Tag Archives: linked_data

Thinking Beyond Semantic Search

Publishers are quickly adopting semantic markup, yet often get less value from it than they could. They don’t focus on how audiences can directly access and use their semantically-described content. Instead, publishers rely on search engines to boost their engagement with audiences. But there are limits to what content, and how much content, search engines will present to audiences.  Publishers should leverage their investment in semantic markup.  Semantically-described content can increase the precision and flexibility of content delivery.  To realize the full benefits of semantic markup, publishers need APIs and apps that can deliver more content, directly to their audiences, to help individuals explore content that’s intriguing and relevant.

The Value of Schema.org Markup

Semantic search is a buzzy topic now. With the encouragement of Google, SEO consultants promote marking up content with Schema.org so that Google can learn what the content is. A number of SEO consultants suggest that brands can use their markup to land a coveted spot in Google’s knowledge graph, and show up in Google’s answer box. There are good reasons to adopt Schema.org markup.  It may or may not boost traffic to your web pages.  It may or may not boost your brand’s visibility in search.  But it will help audiences get the information they need more quickly.  And every brand needs to be viewed as helpful, and not as creating barriers to access to information customers need.

But much of the story about semantic search is incomplete and potentially misleading. Only a few lucky organizations will manage to get their content in Google’s answer box. Google has multiple reasons to crawl content that is marked up semantically. Besides offering search results, Google is building its own knowledge database it will use for its own applications, now and in the future.  By adding semantic annotation to their content that Google robots then crawl, publishers provide Google a crowd-sourced body of structured knowledge that Google can use for purposes that may be unrelated to search results. Semantic search’s role as a fact-collection mechanism is analogous to Google’s natural-language machine learning it developed through their massive book-scanning program several years ago.

Publishers rely on Google for search visibility, and effectively grant Google permission to crawl their content unless they indicate no-robots. Publishers provide Google with raw material in a format that’s useful to Google, but they can fail to ask how that format is useful to them as publishers. As with most SEO, publishers are being told to focus on what Google wants and needs. Unless one pays close attention to what is happening with developments with Schema.org, one will get the impression that the only reason to create this metadata is to please Google.  Google is so dominant that it seems as if it is entirely Google’s show.  Phil Archer, data activity lead at the W3C, has said: “Google is the killer app of the semantic web.”  Marking up content in Schema.org clearly benefits Google, but it often doesn’t help publishers nearly as much as it could.

Schema.org provides schemas “to markup HTML pages in ways recognized by major search providers, and that can also be used for structured data interoperability (e.g. in JSON).” According to its FAQs, its purpose is “to improve the web by creating a structured data markup schema supported by major search engines.”  Schema.org is first and foremost about serving the needs of search engines, though it does provide the possibility for data interoperability as well.  I want to focus on the issue of data interoperability, especially as it relates to audiences, because it is a widely neglected dimension.

Accessing Linked Data

Semantic search markup (Schema.org), linked data repositories such as GeoNames, and open content such as Wikipedia-sourced datasets of facts (DBpedia) all use a common, non-proprietary data model (RDF).  It is natural to view search engine markup as another step in the growth in the openness of the web, since more content is now described more explicitly.  Openness is a wonderful attribute: if data is not open, that implies it is being wasted, or worse, hoarded.  The goal is to publish your content as machine-intelligible data that is publicly accessible.  Because it’s on the web in a standardized format, anyone can access it, so it seems open.  But the formal guidelines that define the technological openness of open data are based more on standards-compliance by publishers than approachability by content consumers.  They are written from an engineering perspective. There is no notion of an audience in the concept of linked data. The concept presumes that the people who need the data have the technical means to access and use it.  But the reality is that much content that is considered linked data is effectively closed to the majority of people who need it, the audience for whom it was created. To access the data, they must rely on either the publisher, or a third party like Google, to give them a slice of what they seek.  So far, it’s almost entirely Google or Bing who have been making the data audience-accessible.  And they do so selectively.

Let’s look at a description of the Empire State Building in New York.  This linked data might be interesting to combine with other linked data concerning other tall buildings.  Perhaps school children will want to explore different aspects of tall buildings.  But clearly, school children won’t be able to do much with the markup themselves.

json-ld for empire state building
Schema.org description of Empire State Building in JSON-LD, via JSON-LD.org

If one searches Google for information on tall buildings, they will provide an answer that draws on semantic markup.  But while this is a nice feature, it falls short of providing the full range of information that might be of interest, and it does not allow users to explore the information the way they might wish.  One can click on items in the carousel for more details, but the interaction is based on drilling-down to more specific information, or requiring a new search query, rather than providing a contextually dynamic aggregation of information.  For example, if the student wants to find out which architect is responsible for the most tall buildings in the world, Google doesn’t offer a good way to get to that information iteratively.  If the student asks Google “which country has the most tall buildings?” she is simply given a list of search results, which includes a Wikipedia page where the information is readily available.

Relying on Google to interpret the underlying semantic markup means that the user is limited to the specific presentation that Google chooses to offer at a given time.  This dependency on Google’s choices seems far from the ideals promised by the vision of linked open data.

screenshot of google search results
Screenshot of Google search for tallest buildings

Google and Bing have invested considerable effort in making semantic search a reality: communication campaigns to encourage implementation of semantic markup, and technical resources to consume this markup to offer their customers a better search experience.  They crawl and index every word on every page, and perform an impressive range of transformations of that information to understand and use it.  But the process that the search engines use to extract meaning from content is not something that ordinary content consumers can do, and in many ways is more complicated that it needs to be.  One gets a sense of how developer-driven that semantic search markup is by looking at the fluctuating formats used by Schema.org.  There are three different markup languages (microdata, RDFa, and JSON-LD) with significantly different ways of characterizing the data.  Google’s robots are sophisticated enough to be able to interpret any of the types of markup.  But people not working for a search engine firm need to rely on something like Apache Any23, a Java library, to extract semantic content marked up in different formats.

Screenshot of Apache Any23
Screenshot of Apache Any23

Linked Data is Content that needs a User Interface

How does an ordinary person link to content described with Schema.org markup? Tim Berners-Lee famously described linked data as “browseable data.” How can we browse all this great stuff that’s out there, that’s been finally annotated so that we get the exact bits we want?  Audiences should have many avenues to retrieving content so that they can use it in the context where they need it. They need a user interface to the linked data.  We need to build this missing user interface.  For this to happen, there need to be helpful APIs, and easy-to-use consumer applications.


The goal of APIs is to find other people to promote the use of your content.  Ideally, they will use your content in ways you might not even have considered, and therefore be adding value to the content by expanding its range of potential use.

APIs play a growing role in the distribution of content.  But they often aren’t truly open in the sense they offer a wide range of options to data consumers.  APIs thus far seem to play a limited role in enabling the use of content annotated with  schema.org markup.

Getting data from an API can be a chore, even for quantitatively sophisticated people who are used to thinking about variables.  AJ Hirst, an open data advocate who teaches at the Open University, says: “For me, a stereotypical data user might be someone who typically wants to be able to quickly and easily get just the data they want from the API into a data representation that is native to the environment they are working in, and that they are familiar with working with.”

API frictions are numerous: people need to figure out what data is available, what it means, and how they can use it.  Hirst advocates more user-friendly discovery resources. “If there isn’t a discovery tool they can use from the tool they’re using or environment they’re working in, then finding data from service X turns into another chore that takes them out of their analysis context.”  His view: “APIs for users – not programmers. That’s what I want from an API.”

The other challenge is that query-possibilities for semantic content go beyond the basic functions commonly used in APIs.

Jeremiah Lee, an API designer at Fitbit, has thought about how to encourage API providers and users to think more broadly about what content is available, and how it might be used.  He notes: “REST is a great starting point for basic CRUD operations, but it doesn’t adequately explain how to work with collections, relational data, operations that don’t map to basic HTTP verbs, or data extracted from basic resources (such as stats). Hypermedia proponents argue that linked resources best enable discoverability, just as one might browse several linked articles on Wikipedia to learn about a subject. While doing so may help explain resource relationships after enough clicking, it’s not the best way to communicate concepts.”

For Linked Data, a new API standard is under development called hydra that aims to address some of the technical limitations of standard APIs that Lee mentions.  But the human challenges remain, and the richer the functionality offered by an API, the more important it is that the API be self-describing.

Fitbit’s API, while not a semantic web application, does illustrate some novel properties that could be used for semantic web APIs, including a more visually rich presentation with more detailed descriptions and suggestions available via tooltips.  These aid the API user, who may have various goals and levels of knowledge relating to the content.

Screenshot of Fitbit API
Screenshot of Fitbit API

Consumer apps

The tools available to ordinary content users to add semantic descriptions have become more plentiful and easier to use.  Ordinary web writers can use Google’s data highlighter to indicate what content elements are about.  Several popular CMS platforms have plug-ins that allow content creators to fill-in forms to describe the content on the page.  These kinds of tools hide the markup from the user, and have been helpful in spurring adoption of semantic markup.

While the creation of semantic content has become popularized, there has not been equivalent progress in developing user-friendly tools that allow audiences to retrieve and explore semantic content. Paige Morgan, an historian who is developing a semantic data set of economic information, notes: “Unfortunately, structuring your data and getting it into a triplestore is only part of the challenge. To query it (which is really the point of working with RDF, and which you need to do in order to make sure that your data structure works), you need to know SPARQL — but SPARQL will return a page of URIs (uniform resource identifiers — which are often in the form of HTML addresses). To get data out of your triplestore in a more user-friendly and readable format, you need to write a script in something like Python or Ruby.  And that still isn’t any sort of graphical user interface for users who aren’t especially tech-savvy.”

We lack consumer-oriented applications that allow people to access and recombine linked data.  There is no user interface for individuals to link themselves to the linked data.  The missing UI reflects a legacy of seeing linked data as being entirely about making content machine-readable.  According to legacy thinking, if people needed to directly interact with the data, they could download it to a spreadsheet.  The term “data” appeals to developers who are comfortable thinking about content structured as databases, but it doesn’t suggest application to things that are mentioned in narrative content.  Most content described by Schema.org is textual content, not numbers, which is what most non-IT people consider as data.  And text exists to be read by people.  But the jargon we are stuck with to discuss semantic content means we emphasize the machine/data side of the equation, rather than the audience/content side of it.

Linked data in reality are linked facts, facts that people can find useful in a variety of situations.  Google Now is ready to use your linked data and tell your customers when they should leave the house.  Google has identified the contextual value to consumers of linked data.  Perhaps your brand should also use linked data in conversations with your customers.  To do this, you need to create consumer facing apps that leverage linked data to empower your customers.

Wolfram Alpha is a well-known consumer app to explore data on general topics that has been collected from various sources.  They characterize their mission, quite appealingly, as “democratizing data.” The app is user friendly, offering query suggestions to help users understand what kinds of information can be retrieved, and refine their queries.  Their solution is not open, however.  According to Wolfram’s Luc Barthelet, “Wolfram|Alpha is not searching the Semantic Web per se. It takes search queries and maps them to an exact semantic understanding of the query, which is then processed against its curated knowledge base.” While more versatile than Google search in the range and detail of information retrieved, it is still a gatekeeper, where individuals are dependent on the information collection decisions of a single company.  Wolfram lacks an open-standards, linked-data foundation, though it does suggest how a consumer-focused application might use of semantic data.

The task of developing an app is more manageable when the app is focused on a specific domain.  The New York Times and other news organizations have been working with linked data for several years to enhance the flexibility of the information they offer.  In 2010 the Times created an “alumni in the news” app that let people track mentions of people according to what university they attended, where the educational information was sourced from DBpedia.

New York Times Linked Data app for alumni in the news.  It relied in part on linked data from Freebase, a Google product that Google is retiring.
New York Times Linked Data app for alumni in the news. It relied in part on linked data from Freebase, a Google product that Google is retiring that will be superseded by Wikidata.

A recent example of a consumer app that is using linked data is a sports-oriented social network called YourSports.  The core metadata of the app is built in JSON-LD, and the app creator is even proposing extensions to Schema.org to describe sports relationships.  This kind of app hides the details of the metadata from the users, and enables them to explore data dimensions as suits their interests.  I don’t have direct experience of this app, but it appears to aggregate and integrate sports-related factual content from different sources.  In doing so, it enhances value for users and content producers.

Screenshot of Yoursports
Screenshot of Yoursports

Opening up content, realizing content value

If your organization is investing in semantic search markup, you should be asking: How else can we leverage this?  Are you using the markup to expose your content in your APIs so other publishers can utilize the content?  Are you considering how to empower potential readers of your content to explore what you have available?  Consumer brands have an opportunity to offer linked data to potential customers through an app that could result in lead generation.  For example, a travel brand could use linked data relating to destinations to encourage trip planning, and eventual booking of transportation, accommodation, and events.  Or an event producer might seed some of its own content to global partners by creating an API experience that leverages the semantic descriptions.

The pace of adoption for aspects of semantic web has been remarkable. But it is easy to overlook what is missing.  A position paper for Schema.org says “Schema.org is designed for extremely mainstream, mass­-market adoption.”  But to consider the mass-market only as publishers acting in their role as customers of search engines is too limiting.  The real mainstream, mass-market is the audience that is consuming the content. These people may not even have used a search engine to reach your content.

Audiences need ways to explore semantically-defined factual content as they please.  It is nice that one can find bits of content through Google, but it would be better if one didn’t have to rely solely only on Google to explore such content.  Yes, Google search is often effective, but search results aren’t really browseable.  Search isn’t designed for browsing: it’s designed to locate specific, known items of information.  Semantic search provides a solution to the issue of too much information: it narrows the pool of results.  Google in particular is geared to offering instant answers, rather than sustaining an engaging content experience.

Linked data is larger than semantic search.  Linked data is designed to discover connections, to see themes worth exploring. Linked data allows brands to juxtapose different kinds of information together that might share a common location or timing, for example. Individuals first need to understand what questions they might be interested in before they are ready for answers to those questions. They start with goals that are hard to define in a search query.  Linked data provides a mechanism to help people explore content that relates to these goals.

While Google knows a lot about many things relating to a person, and people in general, it doesn’t specialize in any one area.  The best brands understand how their customers think about their products and services, and have unique insights into the motivations of people with respect to a specific facet of their lives.  Brands that enable people to interact with linked data, and allow them to make connections and explore possibilities, can provide prospective customers something they can’t get from Google.

— Michael Andrews

Data Types and Data Action

We often think about content from a narrative perspective, and tend to overlook the important roles that data play for content consumers. Specific names or numeric figures often carry the greatest meaning for readers. Such specific factual information is data. It should be described in a way that lets people use the data effectively.

Not all data is equally useful; what matters is our ability to act on data. Some data allows you to do many different things with it, while other data is more limited. The stuff one can do with types of data is sometimes described as the computational affordances of data, or as data affordances.

The concept of affordances comes from the field of ecological psychology, and was popularized by the user experience guru Donald Norman. An affordance is a signal encoded in the appearance of an object that suggests how it can be used and what actions are possible. A door handle may suggest that is should be pushed, pulled or turned, for example. Similarly, with content we need to be able to recognize the characteristics of an item of data, to understand how it can be used.

Data types and affordances

The postal code is an important data type in many countries. Why is it so important? What can you do with a postal code? How people use postal codes provides a good illustration of data affordances in action.

Data affordances can be considered in terms of their purpose-depth, and purpose-scope, according to Luciano Floridi of the Oxford Internet Institute. Purpose-depth relates to how well the data serves its intended purpose. Purpose-scope relates to how readily the data can be repurposed for other uses. Both characteristics influence how we perceive the value of the data.

A postal code is a simplified representation of a location composed of households. Floridi notes that postal codes were developed to optimize the delivery of mail, but subsequently were adopted by other actors for other purposes, such as to allocate public spending, or calculate insurance premiums.

He states: “Ideally, high quality information… is optimally fit for the specific purpose/s for which it is elaborated (purpose–depth) and is also easily re-usable for new purpose/s (purpose–scope). However, as in the case of a tool, sometimes the better [that] some information fits its original purpose, the less likely it seems to be repurposable, and vice versa.” In short, we don’t want data to be too vague or imprecise, and we also want the data to have many ways it can be used.

Imagine if all data were simple text. That would limit what one could do with that data. Defining data types is one way that data can work harder for specific purposes, and become more desirable in various contexts.

A data type determines how an item is formatted and what values are allowed. The concept will be familiar to anyone who works with Excel spreadsheets, and notices how Excel needs to know what kind of value a cell contains.

In computer programming, data types tell a program how to assess and act on variables. Many data types relate to issues of little concern to content strategy, such as various numeric types that impact the speed and precision of calculations. However, there is a rich range of data types that provide useful information and functionality to audiences. People make decisions based on data, and how that data is characterized influences how easily they can make decisions and complete tasks.

Here are some generic data types that can be useful for audiences, each of which has different affordances:

  • Boolean (true or false)
  • Code (showing computer code to a reader, such as within the HTML code tags)
  • Currency (monetary cost or value denominated in a currency)
  • Date
  • Email address
  • Geographic coordinate
  • Number
  • Quantity (a number plus a unit type, such as 25 kilometers)
  • Record (an identifier composed of compound properties, such as 13th president of a country)
  • Telephone number
  • Temperature (similar to quantity)
  • Text – controlled vocabulary (such as the limited ranged of values available in a drop down menu)
  • Text – variable length free text
  • Time duration (number of minutes, not necessarily tied to a specific date)
  • URI or URN (authoritative resource identifier belonging to a specific namespace, such as an ISBN number)
  • URL (webpage)

Not all content management systems will provide structure for these data types out of the box, but most should be supportable with some customization. I have adapted the above list from the listing of data types supported by Semantic MediaWiki, a widely used open source wiki, and the data types common in SQL databases.

By having distinct data types with unique affordances, publishers and audiences can do more with content. The ways people can act on data are many:

  • Filter by relevant criteria: Content might use geolocation data to present a telephone number in the reader’s region
  • Start an action: Readers can click-to-call telephone numbers that conform to an international standard format
  • Sort and rank: Various data types can be used to sort items or rank them
  • Average: When using controlled vocabularies in text, the number of items with a given value can be counted or averaged
  • Sum together: Content containing quantities can be summed: for example, recipe apps allow users to add together common ingredients from different dishes to determine the total amount of an ingredient required for a meal
  • Convert: A temperature can be converted into different units depending on the reader’s preference

The choice of data type should be based on what your organization wants to do with the content, and what your audience might want to do with it. It is possible to reduce most character-based data to either a string or a number, but such simplification will reduce the range of actions possible.

Data verses Metadata

The boundary between data and metadata is often blurry. Data associated with both metadata and the content body-field have important affordances. Metadata and data together describe things mentioned within or about the content. We can act on data in the content itself, as well as act on data within metadata framing the content.

Historically, structural metadata outside the content played a prominent role indicating the organization of the content that implied what the content was about. Increasingly, meaning is being embedded with semantic markup within the content itself, and structural metadata surrounding the content may be limited. A news article may no longer indicate a location in its dateline, but may have the story location marked up within the article that is referenced by content elsewhere.

Administrative metadata, often generated by a computer and traditionally intended for internal use, may have value to audiences. Consider the humble date stamp, indicating when an article was published. By seeing a list of most recent articles, audiences can tell what’s new and what that content is about, without necessarily viewing the content itself.

Van Hooland and Verborgh ask in their recent book on linked data: “[W]here to draw the line between data and metadata. The short answer is you cannot. It is the context of the use which decides whether to considered data as metadata or not. You should also not forget that one of the basic characteristics of metadata: they are ever extensible …you can always add another layer of metadata to describe your metadata.” They point out that annotations, such as reviews of products, become content that can itself be summarized and described by other data. The number of stars a reviewer gives a product, is aggregated with the feedback of other reviewers, to produce an average rating, which is metadata about both the product and the individual reviews on which it is based.

Arguably, the rise of social interaction with nearly all facets of content merits an expansion of metadata concepts. By convention, information standards divide metadata into three categories: structural metadata, administrative metadata and descriptive metadata. But one academic body suggests a fourth type of metadata they call “use metadata,” defined as “metadata collected from or about the users themselves (e.g., user annotations, number of people accessing a particular resource).” Such metadata would blend elements of administrative and descriptive metadata relating to readers, rather than authors.

Open Data and Open Metadata

Open data is another data dimension of interest to content strategy. Often people assume open data refers to numeric data, but it is more helpful to think of open data as the re-use of facts.

Open data offers a rich range of affordances, including the ability to discover and use other people’s data, and the ability to make your data discoverable and available to others. Because of this emphasis on the exchange of data, how this data is described and specified is important. In particular, transparency and use rights issues with open data are a key concern, as administrative metadata in open data is a weakness.

Unfortunately, discussion of open data often focuses on the technical accessibility of data to systems, rather than the utility of data to end-users. There is an emphasis on data formats, but not on vocabularies to describe the data. Open data promotes the use of open formats that are non-proprietary. While important, this focus misses the criticality of having shared understandings of what the data represents.

To the content strategist, the absence of guidelines for metadata standards is a shortcoming in the open data agenda. This problem was recognized in a recent editorial in the Semantic Web Journal entitled “Five Stars of Linked Data Vocabulary Use.” Its authors note: “When working with data providers and software engineers, we often observe that they prefer to have control over their local vocabulary instead of importing a wide variety of (often under-specified, not regularly maintained) external vocabularies.” In other words, because there is not a commonly agreed and used metadata standard, people rely on proprietary ones instead, even when they publish their data openly, which has the effect of limiting the value of that data. They propose a series of criteria to encourage the publication of metadata about vocabulary used to describe data, and the provision of linkages between different vocabularies used.

Classifying Openness

Whether data is truly open depends on how freely available the data is, and whether the metadata vocabulary (markup) used to describe it is transparent. In contrast to the Open Data Five Star frameworks, I view how proprietary the data is as a decisive consideration. Data can be either open or proprietary, and the metadata used to describe the data can be based either on an open or proprietary standard. Not all data that is described as “Open” is in fact non-proprietary.

What is proprietary? For data and metadata, the criteria for what is non-proprietary can be ambiguous, unlike with creative content, where the creative commons framework governs rights for use and modifications. Modification of data and its metadata is of less concern, since such modifications can destroy the re-use value of the content. Practicality of data use and metadata visibility are the central concerns. To untangle various issues, I will present a tentative framework, recognizing that some distinctions are difficult to make. How proprietary data and metadata is often reflects how much control the body responsible for this information exerts. Generally, data and metadata standards that are collectively managed are more open than those managed by a single firm.


We can grade data into three degrees, based on how much control is applied to its use:

  1. Freely available open data
  2. Published but copyrighted data
  3. Selectively disclosed data

Three criteria are relevant:

  1. Is all the data published?
  2. Does a user need to request specific data?
  3. Are there limits on how the data can be used?

If factual data is embedded within other content (for example, using RDFa markup within articles), it is possible that only the data is freely available to re-use, while the contextual content is not freely available to re-use. Factual data cannot be copyrighted in the United States, but may under certain conditions be subject to protection in the EU when a significant investment was made collecting these facts.

Rights management and rights clearance for open data are areas of ongoing (if inconclusive) deliberation among commercial and fee-funded organizations. The BBC is an organization that contributes open data for wider community use, but that generally retains the copyright on their content. More and more organizations are making their data discoverable by adopting open metadata standards, but the extent to which they sanction the re-use of that data for purposes different from it’s original intention is not always clear. In many cases, everyday practices concerning data re-use are evolving ahead of official policies defining what is permitted and not permitted.


Metadata is either open or proprietary. Open metadata is when the structure and vocabulary that describes the data is fully published, and is available for anyone to use for their own purposes. The metadata is intended to be a standard that can be used by anyone. Ideally, they have the ability to link their own data using this metadata vocabulary to data sets elsewhere. This ability to link one’s own data distinguishes it from proprietary metadata standards.

Proprietary metadata is one where the schema is not published or is only partially published, or where the metadata restricts a person’s ability to define their own data using the vocabulary.


Freely Available Open Data

  • With Open Metadata. Open data published using a publicly available, non-proprietary markup. There are many standards organizations that are creating open metadata vocabularies. Examples include public content marked up in Schema.org, and NewsML. These are publicly available standards without restrictions on use. Some standards bodies have closed participation: Google, Yahoo, and Bing decide what vocabulary to include in Schema, for example.
  • With Proprietary Metadata. It may seem odd to publish your data openly but use proprietary markup. However, organizations may choose to use a proprietary markup if they feel a good public one is not available. Non-profit organizations might use OpenCalais, a markup service available for free, which is maintained by Reuters. Much of this markup is based on open standards, but it also uses identifiers that are specific to Reuters.

Published But Copyrighted Data

  • With Open Metadata. This situation is common with organizations that make their content available through a public API. They publish the vocabularies used to describe the data and may use common standards, but they maintain the rights to the content. Anyone wishing to use the content must agree to the terms of use for the content. An example would be NPR’s API.
  • With Proprietary Metadata. Many organizations publish content using proprietary markup to describe their data. This situation encourages web-scraping by others to unlock the data. Sometimes publishers may make their content available through an API, but they retain control over the metadata itself. Amazon’s ASIN product metadata would be an example: other parties must rely on Amazon to supply this number.

Selectively Disclosed Proprietary Data

  • With Open Metadata. Just because a firm uses a data vocabulary that’s been published and is available for others to use, it doesn’t mean that such firms are willing to share their own data. Many firms use metadata standards because it is easier and cheaper to do so, compared with developing their own. In the case of Facebook, they have published their Open Graph schema to encourage others to use it so that content can be read by Facebook applications. But Facebook retains control over the actual data generated by the markup.
  • With Proprietary Metadata. Applies to any situation where firms have limited or no incentive to share data. Customer data is often in this category.

Taking Action on Data

Try to do more with the data in your content. Think about how to enable audiences to take actions on the data, or how to have your systems take actions to spare your audiences unnecessary effort. Data needs to be designed, just like other elements of content. Making this investment will allow your organization to reuse the data in more contexts.

— Michael Andrews

Content Strategy Innovation: Emerging Practices

What new practices will forward-looking publishers start to implement in the next few years? Digital content is in a constant state of change. Are current practices up to the task?

Various professions are actively developing new computer-based practices to address high volume content. Journalists are under pressure to produce greater quantities of content with fewer resources, and to make this content even more relevant. Organizations focused on vast quantities of historical content, such as museums and scholars, have been developing new approaches to extract value from all this material. These practices may not be ones that content strategists are familiar with, but should be.

Ten years ago, online content was largely about web pages. Today it includes mobile apps, tablets, even self published ebooks that live in the cloud, and new channels are around the corner. Even though we now accept that the channels for content are always changing, we still consider content as primarily the responsibility of an individual author. We should expand our thinking to include ways to use computer-augmented authoring and analysis.

It may seem hasty to talk about new practices, when many organizations struggle to implement proven good practices. As powerful as current content strategy practices are, they do not address many important issues organizations face with their content. It’s essential to develop new practices, not just advocate well-established ones. It is complacent to dismiss change by believing that the future cannot be predicted, so we can worry about it when it arrives.

We shouldn’t be defined by current tools and short-term thinking. As Jonathon Colman recently wrote in CCO magazine: “What I fear about our future, however, is that we get so caught up with the technologies, tools and tactics of our trade that we reassign our thinking from the long term to the short. We start thinking and strategizing in ever shorter cycles: months instead of years, campaigns instead of life cycles, individual infographics instead of brands they represent.”

Fortunately, content strategy can draw upon the deep experience of other disciplines concerned with content. To quote William Gibson: “The future is already here — it’s just not very evenly distributed.” I want to highlight some promising approaches being developed by colleagues in other fields.

The pressures for innovation

The pressures for innovation in content strategy come from audiences, and from within organizations. Audience expectations show no sign of diminishing — consumers everywhere are becoming more demanding. They are fickle and individualistic, and don’t want canned servings of content. They desire diversity in content at the same time they complain of too much information (TMI). They expect personalization but don’t want to relinquish control. They want to be enthusiastic about what they view, but can easily react with skepticism and impatience.

Organizations of all kinds are struggling to get their content affairs in order. They are trying to bring process and predictability to the creation and delivery of their content. Much of this effort focuses on people and processes. But approaches that are primarily labor-intensive will not ultimately provide the capability to satisfy escalating customer demands.

Future ready: beyond structure and modularity

Content strategy recommends being future-ready. Generally this means applying structure and modularity to one’s content, so it can be ready for whatever new channel emerges. While these concepts are still not widely implemented, the concepts themselves are already old, having been a recommended best practice since the early 2000s (see for example, the first edition of Anne Rockley’s Managing Enterprise Content, published in 2002). Adoption of structure and modularity has been slow to take hold due to the immaturity of standards and tools. But it does seem that structure and modularity is now crossing the chasm from being a specialized technical communications practice towards mainstream acceptance. While it can be easy to become preoccupied by the implementation of current practices, content strategy shouldn’t stop thinking about what new practices are needed.

The content must be future-ready: able to adapt to future requirements. Equally importantly, one’s content strategy must be future-forward, anticipating these requirements, not just reacting to them. When discussing the value of “intelligent content,” the content strategy discipline has largely focused on one part of the equation: the markup of content, and how rules should govern what content is displayed. It has generally avoided more algorithmic issues. To realize the full possibilities of intelligent content, content strategy will need to move beyond markup and into the areas of queries and text and data analysis. These areas are rich with possibilities to add value for audiences, and enable brands to offer better experiences.

Emerging practices

Content strategy can learn much from other content-intensive professions, especially developments coming from certain areas of journalism (data journalism and algorithmic journalism), the cultural sector (known as GLAMs), and computer-oriented humanities research (digital humanities).

These disciplines offer four approaches that could help various organizations with their content strategy:

  1. Data as Content
  2. Bespoke content
  3. Semantic curation
  4. Awareness of meaning

Data as Content

Savvy journalists are aware that there can be engaging stories hidden in data. Data is solid and concrete compared to anecdotes. Data can be visual and interactive. Data is happening all the time: the story it tells is alive, always changing. The fascination of data is evident in the growing trend to monitor and track one’s own data: the so-called quantified self. We gain a perspective on our exercise or eating we might not otherwise see. The possibility for content strategy is to look not just at “me data” but also “we data”: data about our community. There are numerous quality of life indicators relating to communities we identify with. We already track data about communities of interest: the performance of our favorite sports team, or the rankings of the university we attended. But data can provide stories about much more.

Data journalists think about sources of data as potential story material. How do the property values of our local neighborhood compare with other neighborhoods? If you adjust these findings for the quality of schools, or average commute time, how does it compare then? Journalists curate interesting data, and think of ways to present it that is interesting to audiences. Audiences can query the data to find exactly what interest them.

Brands can adopt the techniques of data journalism, and use data as the basis of content. Brands can tell the story of you, the customer. For example, looking at their data, what do they notice about changes in customer needs and preferences? People are often interested in how their perspectives and behavior compare with others. They want insights into emerging trends. By offering visual data that can be explored thematically, customers can understand more, and deepen their relationship to a brand. The aggregation of different kinds of customer data (even what colors are most popular in what parts of the country) can provide an interesting way to tie together an egocentric angle (reader as protagonist) with a brand centric story (what the brand does to serve the customer). Data about such attributes can humanize activities than might otherwise appear opaque.

I can imagine data storytelling being used in B2B content marketing, where demonstrating engagement is a pressing need. There are opportunities to provide customers with useful insights, by sharing data about order and servicing trends for product categories. Providing data about the sentiment of fellow customers can strengthen one’s identification as a customer of the brand. Obviously this information would need to be anonymized, and not disclose proprietary data.

Bespoke Content

Bespoke content represents the ultimate goal of personalization. It is content made to order: for a person, or to fit a specific moment in time. The tools to create bespoke content are emerging from another area of journalism: robot journalism.

In robot journalism, software takes on writing tasks. Where data journalism uses data to tell stories with interactive charts and tables, robot journalism writes stories algorithmically from data. The notion that computers might write content may be hard to accept. Many content strategists come from a background in writing, and may equate writing quality with writing style. But when we view writing through the lens of audience value, relevance is the most important factor. Robot journalism can provide highly customized and personalized content.

Organizations such as the Associated Press are using robot journalism to write brief stories about sports, weather and financial news.

The process behind algorithmic writing involves:

  1. Take in data related to a topic
  2. Compute what is “newsworthy” about that data
  3. Decide how to characterize the significance of an event
  4. Place event in context of specific interests of an audience segment
  5. Convert information into narrative text

Good candidates for robot journalism are topics involving status-based, customer-specific information that is best presented in a narrative form.  A simple example of an algorithmically authored narrative using customer and brand data might be as follows:
“Your [car model] was last service on [date] by [dealer]. Driving in your region involves higher than average [behavior: e.g., stop and go traffic} that can accelerate wear on {function: e.g., brakes}. According to your driving history, we recommend you service [function] by [this date]. It will cost [$]. Available times are: [dates] at [nearest location].”

Although conditional content has been used in DITA-described technical communications for some time, robot journalism takes conditional content a couple steps further by incorporating live data, and by auto-creating the sentence clauses used in narrative descriptions, rather than simply substituting a limited number of text variables such as a product model name.

The approach can also be used for micro-segments, such as product loyalists who have bought three or more of a product over the past twelve months. A short narrative could be constructed to share the significance of something newsworthy relating to the product. A wine enthusiast might get a short narrative forecasting the quality of the newest vintage for a region she enjoys wine from.

Writing such bespoke narratives manually would be prohibitively expensive. Robot journalism approaches will enable brands to offer customized and personalized narrative content in a cost-effective way and at a large scale.

Semantic Curation

Today multiple issues hinder content curation. Some curation is done well, but is labor intensive, so is done on a limited scale that only touches a small portion of content. Attempts to automate curation are often clumsy. Much curation today is reactive to popularity, rather than choosing what’s significant in some specific way. We end up with lists of “top,” “favorite” or “trending” items that don’t have much meaning to audiences: they seem rather arbitrary, and are often predictable.

True curation aides discovery of content not known to a reader that reflects their individual interests. Semantic curation empowers individuals to find the best content that matches their interests. By semantic, I mean using linked data. And leading the way in developing semantic curation is a community with deep experience in curation: galleries, libraries, archives, and museums (GLAM).

GLAMs have been pioneers developing metadata, and as a result, have been some of the first to experience the pain of locked up metadata. Despite the richness of their descriptions of content, these descriptions didn’t match the descriptions developed by others. It is hard to pair together the content from different sources when their metadata descriptions don’t match. So GLAMs have turned to linked open data to describe their content. It is opening up a new world of curation.

The development of open cultural data is a significant departure from proprietary formats for metadata. When all cultural institutions describe their content holdings in the same way, it becomes possible to find connections between related items that are in different places. For GLAMs, it is opening access to digital collections. For audiences, it enables bottom up curation. Individuals can express what kind of content they are interested in, and find this content regardless of what source has the content. Unlike with a search engine, the seeker of content can be very specific. They may seek paintings by artists from a certain country who depicted women during a certain time period. No matter what physical collection such painting belong to, the content seeker can access the content. They can access any content, not just a small set of content selected by a curator.

The potential to expand such interest-driven, bottom-up curation beyond the cultural sector is enormous. While the work involved in creating open metadata standards is far from trivial, significant progress is being achieved to describe all kinds of content in a linked manner. The BBC has been exemplary in providing content curated using linked data on topics from animals to sports.

Awareness of Meaning

Content analytics today are not very smart. They show activity, but tell us little about the meaning of content. We can track content by the section on a website where it appears, the broad topic it is classified under, or perhaps the page title, but not by what specifically is discussed in an article. When we don’t understand what our content is actually about, what it says specifically, it is hard to know how it is performing.

This problem is well known to people working with social media content. It helps little to know that people are discussing an article. It is far more important to know what precisely they are saying about it.

As Hemann and Burbary note in their recent book, Digital Marketing Analytics: “There is not currently any pieces of marketing analytics software that can do as good job as a human at… classifying the social data collected into meaningful information.” People must manually apply tags to social content in their social listening tool for later analysis. This is labor intensive, and often means that only some of the content gets analyzed. The problem is largely the same for brand created content: CMSs don’t generate tags automatically based on the meaning of the text, so tagging must be done manually, and is often not very specific.

Again, the innovation is coming from outside the disciplines of content management and marketing. Scholars working in the field of digital humanities (DH) have been working at ways to query and tag large bodies of textual content to enable deeper analysis. Some the techniques are quite sophisticated, and rely on widely available open source tools. It is surprising these techniques haven’t been applied more frequently to consumer content.
DH techniques examine large sets of digital content to learn what these sets are about, without actually reading the content. Perhaps the most famous example of such techniques is Google’s Ngram Viewer, which can find the frequency of different phrases over time in books to learn what idioms are popular, or how famous different people are over time. (You can learn about the origins and applications of Ngram Viewer in the book Uncharted.)

Employing diverse methods, the techniques are often referred to as text analytics. Two leading approaches to text analytics are topic modeling, and corpus linguistics. Topic modeling allows users to find themes in large bodies of text, by identifying key nouns that when discussed together signal the presence of a specific topic. Corpus linguistics can identify phrases that are significant, that are used more frequently than would be expected.

Text analytics can be useful for many content activities. It can be used in content auditing, to learn what specific topics has a brand been publishing about, or to learn more about how the brand’s voice is appearing in the actual content. These same approaches can be used for social media analysis. Topic modeling also can be used to auto categorize content for audiences, to provide audiences with richer and more detailed navigation.

A complex machine is not necessarily an intelligent one.  (author photo)
A complex machine is not necessarily an intelligent one. (author photo)

The Opportunities Ahead

This quick tour of emerging practices suggests that it is possible to apply a more algorithmic approach to content to improve the audience experience. Unfortunately, I see few signs that CMS vendors are focused on these opportunities. They seem beholden to the existing paradigm of content management, where individual writers are responsible for curating, tagging and producing nearly all content. It’s an approach that doesn’t scale readily, and severely limits an organization’s capacity to deliver content that’s tailored to the interests of audiences.

It is a mistake to assume that greater use of technology necessarily results in greater complexity for authors. Some new practices need to be performed by specialists, rather than foisted on non-specialist authors who already are busy. When implemented properly, with a user-centric design, new practices should reduce the amount of manual labor required of authors, so they can focus on the creative aspects of content that machines are not able to do. As the value of content becomes understood, organizations will realize they face a productivity bottleneck, where it becomes difficult to deliver sophisticated content they aspire to with existing staff levels. The most successful publishers will be ones that adopt new practices that deliver more value without needing to add to their headcount.

Noz Urbina notes the importance of planning for change early if organizations hope to adapt to market changes. “I fear communicators are in a vicious cycle today. As the change in our market accelerates, the longer we avoid taking on revolutionary changes in search of simple short-term incremental changes, the bigger our long-term risk. Short term simple can be medium-long term awful. The risk increases with every delay that in 2 years’ time, management or the market will push us to deliver something in a matter of months that would have needed a 3-7 year transition process to prepare for. This is a current reality for many organisations for whom I have worked.”

The best approach is to learn about practices that are on the horizon, and to think about how they might be useful to your organization. Consider a small scale project to experiment and pilot an approach to learn more what’s involved, and what benefits it might offer. Very small teams are doing many interesting content innovations, often as a side project.

—Michael Andrews