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Content Efficiency

Four approaches to content reuse

How organizations approach reusing content impacts their publishing efficiency, and their ability to serve audience needs. Four distinct approaches to content reuse exist, each of which focuses on different goals. Due to specialization in the content profession, content professionals may be familiar with only some content reuse approaches. To support broader organizational objectives effectively, content strategists should become familiar with all four alternative approaches to reuse, since each offers each unique benefits.

Why content reuse matters

While content reuse is a topic of active discussion in the content profession, no one definition for content reuse adequately captures its various meanings. In practice, there are four distinct types of content reuse:

  • Ad hoc reuse of assets
  • The planned reuse of content components
  • Enabling reuse of content across channels
  • Selective reuse through adaptive content

Nearly everyone agrees reusing content is a good thing. Content professionals sometimes invoke the phrase “single sourcing” to suggest the notion that one “source” can serve all needs, both internally and for audiences. What is being reused, exactly? Is the source a database? A file? A finished piece of content?

Many different specialities work with content. Each specialty is working to solve an aspect of reuse and will tend to promote its approach as a solution the core problems associated with poor content reuse. But specialists are not always aware of the larger picture needs of complex organizations or multidimensional audiences. Solution advocacy can sometimes create own silo problems!

When discussing content reuse, it is important to distinguish between reusing as-is content, recycling (repurposing) content, and providing on-demand, customized content. Is the source granular or whole? For example, is the source a whole video recording, or a collection of video snippets? Is the source a document, or a library of documents?

Different reuse approaches reflect different goals. All are valid, but none are complete. At present, no one approach will address all needs faced by enterprise scale publishers.

Specifying content

The term content is abstract and fuzzy, open to various interpretations. Content may be raw or finish, partial or complete. We need to understand different levels or states of content. Fortunately, we can draw on insights from library science to distinguish different levels of specificity by using a concept called the FRBR. [1]

The FRBR model provides levels to analyze content, divided according to how explicit the description of the content is. The key levels of concern to us are work, expression, and manifestation. If the content item is a book, it might be described as follows:

  • Work (Bible)
  • Expression (King James translation)
  • Manifestation (1994 Oxford University Press edition)

The work is the raw content, the underlying intellectual property. It might be a class of content such as a novel or symphony. It describes the content or asset.

The expression identifies a version of the content.

The manifestation specifies the content’s specific revision or a rendition, for example, the edition, format, mode of access, or date of publication.

The table below illustrates the hierarchy, with rough equivalents in content strategy.

FRBR Concept Level of Identification Rough Equivalent in Content Strategy Example
Work Described by a Title Assets relating to a topic Long, unedited video file
Expression Uniquely ID’ed Collection of content components relating to a topic Tagged video clip highlights
Manifestation Versioned Finished content about topic Linked series of transcript-captioned video segments

Different levels of content reflect different frequencies of change and target audiences. Assets don’t change; they are repurposed. Components can be revised, but there will only be one version of a component at a given time. Content composites seen by audiences may come in multiple versions, which can exist simultaneously.

Rather than describe everything as content, it is more helpful to separate different notions:

  • content (items audiences consume)
  • content components (recurring elements incorporated in audience facing content)
  • assets (intellectual property used to create finished content)

Delivering equivalent content to different platforms: COPE

As content channels have multiplied, publishers have needed to make their content available to different devices and different kinds of content customers. The approach known as COPE (Create Once, Publish Everywhere) addresses the issue. Rather than recreate multiple versions of the same content for different devices or platforms, publishers can use standards and structure to provide the same content through an API that can be accessed by a variety of applications. The same content is used in multiple contexts, often distributed simultaneously. Since reuse can imply using the same content at different points in time, the notion of “content once” being published everywhere may be better thought of as multi-use content distribution.

One goal of COPE is the wide dissemination of content across different channels. COPE started as a technology solution to address point-of-failure concerns when publishing to multiple parties from a single database of content. Over time, it has evolved into an approach to syndicate content to other parties.

What COPE does

In the COPE approach, a central content database provides multiple versions of the same content to different people and devices. The original idea didn’t foresee revisions to the content (hence: create once), and also presumed that core essence within content items pushed to different endpoints would be essentially the same. Different technical packages (formats and associated metadata) allow endusers to consume the version of content they want. Technical endusers (content partners and third party app developers) are able to choose which content items they want, but generally lack the ability to request specific components of content from within an item. The API disseminates a large, structured chunk, but not finely defined, reconfigurable chunks. Content consumers choose which content host to use to access the content. They might use their local radio station’s website, or NPR’s own app to access the same content.

Benefits and limitations of COPE

COPE is an effective approach to disseminate articles to multiple partners and platforms. Because of its push orientation, it is not optimized to offer personalized content that responds to specific requests from content consumers. As originally conceived, the body of the content is static.

Reusing common elements in different content products: the DITA model

While COPE is largely focused on formats and metadata, another reuse approach is focused on reusing components of content within the body-field of an item.

Publishers of technical content have championed reusing specific content components in different items of content. Technical documentation is repetitive. Much writing is redundant, where the same text is being repeated in many places. Technical writers sometimes speak about the ideal of WOOO: Write Once and Once Only.

Component reuse is closely associated with an approach called DITA (Darwin Information Typing Architecture), an XML schema originally developed by IBM. DITA is designed to address specific publishing issues with user assistance for technical products, though many DITA proponents argue it can be successfully used for other kinds of content.

For the most part, the motivations behind DITA have been writing efficiency and consistency, rather than audience needs. Few individuals will ever read the many minor variations of content possible with a DITA document, and content variations are largely defined by topic variants rather than reflect audience preferences.

Reusing Components through Transclusion

Most approaches that reuse content components rely on transclusion. Transclusion is the process of incorporating content into an item of content from another source by use of a link to that source. In its most simple form, it is similar to when one embeds an item of content in another, such as embedding a slideshow or YouTube video hosted elsewhere in an article you’ve written. In DITA, the process is called a conref or content reference. Transclusion is a core concept not only in DITA but also in MediaWiki, which powers Wikipedia among other sites. Transclusion allows the same content to be used in multiple locations in Wikipedia.

Transclusion can be applied to any item of content: a word or phrase, a paragraph, or a large section.

A related approach is to show and hide components depending on certain criteria, perhaps intended audience segment. Business customers might see a certain paragraph, while consumers wouldn’t see that paragraph. The process of showing and hiding XML nodes is called profiling in DITA. It allows the output of multiple documents (variations on the master document) from a single source.

Benefits and limitations of Transclusion

Reusing components is effective when there is a repetition of messages, and regular variations among specific components. It can provide efficiencies and consistency for content that is highly regular and needs to be delivered in a uniform manner. If business requirements mandate that all customers see the same terms and conditions in the content regardless of what content they see, transclusion can be an effective approach.

The weakness of transclusion is that it is not very flexible. DITA, for example, assumes a linear flow of content from the publisher to the content consumer. It presupposes content elements can be planned and compiled into well-structured formats. That vision implies the presence of regular content entities and that one can anticipate the exact circumstances of when these entities are required by endusers.

Embedding content through link referencing, or hiding content through profiling, is not very dynamic. The process can groan when the variations become complex. It is also difficult for the publisher to confidently say precisely what an audience wants, and so there is a tendency to deliver too much content because it is easy to include it. Transclusion, by itself, doesn’t adapt to specific audience demands for information, or marketers’ desire to change the messaging in response to CRM and real time analytics data. The motivation to write once only doesn’t accord with audience desires to pick and choose what content they want to see at a given time. It is not clear if the XML-based structure of DITA will be up to the demands of real time personalization associated with performance-based marketing.

Mark Baker noted recently some other shortcomings of transclusion:

“Reusing text where you would have been writing substantially the same text anyway is clearly the right thing to do. But taking all the various ways in which you might express an important idea and combining them into one expression is a bad idea. Your idea will have more impact and more reach if it is expressed in different ways and in different media for different audiences, different purposes, and different occasions.”

Asset Reuse: the DAM model

A third approach to content reuse relates to assets. Reusing assets allows organizations to exact more value from their intellectual property. It recognizes that rich assets can be potentially applicable to different contexts at different times. A systematic approach to asset reuse requires a centralized repository for the raw material that authors draw upon to create audience-facing content.

How Asset Reuse works

A growing number of web publishers — though still a minority — have repositories to hold digital assets that are used to create content for audiences. They may use:

  • A digital asset management (DAM) system for videos, audio, graphics and photos, including brand assets and templates
  • An enterprise content management (ECM) system for complex documents, such as legal documentation
  • A database or file server to store code or data files that can be repurposed

Such repositories differ in purpose for content management systems, which are geared toward the creation and management of content for audiences. Unlike a CMS, a DAM may contain content that is neither currently published, nor being readied for publication.

The varied types of assets that can be stored in a repository share certain characteristics. Assets frequently involve complex workflows. They may involve substantial editorial oversight, to produce and prepare for publication. Unique approvals may be required, such as for branding assets stored in a DAM, or legal copy stored in an ECM. Data, perhaps from a periodic customer survey, may be stored in databases that require running structured queries and reports before they can be made available for content authors to use. Photo archives may have permissions and licensing requirements that must be vetted before items are available for publication.

When considering asset reuse, it helps to know how stable the asset is. Elizabeth Keathley distinguishes between static assets and living assets.

Static assets are generally stable and don’t change often. If they do change, there will only be one version at a time, with a persistent ID. These assets may have associated use rights governing when and how they are used, and by whom. The asset creator may have an explicit goal of preventing derivative reuse, such as prohibiting unapproved modifications of brand assets.

Living assets can be repurposed to support different goals, and are sometimes converted into different formats. Living assets are commonly composed of compound asset parts and have elaborate workflows to produce them. They are not simply derivative of other assets but are substantially original. A living asset is broadly equivalent to a work in FRBR terminology. Other items of content are derived from a living asset, and these will have identities separate from the master asset. Because the structure of living assets is complex and irregular, they are not as readily broken into content components, especially if an exact need for elements in the asset cannot be predicted in advance. Also, the nature of repurposing content means that the approval process will be different than it is for content components involving planned reuse for defined purposes.

Benefits and limitations of DAMs

DAMs and other asset repositories can offer authors a richer library of content than available in CMSs. Unlike with a CMS, authors are not restricted to a narrow perspective where they only see and have access to currently published content.

DAMs have challenges as well. Unless actively managed, metadata descriptions can be poor, hindering asset retrieval. Some DAM systems are improving auto tagging of assets to reduce the burden on contributors. Another limitation is that DAM assets are generally not directly accessible by audiences, so audience requirements for access to this content needs to be understood and planned in advance.

A framework for content reuse

Shows relationship between DAMs for digital assets, DITA, COPE, and adaptive content

The conceptual diagram reflects different content reuse activities according to their purpose. It is not meant to show specific platforms or systems, which vary considerably in practice. Only a few publishers perform all these activities as part of an integrated end-to-end process. The path from potential assets to ready-to-consume content resembles a waterfall: one is dependent on what content is available upstream.

The limits of specialized solutions

Relying on one approach entails various potential pitfalls. Not having a DAM means that potentially valuable content assets are siloed within different organizational departments and not available to authors. A failure to plan for modular reuse of content components hinders efficiency and consistency, and hurts the audience experience as well. Relying on responsive web design might be effective to reach immediate consumers, but won’t allow partners to reuse your content the way an API would allow, and might therefore reduce the total reach of your content.

Many aggravations arise from a poor conceptual understanding of the granularity of content, and how frequently different elements change and are used within the organization. Authors may try to reuse content that is actually a compound object made up of different assets and components. They may actually need to only reuse some parts of the content.

A core issue with reuse is whether the content continues to be up-to-date and accurate. Unfortunately, just because something is currently published does not indicate it should be reused elsewhere. A table that complements an article might be sufficiently current to stay on a website, but really shouldn’t be incorporated in new content without updating. Content created for one audience may seem to offer a good blueprint for new but similar content for another audience. But in the course of repurposing this content, the authors may conclude that revisions are needed for content that is being reused. What is sufficiently current is often a judgment call based on resources and mission importance.

Publishers face another challenge: the tension between content modularity and integration. While technical documentation can generally be disaggregated into modular components, other content is more powerful when tightly integrated. Ideally content elements should support one another, rather than simply be presented together. But cross-dependency among elements make them less attractive candidates to manage as separate components. A reusable, adaptable template may be a better approach when elements tend to occur together in an integrated manner. Authors may want to reuse the structure of the body of the content without reusing the actual content components.

Adaptive content and reuse

The newest approach to content reuse is known as adaptive content. Unfortunately, there is no widely accepted definition of adaptive content, and content professionals tend to speak about adaptive content in different ways. The phrase provokes two obvious questions:

  1. What adapts?
  2. To what does it adapt?

Sometimes people will speak about “the content” adapting to “the device” the individual is using. That interpretation is not much different from responsive web design, and is not very ambitious. It should be possible to have the content itself change based on any number of criteria, such as contextual factors (location, time of day, user status), and various user preferences or behaviors. I would rather define adaptive content in terms of the goal it supports.

Adaptive content
content that changes what is presented to reflect the intentions of the content consumer.

How Adaptive Content works

Adaptive content relies on the use of algorithms and audience data to change the content. There are significant differences between preplanned content variations such as are specified in DITA, and enabling dynamic, on-demand variations associated with adaptive content. Adaptive content builds on transclusion and COPE, but extends it.

Content reuse to support adaptive content must accommodate on-demand access to content by individuals, to deliver content composed of components that reflect the interests and needs of an individual when they ask for them.

An early example of adaptive content is the NPR One app for audio content. Individuals indicate what kinds of programming they want, rather than having the publisher deciding that for them. NPR extends its API not only to content partners (local radio stations who add local content), but also to the end consumer of the content, giving them control over what content they receive through likes and shares. The app is adaptive, but not entirely a content on-demand solution, since it is based on streaming.

Benefits and limitations of adaptive content

To realize the goal of having content components available on demand, responding to user preferences in real time, will remove the problems associated with publishers making wrong guesses about what someone wants to view. The limitation of this approach is the complexity it introduces for publishers. They need to think even harder about where the value of their content resides, based on actual use analytics, and structure the content elements to allow retrieval. Web searchers can now cherry-pick information in the search results to get the exact content items they want from articles marked up in schema.org. Such behavior provides a preview of how content will need to become adaptive to user needs.

Conclusion

Content reuse is rich with possibilities. Different content specializations are working to improve reuse. It is useful to understand different approaches. By combining approaches, one can support an integrated strategy that improves both internal goals such as efficiency and governance, and external goals such as personalization and engagement.

— Michael Andrews


  1. FRBR stands for Functional Requirements for Bibliographic Records. FRBR’s focus is on bibliographic records for long-form content such as books, sound recordings, and films. Its focus is different from that of content strategy, so it will not be exactly equivalent. It offers helpful insights as long as we don’t expect literal compliance to its terminology. My apologies to librarians if I run roughshod over these concepts.  ↩
Categories
Intelligent Content

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.

Data

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

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.

Examples

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