Chief Marketing Technologist

Data as a new marketing channel

Marketers love data. Web analytics, behavioral targeting, social media monitoring, audience measurement, and ever-expanding CRMs are all testament to the demand for more data about prospects and customers. But that kind of data flows one-way: from the outside world into marketing.

However, a new way of marketing with data is emerging that heralds a more significant shift for marketing — and for the web itself. It's about reversing the flow, having marketing publish data to the outside world, as a way to increase visibility, grow relationships, and build a brand. Call it data web marketing.

Data web marketing is the product of several diverse forces: search engine optimization (SEO), web service APIs and mash-ups, the semantic web and linked data, and a new generation of data sharing and visualization sites. The common thread among all of them is data — particularly structured data — in formats that are easy for software programs to consume. Let's take a closer look at how these different data initiatives fit together in this new kind of marketing.

Metadata for Finding and Sharing

Much of digital marketing has been centered around the production of compelling content and the promotion of such content through search, advertising, and social media. This content — text, images, video, audio — is designed for people to consume directly: reading, watching, listening. But as search engines and other agent-like services know well, processing that kind of freeform content with software into other useful applications can be challenging.

To overcome that challenge, web publishers — a label which includes most digital marketers — are increasingly encouraged to tag their content with metadata, describing it with machine-friendly RDFa or microformat vocabularies. For instance, Facebook's Open Graph Protocol leverages such metadata to facilitate sharing among friends and like-interested communities. Google Rich Snippets and Yahoo SearchMonkey leverage such structured data to improve search results for locations, people, reviews, events, and more.

Marketers who tag their content with such metadata are rewarded with better visibility in search results and social media streams.

This is the first step towards data web marketing. It's an incentive for marketers to start to think about deploying structured data on the web in a consistent and logical fashion. These skills are currently lumped into SEO and social media optimization (SMO). But as the metadata vocabularies used get more sophisticated — like the GoodRelations vocabulary adopted by several major online retailers such as Best Buy — the art of organizing and maintaining this metadata becomes a more significant mission unto itself.

Data APIs for Apps and Mash-ups

The next incarnation of data web marketing is evident in the proliferation of web APIs. For years, such APIs have been provided by major web players such as Google, Amazon, Facebook, and Twitter as a way to strengthen their third-party ecosystems. Enabling mash-up developers to remix key pieces of their content has helped those companies build their brands, drive affiliate revenue, and spur innovation around their core services. In the case of Twitter, these APIs have arguably been paramount to their success, as most people tweet via one or more third-party apps.

But now, organizations as diverse as The New York Times, Netflix, Compete, and Wine.com are offering APIs too. The API directory ProgrammableWeb lists over 2,000 APIs from Adgooroo to Zillow. There's even a thriving market of service providers — including Mashery, 3scale, and Sonoa Systems — to help companies launch and maintain these APIs. What's driving this?

Although APIs are a technical endeavor, the reasons to offer them — and the decisions about what to provide and under what terms — are rooted firmly in marketing. Generally, the motivation is one or more of these objectives:

• generate traffic back to the organization's web site
• build brand authority by being the "reference source" in a particular area
• drive affiliate revenue with embedded links for e-commerce transactions
• strengthen relationships with power users and their respective communities
• accelerate feature innovation beyond internal development resources

The explosion of apps for the iPhone, iPad, and Android devices has further increased interest in APIs. Developers crave new sources of data they can mash up in cool, innovative ways, and publishers (i.e., marketers) are eager to extend their reach in this burgeoning app-sphere.

Here is where the shift towards outward-flowing data clearly manifests itself. Via APIs, businesses are publishing data — sometimes metadata about content, but in many cases the data itself as the content — as a marketing tactic. The quality, reliability, and usefulness of that data impacts the brand of the company that is publishing it. Therefore marketers should take a proactive role in the design and management of such services.

It's worth noting that the distribution of data this way is a kind of "channel" to prospects and customers. Often the people who will actually buy a company's products and services are not the people who will directly consume these APIs. Rather, the target audience is the people who will use the widgets and apps built with these APIs. The developers leveraging these APIs are, in a manner of speaking, distributors for the electronic essence of the publisher's brand. This raises many of the classic challenges of channel marketing in a new context — balancing the needs of two very different yet interdependent constituencies.

Data as Social Media

But the migration of data from a back-office workhorse to a front-office ambassador is perhaps most apparent in the rise of data sharing and visualization sites such as Factual, Infochimps, Freebase (which was recently acquired by Google), Many Eyes, and Swivel.

These sites turn data into social media. What YouTube does for videos and Slideshare does for presentations, these sites do for data sets — let people share, search, and associate them in the social sphere. But data, unlike videos or slide decks, also lends itself to community contributions in assembling and editing data sets, enabling crowdsourced data management.

The people consuming these data sets include some of the same developers using web APIs. However, these data sets are also being downloaded by non-programmers, regular desktop users, who use them to feed spreadsheets and other business analysis applications. This broader audience demonstrates the real value data on the web can provide. Many of us crave data in our work — it helps us make decisions and persuade others. But to date, it's been very hard to find such data in its raw form. Being able to quickly search social repositories for relevant data is a significant step forward.

Of course, the moment you have an engaged audience consuming something — in this case, data — marketers will have an interest in piggybacking on that channel to reach them. This can be a good thing, if marketers are incentivized to bring something of value to the channel. What value could marketers bring to a data web? I'd propose: good data that their audience would find useful.

As with content marketing, the strategy of such data web marketing has to be publishing things that are genuinely valuable to the intended audience — otherwise, no one's going to bother to consume it. Such data might include industry research that the company has sponsored, aggregate performance data that customers can use for benchmarking, knowledge bases to support specific applications, and so on. Like web APIs, the benefits to marketers for producing and distributing such data are brand building, relationship building, and generating pull-through for their products and services.

Converging in the Semantic Web

So where does the semantic web fit in data web marketing? It could be argued that the semantic web — specifications such as RDF and OWL, SPARQL, and the vision of linked data — has been an elegant solution waiting for a mission. Outside the walls of enterprise IT and certain scientific communities, there just hasn't been enough concrete demand for directly sharing data on the web. Yet.

But as publishing data becomes more popular in the contexts discussed above — SEO, web APIs, and data as social media — the advantages of adopting semantic web standards become more apparent as well. The problem with web APIs is that they're different for each publisher, requiring developers to explicitly build and maintain their software for each one. Similarly, the data sets shared on sites like Factual and Infochimps are currently independent of each other — each its own little silo — that makes it difficult to discover and leverage relationships between them.

The semantic web was designed to address those problems. As momentum in data publishing grows, the attractiveness of its standards will grow as well, making it easier to publish and consume new data sets. By providing a common language for data, the semantic web also makes it easier for different data sets to be related to each other and to enable those relationships to be programmatically discovered by software. This is why the semantic web movement is also called "linked data."

Perhaps most exciting, however, is that the semantic web enables all of the above data publishing scenarios to converge. SEO and SMO are already leaning towards the semantic web standard of RDFa over ad hoc microformats for encoding metadata on web pages. In addition to its web API, The New York Times now offers its data in RDF. At least one social data site, Freebase, offers an RDF interface as well. Leveraging the same formats across these different scenarios makes it easier to reuse data and software across all of them — instead of many separate pockets of online data, we can begin to view a more cohesive web of data.

In conjunction with independent community initiatives such as DBpedia and open government data programs in the U.S. and the U.K., a critical mass of such data is emerging. Like Metcalfe's Law for networks, the more data there is in a common format — particularly one that facilitates interrelationships — the more valuable this web of data becomes. That encourages more developers to create more applications, which in turn inspires more publishers to participate by sharing more data, and a virtuous cycle can ensue.

The New Wave of Data Web Marketing

Many aspects of digital marketing today are tactical in nature, with relatively technical details driving their implementation. For instance, most CMOs and VPs of marketing don't think about the techniques involved in good SEO. But they do care about ranking favorably on Google and Bing for topics that are core to their brands.

The fledgling scenarios of data web marketing described above, when examined individually, are largely tactical too. But when viewed collectively, they reveal a tectonic shift towards data as a new wave of web content — one that is powering a new generation of smart applications on the web and mobile devices. At that level, there are strategic opportunities that deserve executive consideration.

Just as companies who were quick to embrace the web, search marketing, and social media used that as a competitive advantage, a similar dynamic is likely to exist with the web of data.

• Whose data will be the authoritative source in each market out there?
• Whose data will be embedded in the most exciting third-party applications?
• Whose industry-specific vocabularies will become de facto standards?
• Whose open data will be most linked to within other data sets, generating a positive feedback loop around a data-level brand?

Those are strategic questions that marketers should begin thinking about.

The shift to data web marketing will take a little getting used too — we've had 30 years of clinging to data as something to be squirreled away. Gaining organizational experience in the web-facing data tactics above is a good start, but more forward-thinking leaders in marketing should encourage their teams to connect the dots between these initiatives. They should contemplate what Tim Berners-Lee said in an interview with Talis, advising company leaders on what the semantic web can really mean for them:

You should take an inventory of what you have got in the way of data and you should think about how valuable each piece of data in the company would be if it were available to other people across the company, or if it were available publicly, and if it were available to your partners.

That is the essence of data web marketing.

3 nimble trends changing content and marketing

Razorfish just released a great new report, Nimble: Publishing in the Digital Age, written by Rachel Lovinger. The target audience is clearly large publishers and mega media companies, but there are important trends here that are relevant to everyone who thinks of distributing content on the web — e.g., anyone in marketing.

The three key points that pop out to me — particularly because I keep seeing them in a lot of posts and discussions these days:

1. The concept of "the web site" is becoming less important than the underlying content and its social propagation.

People now consume online content on a variety of devices, from their computers, their iPhones and Android phones, and new tablets like the iPad. They consume content not necessarily from the site of the content producer, but from social media hubs such as YouTube, social networks such as Facebook, aggregators such as Alltop, and a wave of social sharing services such as Twitter, Buzz, LinkedIn from which it is reblogged and reposted (or reshared as the term du jour).

You need to design your content — and the process and metrics by which you generate and maintain that content — to be effortlessly absorbed into this malleable and organic distribution engine in the cloud. It's a different paradigm than the closed walls of the 1990's web site.

Quoted in the report is Nic Newman of the BBC:

You can't afford to [create] a piece of content for any one platform. Instead of crafting a website, you have to put more effort into crafting the description of an asset and the different bits of an asset, so they can be reused more effectively, so they can deliver more value.

2. Structured data and metadata around content are becoming increasingly important.

As Facebook's Open Graph protocol is demonstrating, even just a little bit of semantic markup associated with your content can go a long way in making your content more friendly to find, share, and curate. This makes it much easier for you — and others — to adapt your content in a variety of containers, beyond the static information architecture of your web site.

Forget the fancy names of "semantic web" or "linked data." Associating structured data with your content assets lets you take advantage of Open Graph, Google RichSnippets, Yahoo Search Monkey, and a new generation of agents such as Siri. Disseminating your content with metadata through APIs enables developers to spread the seeds of your brand in a variety of mash-ups and apps. Sharing your data sets in collaborative venues such as Factual and Infochimps helps build relationships with the world of analytic power users, improve your data quality, and turn those dusty data silos into tools for advocating ideas and brands.

And with the coming data explosion (already 281 exabytes of online data in 2009!), this type of self-guiding roadmap within and around your assets will soon be essential for even you to find and use the right content in the right place.

3. Content producers must become much more nimble (i.e., agile).

When many people come to the same conclusion from independent streams of research, you know a major movement is in progress. Rachel's overarching advice to publishers is to push to be more nimble — or as I'd call it, more agile. And, as you've probably noticed, a lot of people have been talking about business agility this year.

I've been banging the drum for more agility in marketing — such as a call for an agile marketing manifesto — because in a world where everything is in constant flux, your only reliable source of competitive advantage is your ability to adapt. Fast. That's easy to say, but it requires cultural and managerial shifts that are still quite foreign to most organizations. It's a change in metabolism that needs to be taken seriously and mandated from the highest executive levels.

As Rachel writes in her report:

To succeed as a digital content producer you need to be nimble. Many companies will say that they're nimble, but very few actually are. Being nimble is about the ability to adapt quickly to the new challenges and opportunities in today's media ecosystem; things like the explosion of new media devices, the world-domination of social networks, and consumers' growing expectation of first-class digital experiences.

Being nimble is not just about an organization. It's about the industry's business models. It's about production processes. It's about the content itself.

Nimble is a thought-provoking report, well-worth reading and considering how it applies in your domain.

(It also includes a reference to my post on business models for linked data and Web 3.0, which was inspired by discussions and feedback from Rachel as well.)

15 sessions on marketing and the semantic web

Looking for a conference that really pushes the envelope of digital marketing?

Last year, I gave a talk at the Semantic Technology Conference on marketing in the semantic web. At the time, the topic was in its infancy. But as predicted, a lot happens in one year. This June, at the 2010 Semantic Technology Conference in San Francisco, there is now a whole track dedicated to this subject, Marketing in the Web 3.0 World. And, I humbly say as the track's chair, it's going to rock.

With Facebook, Google, Yahoo, and Bing now all leveraging semantic web data — Facebook's announcement of Open Graph being the latest major development — the timing couldn't be better.

While the overall conference — which runs a full week — dives deep into the underpinnings of the semantic web, this track will focus on the higher-level applications of this technology in marketing and advertising. It's still not for the faint of heart, but it will be the "real deal." If you want to be on the cutting edge of leveraging linked data and the semantic web in your digital marketing, this is for you.

Here are a few highlights of the 15 sessions in this marketing track:

David Siegel, author of the terrific book Pull: The Power of the Semantic Web to Transform Your Business, will give the keynote. David has been one of the leading advocates for the potential of the semantic web to change business — tapping into the accelerating shift from push to pull marketing models.

Kavi Goel from Google will give an hour-long presentation on Google Rich Snippets — what promises to be one of the most thorough examinations yet of how marketers and publishers can embed structured data into their web pages to make them stand out in Google search results. Tactically, I expect this session to be worth its weight in gold.

Jay Myers of BestBuy.com — whose e-commerce success with linked data has been widely hailed — will join Barbara Starr and Paul Bruemmer who worked on Overstock.com for a session on leveraging RDF and the GoodRelations vocabulary in e-commerce SEO. These presenters have tremendous real-world experience with the state-of-the-art in semantic web and search engine optimization.

Gil Elbaz of Factual and Dhruv Bansal of Infochimps will join me for a session that steps back from the semantic web and looks more broadly at using the web to share data — and collaborate on it — with customers and partners. This is Data as the New Marketing Channel, and when you see the examples these presenters have, I know you'll be as inspired as I am.

David Bean of Attensity, Shion Deysarkar of Extractiv, and Dmitri Soubbotin of Semantic Engines will give a powerhouse presentation on sentiment analysis and marketing intelligence powered by semantic technologies. These are three brilliant minds on the leading edge of social media monitoring, who will show you what's possible today... and in the near future.

And there's so much more:

• Ian Davis and Michael F. Uschold on linked data business models
• Brooke Aker, Amiad Solomon, and Ian Saunders on semantic advertising
• Chris C. Arning on semantic technologies for market research
• Jamie Taylor on the social fabric of semantics
• Mark Leyden on the legal implications of linked data
• Lars Hard and Brooke Aker on mobile semantics

If I could bring together two dozen of the most fascinating figures in the semantic web and its intersection with marketing, this would be them. I'm thrilled that they will all be there in San Francisco for some terrific talks and discussions.

If you'd like to join us, you can use the special code ST10SPKR to save $200 off registration.

P.S. If this is all new to you, you might want to start by taking a look at the presentation I gave earlier this year at the Web 3.0 conference on Data Marketing for Web 3.0. There's also a video interview available as well.

Business models for linked data and web 3.0

In January, I wrote a post on 7 business models for linked data (with an 8th model added shortly thereafter). Although far from comprehensive, it attempted to illustrate the range of direct revenue vs. indirect revenue models that could justify development of linked data initiatives.

A number of people from the linked data community contributed feedback and suggestions of additional models, including Leigh Dodds (Thoughts on Linked Data Business Models), Paul Groth (Another 5 Linked Data Business Models), Eric Hellman (8 One-Way Business Models for Linked Data), and John S. Erickson (The Evolution of Linked Data Business Models).

However, some of the best feedback came offline from Rachel Lovinger at Razorfish (and author of the blog Meaningful Data). Over a series of discussions, we developed an expanded model:

There are three dimensions in this model, which we phrased as questions:

1. How direct is our revenue? This is the Y axis of the chart. At the top are ways of directly being paid for data, such as licensing and subscriptions. Toward the bottom are more indirect revenue sources, such as using data to drive traffic to a web site or build one's brand and reputation.

2. Who do we provide our service to? Along the X axis are three categories that identify, from left to right, the openness and accessibility of the service.

On the far left are private partner relationships, where data is provided in closed relationships with partners who use it internally or incorporate it into other offerings downstream. In the middle, data is provided via APIs to a more open developer community, who in turn leverage the data in more public facing applications. And on the far right are web sites that regular users visit or subscribe to, without requiring any technical expertise on their part.

3. Who do we get revenue from? For any given model, the direct user of the service and the source of revenue are not necessarily the same. For instance, with a subscription model the users and the revenue source are the same; with an advertising model, they're usually two different constituencies.

We used colors to distinguish four classes of revenue sources: blue from direct customers, purple from partner relationships, green from advertisers, and red from "subsidized" sources such as government or non-profit mandates.

In this landscape, we identified 15 business models that offer a good representation of the different ways in which organizations can monetize — directly or indirectly — data publishing initiatives:

1. Subsidized/public service: funded by a government, an NGO, or a regulatory mandate — revenue = funding.
2. Licensing: charge fees to let developers use data in other environments.
3. Microtransactions: on-demand payments for individual queries or data sets.
4. Subscriptions: charge for access to data for a period of time (may have tiers for different levels of access).
5. Freemium: free but limited access to data to sample, but charge for extended premium access.
6. Paid inclusion: charge to be included in the data set or attributed valuable meta-data (what I formerly called an "authority" business model).
7. Sponsorships: charge a small number of advertisers for brand visibility of sponsoring the data.
8. Advertising: charge for ads placed around data on web pages (may also tap into ad networks).
9. Marketplace: provide data to a partner service in exchange for an opportunistic royalty.
10. Affiliate program: provide data streams to affiliates who distribute them in other applications in exchange for commissions on related sales.
11. Affiliate participation: as an affiliate of other companies, combine affiliate product links with data to earn commissions on related sales.
12. Value-add/loss leader: incorporate free or bonus data as an enhanced feature to win customers for another product or service.
13. Traffic generation/SEO: publish data to earn favorable positions in search engines and other directories to generate more traffic.
14. Branding: provide data free of charge on a friendly web site to build brand (i.e., self-sponsorship).
15. Data branding: provide data free of charge to build brand, but it's the data itself — not the visible manifestation of it — that is the vehicle for meme distribution.

To be certain, this isn't an exhaustive list. I'm sure there are plenty of innovative business models that are more unique, and that many real organizations will adopt hybrid approaches. However, we do hope this helps people better visualize the landscape of data delivery business models, and that it helps move the discussion forward.

Let us know what you think!

If you're interested in this topic, I also suggest that you visit the Business Of Linked Data (BOLD) Google group organized by Kingsley Idehen, read the book The Power of Pull by David Siegel, and consider attending this year's Semantic Technology Conference in San Francisco in late June.

Web 3.0 interview on data marketing

While I was at the Web 3.0 Conference last week, giving a talk on data marketing, I did a quick video interview on the subject with Scott Koegler of SemanticWeb.com.

Watch Web3.0 Conference Interview Scott Brinker - Ion Interactive in Tech & Gaming  |  View More Free Videos Online at Veoh.com

Data marketing for Web 3.0

I just finished up my presentation on data marketing at the Web 3.0 Conference, and I thought I'd share it here as well.

Web 3.0 Data Marketing

View more presentations from sjbrinker.

I'll recap what I said to accompany the above slides, a kind of running commentary like they have on DVD's. I'll put slide numbers in brackets, like [4] for slide #4, so you can follow along.

Marketing with Linked Data

I want to talk to you about [1] the emerging field of data marketing.

Web 3.0 means different things to different people [2]. Without launching into a big nomenclature debate — there's something ironic in arguing the semantics of the semantic web — I'd humbly propose the following categories based on different waves of linking. After all, linking is what the web is all about:

• Web 1.0 was about linking pages. The giants that emerged out of that wave included Google and Yahoo!, who took advantage of those linked pages to help us find great content.
• Web 2.0 was about linking people. Social networking exploded, from sharing photos on flickr and videos on YouTube, to connecting with our friends and colleagues on Facebook, LinkedIn, and Twitter.
• Web 3.0 will be about linking data. Who will be the leaders of this wave? Maybe you...

By "data," I mean the technologies of the semantic web and the linked data movement.

By "marketing" [3], I mean the broadest definition of the field. Marketing includes the 4 P's: product, pricing, placement, and promotion.

• Product includes the design of your offerings and your strategy for differentiation.
• Pricing covers not just "the price," but how your pricing evolves over time and how it changes for different segments and bundles.
• Placement is the channels by which you deliver your product to market: distribution, retail, e-commerce, partnering.
• Promotion includes advertising, branding, PR, sales promotions — online this means web sites, email, search, and social media marketing.

Linked data will impact all of these [4].

Web 1.0 brought us web marketing and search engine marketing. Web 2.0 brought us social media marketing. Web 3.0 will enable a whole new marketing paradigm, data marketing.

Data for customers

I'll touch on 4 data marketing topics in this presentation [5]. Let's begin with the big picture, thinking of data for customers.

Data helps us make decisions [6].

Data helps us impress our boss [7].

Data helps us persuade people [8].

Data is the fuel of the 21st century [9].

Yet despite its ubiquity in our lives — particularly our professional lives — finding the data we want in any given circumstance is hard work [10].

And then once we find it, we tend to stow it away in silos [11]. Spreadsheets. Documents. Databases. We end up with data that is dispersed, duplicated, out-of-date, and difficult to relate from one application to the next. To paraphrase the children's rhyme: data, data, everywhere, but not a drop to drink.

But a new generation of Web 3.0 companies is working to make data "open" [12] — linkable and shareable. Freebase, Infochimps, Factual, Calais, Flowing Data, Wolfram Alpha. These companies are making it easier for us to find and use the data we want.

But they won't have a monopoly on linked data [13]. The beauty of semantic web standards, such as RDF and OWL, are that they will enable any web site to publish structured data and link it to other sets of related data, anywhere on the web. Just like we all did with HTML. Linked data is the great wave of Web 3.0.

Now, marketers love data [14]. We may not be database architects — at least not yet — but we appreciate information: market research, web analytics, CRM records, social media monitoring. We know the value of good data.

As Sting said [15], "If you love something, set it free." (This may be the first — and last — time someone cites Sting for explaining the semantic web.)

Set data free.

The essence of Web 3.0 for marketers is to think about data for customers, not just data about customers [16]. Shift from data stalking to data talking. What data could you share with your prospects, customers, and partners to strengthen your relationships, build your brand, and win more business?

Data + search = SEO++

One of the first places data can help is with search marketing [17].

The web's a big place [18]. Carl Sagan would say, "billions and billions" of pages. Just doing a search for a specific restaurant by name in a specific city can return 80,000 hits. It can be hard for us — or our customers — to find answers.

Yahoo! was one of the first major search engines to start leveraging structured data from web sites in their search results with SearchMonkey [19]. When you do a search for a restaurant — such as "slanted door san francisco" — you might see a particularly useful summary from Yelp, right in the results. Aggregate rating, number of reviews, address, phone number, a photo of the interior.

That's a heck of a lot more useful than a random line or two of text.

Google does something similar with their Rich Snippets feature [20]. As does Bing [21].

As a marketer, the benefit of adding search engine friendly structured data to your web site is simple: more traffic [22]. Yahoo! reported that search results that embedded these useful snippets of structured data received 15% more click-throughs than the competition.

Although the search engines claim adding structured data to your web site won't directly improve your ranking [23], to prevent spammers from gaming the system with bogus data, there is a virtuous cycle here. A better listing gets you more clicks, more visitors — some percentage of whom will link back to your site, thereby improving your ranking, which in turn wins you even more clicks.

To clarify, the data bubbling up in these search results was published by Yelp [24]. Yelp is a classic example of a company that has a value proposition rooted in data. They offer extensive data about restaurants — featuring the collective intelligence of over 9 million user reviews — that makes them a data authority in the subject.

The value of that authoritative brand position was estimated at over $500 million in recent M&A rumors.

Adding structured data to your web site is pretty straightforward, using microformats and RDFa [25]. If you're not technical, just think of this as extended HTML.

Embedding this kind of data in your web site is a natural extension of search engine optimization (SEO) [26]. Whoever is doing your SEO now should be able to help with this. But since structured data takes SEO a little further, I call this SEO++ — a mix of SEO and data objects. It's reminiscent of the paradigm shift to object-oriented programming, from C to C++.

As semantic search technology advances, this will be a big deal.

You can certainly take this kind of structured data further than restaurant reviews. Last year, Best Buy launched a large-scale semantic web site with their products [27].

They used a more advanced vocabulary called GoodRelations [28] to more fully describe their products. GoodRelations can describe components of products, accessories, delivery options, payment options, warranties — just about anything you could want for defining e-commerce offerings in a structured format.

The payoff, according to Best Buy, was a 30% increase in site traffic from organic search [29].

This is why search is a great place to dip your toe with data marketing. It provides a clear business incentive — more traffic to your web site — and there's an existing budget to invest in it [30]. This year over $2 billion will be spent on SEO; within 4 years, that will be more than $5 billion.

Data branding

Now let's look beyond search and think more broadly about data marketing and what it can mean for your brand [31].

Josh Jones-Dilworth, who handles PR for many of the leading companies in the semantic web and linked data space, wrote an excellent article on Mashable, Marketing in 2010: It's All About the Data. He starts with this brilliant insight [32]:

Data shapes conversations and markets.

That is the essence of data branding — positioning your company through the data you create and publish — and, in a linked data world, how you relate that data to others.

In data branding, you want to ask yourself three questions:

1. What data, if widely disseminated, would grow my market? [33]

For example, consider Google Insights for Search [34]. Google's "database of intent" is arguably their most valuable asset. But rather than lock it in a vault, they let anyone mine this data for trends. With their Keyword Tool you can even drill down into specific number of impressions for all related keywords. This is smart, as it helps get marketers more engaged in search. Ultimately, this data encourages more — and more successful — search advertising.

2. What data would position you as a leading authority in your field? [35]

The New York Times is struggling, like all newspapers, to find their place in the digital world. But Evan Sandhaus, the paper's resident Semantic Technologist, is forging an exciting strategy [36]: to have the "paper of record" become the authoritative data source of record — at least for the paper's areas of expertise with news, public figures, politics. They have over 150 years of original content to draw upon. Check out data.nytimes.com and developer.nytimes.com to glimpse this future.

But they'll have competition. Thomson Reuters also has claim to being a data authority, particularly with companies, law, markets, and finance [37]. And they've got a strong competitive advantage with their Open Calais technology to help web developers leverage that data quickly and easily.

CNET is a data authority with product reviews. Yelp is a data authority with restaurant reviews. Google is a data authority with search behaviors. What will your company be a data authority for?

3. What data vocabularies (ontologies) will define your market? [38]

To unlock the real power of linked data, you must do more than publish structured data [39]. You need to use vocabularies — or, more technically, ontologies — that other people will recognize and use to publish or relate their data too. The interlinked web of data is the real vision.

For example, there's a well-established vocabulary for social network data — friend of a friend (FOAF) — and a very popular vocabulary for publishing called the Dublin Core. GoodRelations, which we saw earlier, is another.

But for most areas of expertise and most domains of knowledge — probably many in your specific market — there aren't well-defined vocabularies. Yet. You have an opportunity to be the first mover, to shape the nomenclature and structure of information in your space.

Reflect for a moment on the brand potential here, defining the structure of shared data as a way of shaping the use of that data in your market — in ways that are particularly synergistic and relevant to your position.

Engaging in such data branding initiatives will involve a new ecosystem of players [40] — developers and popular data applications. Search engines are one example of a data application, but there will be hundreds, thousands as the semantic web blossoms.

Smart marketers concerned with data branding will cultivate these relationships — and start soon.

Data business models

Finally, I'd like to mention a bit about business models for data [41].

Implementing linked data doesn't happen automagically. Companies need to think about how they can get return on such investments [42]. We briefly discussed this with SEO++, but there's a wide range of possibilities.

A few weeks ago, I wrote a post on 7 business models for linked data, along with a follow-up piece on the 8th linked data business model. These triggered vibrant conversations in the semantic web community around this topic, with a number of people suggesting many additional models.

Once you realize that data has tremendous value -- and that the interoperability of linked data will only increase its usefulness and accessibility -- you can start thinking creatively of many ways to capture that value.

That's going to be one of the great adventures in marketing for this next decade.

Thank you, good luck, and let me know what you think [43].

Linked data lab at MIT was excellent

Earlier this month, MIT ran a special Linked Data Product Development Lab during their Independent Activities Period (IAP).

The lab brought together an eclectic mix of students and alumni, engineers and entrepreneurs, for a week of lectures, hands-on workshops, and collaborative team projects — all focused on the present-day capabilities of linked data. Sir Tim Berners-Lee kicked off the event with an inspiring talk about the vision of linked data.

I had the pleasure of presenting on the "business value of linked data" panel on the second morning, along with K. Krasnow Waterman (one of the organizers behind the event) and Evan Sandhaus of The New York Times.

By the way, if you haven't yet checked out data.nytimes.com, I'd highly encourage you to do so. Evan gave an inspiring talk about what they're up to, and it's well worth examining it from the perspective of any publisher. And in this day and age of search and social media marketing, just about every company is a publisher to some degree or another.

My topic on the panel, as frequent readers here might expect, was marketing with linked data. Here's the brief slide deck I put together for it:

Marketing with Linked Data (MIT)

View more presentations from sjbrinker.

It was an excellent event. My thanks to the organizers for inviting me to participate. (It also afforded me the opportunity to meet Kingsley Idehen, one of the leading advocates for the business opportunities enabled by the semantic web — and a frequenter commenter on this blog.)

I'll also be speaking on this topic later this week at the Web 3.0 Conference in Santa Clara on the Web of Data: Semantic Web in Marketing panel with Krista Thomas and moderator Carla Thompson.

On Her Majesty's linked data service

Earlier today, Sir Tim Berners-Lee and Nigel Shadbolt posted a great article on The Guardian's Data Blog, Our manifesto for government data.

They announced the public availability of the U.K.'s beta data.gov.uk site, a cornucopia of over 2,500 data sets of government data — everything from agriculture statistics to workplace employment by industry — open to anyone to use. It's a great example of the 8th business model for data.

And most of it adheres to proper linked data standards.

Marketers should pay attention to this initiative. When you read the introduction to their article, it's not hard to imagine this being about business instead of government, customers instead of constituents:

Data underpins our economy and our society — data about how much is being spent and where, data about how schools, hospitals and police are performing, data about where things are and data about the weather.

Yet until recently, not many non-technical people concerned themselves with data and how it could be used better.

That's changing, and changing quickly. There are growing expectations of greater accountability and transparency of public bodies. New technologies allow data to be managed and re-used quickly and cost-effectively. A greater number of people have the innovation and the skills to use data. And organizations from government to the private sector, from voluntary groups to the media are hungry for data.

Everyone is hungry for data. And as the linked data movement advances, making data as easily shareable and interlinkable as web pages are today, businesses are going to have a tremendous opportunity to use data as a marketing channel.

Initiatives such as data.uk.gov — and its U.S. sister site across the pond, www.data.gov — will help to build critical mass for such interlinked data and demonstrate how useful it can be.

Want to be inspired? Take a look at some of the innovative applications that are leveraging such open data in the U.K., such as Planning Alerts that notifies you when construction projects are being planned in your neighborhood. Useful, eh? (Would have altered the opening plot line in The Hitchhiker's Guide to the Galaxy, that's for sure.)

What are the potential data-driven applications that your customers, partners, employees, and fans could build to promote your company's mission and add value to your ecosystem?

The 8th linked data business model

In response to my post on linked data business models, Leigh Dodds at Talis wrote a terrific piece with his thoughts on the business of linked data. Leigh presents a number of great ideas that I think really carry the conversation forward.

One of his points is that I overlooked an important model, what he calls the "sponsorship model." Under this model, a government entity or a non-profit organization has a funded mandate to deliver certain data to the public or their targeted constituency. I'd humbly suggest calling it the subsidized model though, to avoid confusion, because sponsorship is often associated with advertising and branding — very different business models.

Here's an updated diagram reflecting this additional model:

I put the subsidized model at the top for two reasons. First, it could be argued that it is the most direct revenue source — you're paid to produce the data before you even put it online. Second, I wanted to put a lot of distance between it and the branded model to emphasize the subtle yet significant distinction between them.

Now, you could debate that this isn't really a business model per se because it's clearly non-commercial. However, in the broader sense of the term, it does represent a major "revenue" source that can employ people to produce and maintain such data.

It also happens to be the model behind many of the largest linked data repositories on the web today, such as those from the BBC or the US Census. To ignore it would be to leave unexplained the organizational motivation behind a huge portion of the linked data graph.

I can also see how the subsidized model might apply to commercial organizations, who either have a regulatory requirement for delivering certain information — i.e., a cost of doing business — or a philanthropic arm that engages in good deeds for reasons other than corporate posturing.

But I want to emphasize the subsidized model is not the same as the branded model.

The subsidized model makes the investment in linked data because the delivery of that data is a core part of its organizational mission. It doesn't need to generate any subsequent financial return from the publication of that data.

The branded model, however, has an ulterior motive for producing linked data: shaping impressions in the minds of the audience who consumes that data — with the intention that such influencing of opinion will lead to future sales and business relationships. It's the most indirect business model, but indirect shouldn't be interpreted as inefficacious. The right branding can have tremendous value.

When The New York Times invests in their Linked Open Data initiative, it isn't altruism. It's a part of their strategy to retain their brand as the leading authority of news and editorial in the world. It's a tremendous benefit to the public. But it's also a powerful move for their business.

I personally think that data branding will become one of the most significant forces behind the widespread adoption of linked data.

7 business models for linked data

Now that major companies are implementing linked data, and more marketing thought leaders are championing data as an outward-facing competitive advantage, the question I'm hearing more frequently is:

How do you turn data into revenue?

Creating, publishing, and maintaining data takes work. What are the economic incentives for companies to put in the effort?

Here's my take on 7 business models for data web initiatives:

I've organized these by how revenue is generated, from direct money-for-data to indirect branding programs.

Within each of these revenue models, there's also a secondary dimension of how the data is delivered, whether in raw form for others to leverage in their own applications or embedded into a pre-packaged application provided directly to end-users.

1. Subscription model. Some data will be valuable enough that you can charge people a subscription to access it. This model has been around for a while, but it will gain new life as linked data standards make it easier for people to consume and mash-up data in novel applications.

2. Advertising model. Advertising: the second oldest profession. Data-driven applications will have plenty of opportunity for contextual ads and sponsorships. One interesting twist will be advertisers who pay to include information in raw data feeds, data-layer ads if you will.

3. Authority model. If anyone can publish data on the web, how will you know what data is good? That problem will be an opportunity for third-party "authorities" to validate data — or do official reviews and certifications that are published as data — and charge for participation. Compliance services are related to this.

4. Affiliate model. Affiliate marketing programs generate over $6 billion/year in commissions and are a major source of transactions and leads for merchants such as Amazon.com. Embedding affiliate links in data, so that they are activated when surfaced into end-user applications, are a natural extension of this existing model.

5. Value-Add model. Useful data can be bundled with other services to make the overall solution more valuable. For example, think of the benchmarking data now included with Google Analytics. Access to data can also be offered earlier in the sales funnel, as a lead generation incentive.

6. Traffic model. As with Google Rich Snippets, data can be used to boost the visibility and ranking of sites in major and vertical search engines. This is data-enhanced search engine optimization (SEO++) to increase traffic. Nickname: the "data for nothing and links for free" model (apologies to Mark Knopfler).

7. Branding model. As Josh Jones-Dilworth said, "Data shapes conversations and markets." Data branding can use data — and the vocabularies that define and structure data — to position and promote a company's worldview and differentiation strategy.

Of course, there will be hybrid models that combine several of these approaches.

Particularly in the early days, most organizations will benefit from experimenting with linked data for traffic, branding, and a little value add. Their own value will be learning as much as anything. As the data web matures, and they become more experienced, they may embrace more direct revenue models.

But don't underestimate the importance of data branding. When it comes to establishing industry standard vocabularies and ontologies, there is a definite first-mover advantage.

For the entrepreneurs in this space, however, everything is fair game.

Can you think of other models? Are you willing to share?