Transforming Data the Humin Way

Imagine launching a start-up that is touted as a pioneering “social operating system” a key player in the burgeoning area of “contextual computing” and even a “digital butler.” Let’s go even further, and imagine the burden of having to live up to the goal of “organizing the world” and most intriguing of all, building “a master contacts database for pretty much the entire world?” Well, if you can in fact imagine living up to expectations like this, you’ll probably want to apply for a job at a company called Humin.

On a more practical level, Humin (at least for now) is an app that grabs your contact list, calendar entries and social networks to build a master list. It then automatically contacts everyone on the list and asks them to confirm their details and provide additional information. Once all these data are confirmed and unduplicated, you get a contact list that can be searched by location, by connections (who knows who) and a lot of other ways that go far beyond the typical address book.

To live up to its contextual computing hype, Humin wants to move into push mode. Fly into Cincinnati, for example, and it will present you with a list of your contacts there. Humin will of course get smarter as it begins to find deeper meaning in both the data itself and how you use it. Privacy concerns? Not to worry. Humin hangs onto only the minimum amount of data needed to do its magic – all the most valuable data stays right on your phone.

Those of you who are students of data may see shocking similarities between to an earlier service called Plaxo. In its original incarnation, Plaxo grabbed your address book and would periodically query everyone in it via automated emails to confirm that their details were current. Even more cool, if you updated your own information, Plaxo pushed it out to all your contacts automatically. It was the original globally synchronized contact list. Ultimately, Plaxo went astray, jumping on the social media bandwagon in a failed attempt to challenge Facebook.

The lesson of Humin (beyond possible confirmation that all great data publishing ideas are derivative), is that while Humin may be loosely based on the Plaxo concept, it is moving aggressively to surround data with tools. Humin isn’t just organizing and tidying a giant pile of data and then asking the user to find value in it – it is innovating in multiple ways to do that thinking for the user, and to deliver the right data in the right format at the right time to offer maximum value. We at InfoCommerce Group call it “data that does stuff.” Surround good data with good tools, and you, too, can become master of the data publishing universe.

People Power

There was news this week about the formation of Bloomberg Beta, a new venture capital fund sponsored by data company Bloomberg LP. One of Bloomberg Beta’s early investments is a company called Newsle, that will alert you whenever someone you specify – a friend or colleague – is in the news. This is a tough nut to crack. Searching thousands of news sources and trying to determine if the John Smith mentioned in an article is the same John Smith of interest to you is a complex undertaking. But what really intrigued me is that those who have written about Newsle see another major problem that the company faces: lack of activity. Think about it. If you import your list of Facebook friends (something Newsle encourages you to do), the chances of any of them appearing in news stories is pretty low. That means most people will sign up for Newsle and nothing will happen, not because Newsle isn’t working, but because there is no news to report. It’s hard to establish the value of your service if you’re not delivering at least a little something every now and then.

That’s why in addition to your Facebook friends, Newsle also encourages you to import your LinkedIn contacts, and while you are at it, your address book as well. Somewhat incongruously, Newsle also encourages you to follow politicians and celebrities. The hope is the more people you track, the more likely you’ll get hits.

But what if Newsle flipped its model? Instead of serving individuals who for the most part have small lists of mostly boring contacts, why not hook up with commercial data publishers, many of whom have tens and even hundreds of thousands of contacts in their databases? Publishers could then send real-time alerts out to their subscribers who are interested in specific people or any activity relating to executives at a given company. In addition to sales intelligence, these news alerts could also provide a basis for making contact with a prospect. Plus, publishers could database these news events to build deep profiles on company executives that would have evergreen value.

This could be a great opportunity for Newsle to crack its volume problem, and for data publishers to add in high-value alerting services and historical data all in one fell swoop.

That’s powerful, people!

Enigma: Disrupting Public Data

Can you actually disrupt public data, which by definition is public, and by extension is typically free or close to free? Well, in a way, you can. A new start-up called Enigma, which can be thought of as “the Google of public data,” has assembled over 100,000 public data sources – some of them not even fully or easily accessible online. Think all kinds of public records from land ownership, public company data, customs filings, private plan registrations, all sorts of data, and all in one place.

But there’s more. Enigma doesn’t just aggregate, it integrates. That means it has expended tremendous effort to both normalize and link these disparate datasets, making information easier to find, and data easier to analyze.

The potentially disruptive aspect to a database that contains so much public data is that there are quite a few data publishers with very successful businesses built in whole or in part on public datasets.

But beyond the potential for disruption, there’s some other big potential for this (I’ve requested a trial, so at this point I am working with limited information). First, Enigma isn’t (at least for now) trying to create a specific product, e.g. a company profile database. Rather, it’s providing raw data. That will make it less interesting to many buyers of existing data products who want a fast answer with minimum effort. But it also means that Enigma could be a leveraged way for many data publishers to access public data to integrate into their own products, especially since Enigma touts a powerful API.

The other consideration with a product like this is that even with 100,000 datasets, it is inherently broad-based and scatter-shot in its coverage. That makes it far less threatening to vertical market data publishers.

Finally, Enigma has adopted a paid subscription model, so it’s not going to accelerate the commoditization of data by offering itself free to everyone and adopting an ad-supported model.

So from a number of angles, this is a company to watch. I’m eagerly waiting for my trial subscription; I urge you to dig in deep on Enigma as well.

A Plug and Play Publishing Platform?

Dun & Bradstreet Credibility Corporation, an independent company with such an extensive relationship with Dun & Bradstreet that it was even granted use of the vaunted D&B name, has been targeting smaller businesses with not only traditional D&B credit products, but a beta offering of what might be called a “next generation credit rating,” a so-called credibility score that examines the company from a number of different non-financial perspectives, yielding a letter grade and presumably an online trust mark that companies can use to build confidence with both suppliers and customers. It’s a clever and ambitious concept. And there are some serious resources behind this venture: Boston-based private equity firm Great Hill Partners is backing the venture with in excess of $100 million. In an apparently related development, D&B Credibility recently announced the launch of the “Credibility Review Business Marketplace,” an innovative move to partner with publishers to extend the reach of its credibility ratings, by turning B2B data publishers into a sales channel. D&B Credibility indicates a number of publishers have already signed onto this program.

I’m still waiting to get full details on this program from the company, leaving me free to speculate wildly, a favorite pastime. Here’s what I picture:

D&B Credibility has licensed access to the full D&B business database, and this provides a content backbone to the initiative. When it emerges from beta, D&B Credibility will presumably move to aggressively sell credibility scores to smaller businesses. Each sale yields a richly detailed business profile (part of the score involves “transparency,” so participating companies are obliged to supply all sorts of useful information – smart!) that the participating company is highly motivated to keep current (yielding high leverage user-generated content). These enhanced listings are added to the basic listings in the content backbone.

To accelerate adoption of the credibility scores, D&B Credibility will partner with publishers on an intriguing offer: a self-maintaining database offering a growing number of credibility scores, that the publisher can access for free in exchange for selling credibility scores (and anything else it wants) to companies in its vertical market.

As I envision it, publishers would simply flag the companies they want to appear in their vertical market buying guides, getting in effect a customized view of the larger database. The publisher codes each company against its own vertical market taxonomy, and presto-whammo, it’s got a high quality database that costs almost nothing to build or maintain. All it has to do is sell the credibility scores and other advertising to companies that it has flagged. For trade magazine publishers in particular, selling ads is a true core competency, where database development and maintenance is not.

What’s in this for D&B Credibility? It gets a revenue cut from every credibility score a publisher sells. It gets all the company information being collected (everything goes into its backbone database), and it gets valuable help in building momentum and acceptance for its scores.

Is this a good deal for publishers? When it comes to vertical market buying guides, the majority of publishers have unevenly maintained databases with limited company information. This approach not only goes a long way to solving the twin issues of data quality and data depth, it also provides the ability to sell a new and useful offering – a B2B trust mark.

Fascinating stuff, and well worth watching as the product rolls out from beta.

Walking Around Money

A young company called Placed is deep into Big Data analytics, but with a twist: it marries customer data with its own proprietary data to yield insights into customer behavior. Essentially, Placed wants to provide context around how customers use the mobile applications of its clients, for example, when do they use the app and where do they use it?

The “where” part of the analysis is what’s interesting. Placed could simply spit back to its clients that its customers are in certain ZIP codes or other dry demographics – interesting, like so many analytics reports are, but not particularly useful.

Instead Placed marries customer location with its own proprietary database of places – named stores, major buildings, points of interest. By connecting the two, Placed can tell its clients where mobile use of its app is occurring. For example, if a client’s customers utilize its mobile app in a competitor’s store, it might suggest competitive price comparisons. Knowing its customers frequent Starbucks and nightclubs might influence the clients’ marketing strategy or advertising campaign design. Knowing that the app is used most often when someone is walking (yes, Placed can tell you that) can be important for user interface design – you get the idea.

And therein lies an important insight. There are an endless number of companies offering Big Data analytics capabilities. But almost all of them expect their customers to bring both the problem and the data. That’s a sure recipe for commoditization, and as analytics software evolve, it’s also certain that the companies with the biggest analytics needs will decide to do the work themselves.

Solution? Big Data analytics players should bring proprietary data to the party. Placed is a perfect case study. It differentiates itself by providing answers others can’t. It adds value to its analytics by integrating proprietary and licensed data with customer data and its own optimized analytical tools. As I discussed in my presentation at DataContent 2012, there are lots of ways publishers can profit from the Big Data revolution -- even if they don't have big data themselves.

In a market where companies like Placed can make money by tracking people walking around, it behooves data publishers to walk around to some of these Big Data analytics players and suggest data partnerships that will help them stand out from the crowd.