discount offers and detailed profiles
• Strong user interface – clean, inviting design and both parametric search and a custom matching service         

If you have ever wondered how you could compete against a free, government online database, Unbiased provides the answer: data presentation can be as valuable as the underlying data itself, particularly if you are serving a consumer market. And aggressive promotion of your online database will let you run circles around government agency databases, that are generally hard to find in addition to being hard to use. 

Inferring Intent

Today’s Gartner blogpost points to some interesting limitations and opportunities surrounding intent data. Let’s start at the beginning by defining what it is.

Simply put, intent data is an indication that an individual or organization is actively interested in purchasing a specific product or service. You may already be familiar with sales triggers. One classic sales trigger is so-called “new move” data. It’s valuable to know when a company moves offices because it is highly likely that the company will likely make lots of new purchases such as office furniture and the like. Think of intent data as a more sophisticated cousin of the sales trigger.

Media companies are in a great position to generate sales intent data, because much intent data is generated by watching what a person reads and does online. If a reader looks at five articles on 3-D printers in a short period of time, those actions can be viewed as indicating an intention to purchase a 3-D printer. Intent data can get a lot more sophisticated than that, but this gives you the general idea.

You might think that if a sales organization has intent data available to it, that’s probably all the data it needs. After all, intent data is like mind-reading: it’s identifying people who are likely to be purchasing a product before they purchase it. What could be better?

Well, as the Gartner blogpost points out, many companies are filtering sales leads based on intent data with something called “fit analysis.” This is an automated attempt to evaluate if the company is a likely buyer. If your company typically sells to larger, multi-office organizations, a fit analysis will filter out smaller, single location companies because they represent lower grade prospects.

Further, the Gartner blogpost notes that companies selling highly specialized products or brand-new technologies often can’t get enough intent-based sales leads or they get leads that are weak because the intent indicators aren’t sufficiently granular. Finally, some sales departments don’t like intent-based sales leads because they identify prospects too early in the sales process. As you can see, sales leads based on intention are still fairly rudimentary, and there is lots of opportunity to refine them.

But what’s most worthy of note is that Gartner believes that most intent-based sales lead data is focused on the technology industry. But there is no reason that it should. Technology sellers just happen to be free-spending early adopters. I have long preached the virtues of what I call “inferential data,” a term that includes both intent and sales trigger data. I firmly believe that many data publishers have opportunities in this area, and if they happen to be part of larger media companies, they are even greater. In fact, data publishers are natural providers of fit analytics as well. If you look at your data creatively and read between the lines you can make some very lucrative connections. 

Data Democratization: A Timely Trend That Empowers Users

“Democratization” is the latest trend in data. While it is rapidly acquiring multiple definitions, the one I find most useful suggests that there is a growing opportunity to open up complex datasets to people who could benefit from them, but haven’t traditionally used them.

With this definition, data democratization usually involves some combination of pricing and user interface design. Reduced pricing is meant to make a data product more broadly accessible, and user interface design is about making the data incredibly easy to use. Putting these two together, those employing a data democratization strategy believe they can significantly expand their markets. In addition, a powerfully simple user interface should result in reduced support costs by enabling less sophisticated data users to start getting the answers they need directly, by themselves.

The best opportunities for data democratization? Look for data silos.  The data provider combines several datasets, doing all the complex normalization and matching that is required. The user interface then lets users painlessly do what amounts to cross-tabulation and filtering with all the complexity carefully hidden. Results are usually in the form of highly visual data presentations.

Data democratization is not “dumbing down” data. Indeed, a democratized data product often has all the power of much more complex and expensive business intelligence (BI) software. The nuance is making the user interface more accessible and less scary, and reducing the price point so that the product isn’t a major purchase decision.

You can see an analogy of sorts with what happened with computers, moving from centralized, expensive installations operated by a few with specialized skills to the amazing desktop computing capabilities we all enjoy today. Whether data democratization is an opportunity of the same scale and profundity as the computer revolution is unclear, but it certainly bears close watching because this is a strategy with a powerful first-mover advantage.

To see a great example of data democratization, check out one of this year’s Models of Excellence, Franklin Trust Ratings.

Better yet, meet the founder behind it. John Morrow, at this year’s Business Information and Media Summit, Nov. 13 – 15 in Ft. Lauderdale. There will be lots of other data trendsetters there too!