Category Archives: Content Experience

How Content Can Answer Unanticipated Questions

How can publishers answer questions that audiences may have, when they don’t always know what will interest people? This is not a trick question. To be agile, publishers need to plan for flexibility.   They need to prepare content for scenarios they can’t anticipate in advance.

Content design has never been more important.  People have less time than ever to deal with unwanted content.  But content design should not be about spoon-feeding audiences answers to pre-approved questions.  Content design should instead empower audiences to consume the precise content they need.  Publishers should enable audiences to decide the answer that matches their need.  Publishers shouldn’t believe they can always anticipate what audiences need.  They can’t  always package content to match a  known need.  Recent developments in search technology are shaking up thinking about how to provide answers to audiences.

The Limitations of Questions as Templates for Content Development

Current practices presume a certain process.  We should start with a list of questions that users have, then write content answering those questions. The question will tell us what content to create. This approach, however, has limitations which may not be obvious.

I’ve long been an advocate and practitioner of user research.  It makes no sense to create content users indicate they have absolutely no interest in.  But user research is merely a starting point for considering user questions.  It should not be the final arbiter of what could be important to users.

“People are really fascinating and interesting … and weird! It’s really hard to guess their behaviors accurately. ” — Peter Koechley, Upworthy

Many user questions can’t be guessed — or discovered — in advance.  When doing user research, organizations can be over-confident about what questions they think users will have in the future.  User research probes the motivational level of interests and needs, rather than the more granular informational level of specific questions.  User research helps to  understand users, but it will simplify user needs into personas.  The diversity, and contextual complexity, that spawn the range of real word user questions gets smoothed over.  Qualitative user research data is too broad to uncover the full range of potential questions in detail.  Quantitative data analysis of past online queries can provide more granular insights, But even quantitative data won’t predict all situations, especially when novel situations arise.

Two common approaches to question-templated content development are:

  • The “top tasks” approach
  • The long-tail approach.

Some content strategists favor the top task approach  — especially those who focus on task-oriented transactional content.

Many SEOs favor the long tail approach — especially those who want to promote awareness-orientated marketing content.

The top tasks approach makes assumptions about essential user questions, based on past user behavior with a website.  An organization may decide that the top 10 search queries drive 90% of web traffic, so those 10 questions are the ones to offer answers.  Each question gets one answer.  It’s a rearview approach that assumes no curiosity on the part of audiences.  Audience needs exist only as an extension of their interaction with the organization.  All questions considered relevant relate to user tasks linked to that specific organization.

The hidden assumptions of the top tasks approach are:

  • Everyone has the same questions
  • Because everyone has the same questions, everyone should get the same answers
  • If different people start to ask different questions, publishers can ignore those questions, because they aren’t top questions.

Providing homogenized answers to homogenized questions is appealing to homogenized organizations.  Especially to  government offices, banks, or tech support units.  But cookie cutter content can seem like it’s created by a faceless organization.  Standardized answers don’t satisfy customer’s growing expectations. They expect more personalized service.

The long tail approach tries to anticipate user questions by crafting answers for many question variations.  Each variation addresses an ever narrower ranges of questions. The idea is to get an inventory of questions all kinds of people are asking, and then develop answers to all these questions, so there is something for everyone.  On the surface, this approach seems to deliver more individualized answers.  But we will see, that is not always the case.

Both the top tasks, and long tail, approaches assume that each question has one answer.  A content item exists to answer that one specific question.

In practice, the formula that one question has one answer doesn’t hold.   Different questions lead to the same content.  Type question variations on Google, and Google rewards you with the same links going to the same content.  Not all question variations are substantially different.  If you type “How to fly a kite” in Google, you can see related questions such as “How to fly a kite step-by-step” or “How to fly a kite by yourself”.  You’ll also find “long tail” questions such as “How to fly a kite with little wind” or even more optimistically, “How to fly a kite with no wind”.

The notion of a related search is vague.  It could be a search query that is essentially equivalent to another, but phrased differently.  It could be question that implies distinctions or details that may not be present in the information or that may not even be crucial.  Suppose we imagine content addressing “How to fly a kite for firefighters” and another on “Easy steps to kite flying for bus drivers”.  We’d likely find the essence of this long tail content is no different from the more general answer.  The idea that long tail content is necessarily more relevant is fiction.

The other characteristic of question-templated content is that the questions and answers are pre-assembled and frozen.  If we phrase a question differently, such as “What’s different about kite flying for bus drivers?”, we aren’t likely to get an answer.  At most, we’ll get content talking about kite flying that for some reason mentions bus drivers.  The content creator decides what content the reader will get, instead of the reader deciding.

Content design should be built on a foundation of compositional content.  What content is assembled and delivered can be based on the specific question asked.  Suppose you want to ask “How to tell someone to ‘go fly a kite’ ”?  When decomposed, the question reveals two distinct sub-questions.  One sub-question concerns how to deliver a message in general, covering tone or medium.  The other sub-question concerns what message alternatives are available about a specific issue — in this example, the desire to get someone else to change their behavior.

In principle, machines can assemble an answer to such a complex question, even though no person has created an answer to that specific question already.   The machine would draw on two components.  One would component address points to make about an issue; and the other component would address ways to deliver those points.

A compositional topic could be rich in variations that would yield different answers.  It could address: “How to tell a colleague…” or “How to tell a nosy relative…,” or whomever.  The answer could include components about the general aspects of the issue, which could be supplemented with some advice specific to the question variation.

For those familiar with structured content, the use of components to create content variations will seem familiar.  The difference here is that users initiate the assembly of components in novel configurations.  We don’t know in advance what the user wants, so we therefore have to provide them with the raw material to supply the answer to their unknown query.

Information Generates Questions

Part of the reason people can be unpredictable in their questions is that their interests and understanding evolve over time.  Sometimes the facts of a situation can change as well.

Laura E. Davis, digital news director of USC’s Annenberg Media Center, recently wrote about “Writing answers before you know the question.”   Her question flips the assumption that most writers have: that writers know reader questions ahead of time, and the task of the writer is to provide answers to them.  Most writers expect that information presented will follow the questions audiences ask.  But the reverse is also true. Information, or the expectation of information, sparks questions.  Sometimes writers will never have thought of the questions their readers might have.

Davis cites several trends that are making audience questions less predictable.  Audiences are becoming more conversational in how they access content.  Questions can unfold in a conversation, without knowing where they may lead.  Events can unfold quickly, and not conform to a tidy summary answer. These issues gain importance as conversational interfaces become more common.  “As we move forward, more and more, we’ll be writing answers before we know the question.”

In conversation, questions and answers flow spontaneously.  How can content become more spontaneous?  How can content prepare for a “zero UI” future, as Davis puts it?  We’ll look at two approaches, metadata and machine reading, which publishers can combine to offer laser precision in answers.

‘Literate machines’ will provide dynamic answers

Historically, questions asked online were answered by a list of hyperlinks.  Even today, many chatbots provide an answer by pointing to a hyperlink of content the reader must read.   When a computer points a user to a document title (in the form of a hyperlink), it generally is pointing the user to pre-assembled content.  Pre-assembled content runs a high risk of not being exactly what the user is looking for.

Yet the more recent trend is to provide answers directly, instead of answering queries by providing links to documents.  Everyone is familiar with Google’s instant answers. This approach is being adopted most of the other major tech companies as well.  How answers are being delivered is transforming quickly.

Advances in semantic technology and AI are allowing both questions and answers to become more iterative, and fluid.  Users may not consider a single answer to a question they pose as complete. They may want several pieces of information to give them a complete understanding.  To give users complete answers, machines stitch together several fragments from different source.  Audiences can ask clarifying or follow up questions to fill out their knowledge, and contextual answers will appear.

Semantic metadata facilitates machine discovery and understanding of information.  Metadata is powerful because it can relate information from different sources. Publishers can include their information as part of a relevant answer to a user query.  For example, suppose a user asks “What local cinemas are showing films made before 1960 this evening?”  There may not be a single item of content providing that answer.  But metadata from different content can assemble an answer.  The listings of local cinemas can be combined with data about films from a film encyclopedia (to filter by year).  The ability of metadata to assemble information from many sources upends the expectation of some publishers, who believe they must provide comprehensive information about topics to answer any audience question.  Instead, their goal should be to focus on providing comprehensive information that they are uniquely positioned to offer, and to link through metadata to other sources that provide related information that might arise in a question asked by users.

The question in this example may seem arbitrary — and it is.  Why would someone want to watch films made before 1960?  What special about 1960?  Why not 1965?  Or 1950?  Because the question, seen from the outside, seems arbitrary, no one will create content specifically to answer this question.  The variations in how the question could be framed are limitless.  Which is why metadata is powerful in providing answers to questions that may be infrequently asked, or have never been asked previously.  Just because a question is novel does not mean it is unimportant.

Given the quantity of content that’s created, someone may have written content that provides part of an answer to a question.  But that answer could be buried within a larger discussion that isn’t the focus of the user’s question.  If you are curious where a new film start grew up, there might not be specific content answering that question.  But he or she may have mentioned it in passing during an interview about their latest film.  How might you locate that information without reading various interviews in full?

Machine reading comprehension (MRC) is an emerging technique that promises to transform how content is used.  Its premise is simple but awe inspiring.  Machines can read texts just like humans do, and understand what the text means.  They can do this at incredible speeds, so that can locate specific statements quickly, interpreting what the statement means, relating it to questions or statement made elsewhere.   Machine reading does not require structure, but it presumably benefits from having structure.

Amy Webb at NYU demonstrated how machine reading comprehension works in a recent presentation (here at minute 34) . Reading a book, MRC can extract the meaning.  Yes, someday soon computers will be able to speed-read War and Peace and be able to tell us what the novel is about (beyond the obvious, that it’s about Russia.)

slide with text
Slide from Amy Webb presentation on machine reading comprehension (MRC) at ONA17 conference.

MRC has been a keen research focus of many firms developing audio interfaces.  Audioburst is a new service that digests the transcripts of audio interviews.  Users can ask Alexa a question about a news topic, and Alexa can query Audioburst to find snippets of content relevant to the query, and will combine and play back different audio clips from different radio programs related to the question.

Microsoft has been at the forefront of MRC research.   I want to highlight some of their work because they are combining MRC with semantic metadata in products that are widely used.

“We’re trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it’s written or orally.” — Kaheer Suleman of Microsoft

Microsoft notes: “Machine reading comprehension systems also could help people more easily find the information they need in car manuals or dense tax code documents.”

MRC is being used in Microsoft products such as Cortana (the voice assistant similar to Alexa or Siri), and Bing (the search engine that competes with Google).

A recent news article states: “Microsoft’s virtual assistant Cortana will get an upgrade as well, allowing it to make use of machine reading comprehension to summarize search results. ”

Earlier this month, Bing announced it would use MRC: “Bing’s comparison answers understand entities, their aspects, and using machine reading comprehension, reads the web to save you time combing through numerous dense documents.”

screenshot of Bing blog post on MRC
How Bing uses machine reading to provide multifaceted answers based on text from different sources


For Bing users this means:

  • “If there are different authoritative perspectives on a topic, such as benefits vs drawbacks, Bing will aggregate the two viewpoints from reputable sources”
  • “If there are multiple ways to answer a question, you’ll get a carousel of intelligent answers.”
  • “If you need help figuring out the right question to ask, Bing will help you with clarifying questions.”

As the Microsoft examples highlight, the notion that there is only one best answer to a question is no longer a given.  People want different perspectives, and different levels of detail.  Literate machines can help people retrieve answers that match their interests.


Information-rationing is not in the best interests of content consumers.  Content strategists have long warned of the dangers of providing too much information.  But too much information isn’t necessarily the problem.  No one complains about Wikipedia having too much information.

My advice to content creators is this.  If you have unique information to share, you should publish it.  Even if you’re not sure whether users have a pre-existing need to look for that information, it could be valuable.  Self-censorship does not make sense.  At the same time, content creators should not feel they must create a complete or definitive presentation of a topic.  Increasingly, machines will be able to stitch together information from different sources for the benefit of users.  Content creators should focus on what they know best.  Duplicating information that exists elsewhere benefits no one.

We can’t predict what information people will need in the future. Content that is information-rich is worthwhile content.  We need to make such information accessible, so audience can retrieve it when it is be needed.  We need to help make machines literate.

— Michael Andrews

Should Information be Data-Rich or Content-Rich?

One of the most challenging issues in online publishing is how to strike the right balance between content and data.  Publishers of online information, as a matter of habit, tend to favor either a content-centric, or a data-centric approach.  Publishers may hold deep seeded beliefs about what form of information is most valuable.  Some believe that compelling stories will wow audiences. Others expect that new artificial intelligence agents, providing instantaneous answers, will delight them. This emphasis on information delivery can overshadow consideration of what audiences really need to know and do. How information is delivered can get in the way of what the audience needs. Instead of delight, audiences experience apathy and frustration. The information fails to deliver the right balance between facts, and explanation.

The Cultural Divide

Information on the web can take different forms. Perhaps the most fundamental difference is whether online information provides a data-rich or content-rich experience. Each form of experience has its champions, who promote the virtues of data (or content).  Some go further, and dismiss the value of the approach they don’t favor, arguing that content (or data) actually gets in the way of what users want to know.

  • A (arguing for data-richness): Customers don’t want to read all that text!  They just want the facts.  
  • B (arguing for content-richness): Just showing facts and figures will lull customers to sleep!

Which is more important, offering content or data?  Do users want explanations and interpretations, or do they just want the cold hard facts?  Perhaps it depends on the situation, you think.  Think of a situation where people need information.  Do they want to read an explanation and get advice, or do they want a quick unambiguous answer that doesn’t involve reading (or listening to a talking head)?  The scenario you have in mind, and how you imagine people’s needs in that scenario, probably reveals something about your own preferences and values.  Do you like to compare data when making decisions, or do you like to consider commentary?  Do your own PowerPoint slides show words and images, or do they show numbers and graphs? Did you study a content-centric discipline such as the humanities in university, or did you study a data-centric one such as commerce or engineering? What are your own definitions of what’s helpful or boring?

Our attitudes toward content and data reflect how we value different forms of information.  Some people favor more generalized and interpreted information, and others prefer specific and concrete information.  Different people structure information in different ways, through stories for example, or by using clearly defined criteria to evaluate and categorize information.  These differences may exist within your target audience, just as they may show up within the web team trying to deliver the right information to that audience.  People vary in their preferences. Individuals may shift their personal  preferences depending on topic or situation.  What form of information audiences will find most helpful can elude simple explanations.

Content and data have an awkward relationship. Each seems to involve a distinct mode of understanding.  Each can seem to interrupt the message of the other. When relying on a single mode of information, publishers risk either over-communicating, or under-communicating.

Content and Data in Silhouette

To keep things simple (and avoid conceptual hairsplitting), let’s think about data as any values that are described with an attribute.  We can consider data as facts about something.  Data can be any kind of fact about a thing; it doesn’t need to be a number. Whether text or numeric, data are values that can be counted.

Content can involve many distinct types, but for simplicity, we’ll consider content as articles and videos — containers  where words and images combine to express ideas, stories, instructions, and arguments.

Both data and content can inform.  Content has the power to persuade, as sometimes data can possess that power as well.  So what is the essential difference between them?  Each has distinct limitations.

The Limits of Content

In certain situations content can get in the way of solving user problems.  Many times people are in a hurry, and want to get a fact as quickly as possible.  Presenting data directly to audiences doesn’t always mean people get their questioned answered instantly, of course.  Some databases are lousy answering questions for ordinary people who don’t use databases often.  But a growing range of applications now provide “instant answers” to user queries by relying on data and computational power.  Whereas content is a linear experience, requiring time to read, view or listen, data promises instant experience that can gratify immediately.  After all, who wants to waste their customer’s time?  Content strategy has long advocated solving audience problems as quickly as possible.  Can data obviate the need for linear content?

“When you think about something and don’t really know much about it, you will automatically get information.  Eventually you’ll have an implant where if you think about a fact, it will just tell you the answer.”  Google’s Larry Page, in Steven Levy’s  “In the Plex”.

The argument that users don’t need websites (and their content) is advanced by SEO expert Aaron Bradley in his article “Zero Blue Links: Search After Traffic”.   Aaron asks us to “imagine a world in which there was still an internet, but no websites. A world in which you could still look for and find information, but not by clicking from web page to web page in a browser.”

Aaron notes that within Google search results, increasingly it is “data that’s being provided, rather than a document summary.”  Audiences can see a list of product specs, rather than a few sentences that discuss those specs. He sees this as the future of how audiences will access information on different devices.  “Users of search engines will increasingly be the owners of smart phones and smart watches and smart automobiles and smart TVs, and will come to expect seamless, connected, data-rich internet experiences that have nothing whatsoever to do with making website visits.”

In Aaron’s view, we are seeing a movement from “documents to data” on the web. “The evolution of search results in terms of the gradual supplanting of document references by data than it is to infer that direction through the enumeration of individual features.”  No need to read a document: search results will answer the question.  It’s an appealing notion, and one that is becoming more commonplace.  Content isn’t always necessary if clear, unambiguous data is available that can answer the question.

Google, or any search engine, is just a channel — an important one for sure, but not the end-all and be-all.  Search engines locate information created by others, but unless they have rights to that information, they are limited in what they do with it. Yet the principles here can apply to other kinds of interactive apps, channels and platforms that let users get information instantly, without wading through articles or videos.  So is content now obsolete?

There is an important limitation to considering SEO search results as data.  Even though the SEO community refers to search metadata as “structured data”, the use of this term is highly misleading.  The values described by the metadata aren’t true data that can be counted.  They are values to display, or are links to other values.  The problem with structured data as currently practiced is that is doesn’t enforce how the values need to be described.  The structured data values are never validated, so computers can’t be sure if two prices appearing on two random websites are both quoting the same currency, even if both mention dollars.  SEO structured data rarely requires controlled vocabulary for text values, and most of its values doesn’t include or mandate data typing that computers would need to aggregate and compare different values.  Publishers are free to use most any kind of text value they like in many situations.   The reality of SEO structured data is less glamorous than its image: much of the information described by SEO structured data is display content for humans to read, rather than data for machines to transform.  The customers who scan Google’s search results are people, not machines.  People still need to evaluate the information, and decide its credibility and relevance.  The values aren’t precise and reliable enough for computers to make such judgements.

When an individual wants to know what time a shop closes, it’s a no brainer to provide exactly that information, and no more. The strongest cases for presenting data directly is when the user already knows exactly what they want to know, and they will understand the meaning and significance of the data shown.  These are the “known unknowns” (or “knowns but forgotten”) use cases.  Plenty of such cases exist.  But while the lure of instant gratification is strong, people aren’t always in a rush to get answers, and in many cases they shouldn’t be in a rush, because the question is bigger than a single answer can address.

The Limits of Data

Data in various circumstances can get in the way of what interests audiences.  At a time when the corporate world increasingly extols the virtues of data, it’s important to recognize when data can be useless, because it doesn’t answer questions that audiences have.  Publishers should identify when data is oversold, as always being what audiences want.  Unless data reflects audiences priorities, the data is junk as far as audiences are concerned.

Data can bring credibility to content, though has the potential to confuse and mislead as well.  Audiences can be blinded by data when it is hard to comprehend, or is too voluminous. Audiences need to be interested in the data for it to provide them with value.  Much of the initial enthusiasm for data journalism, the idea of writing stories based on the detailed analysis of facts and statistics, has receded.  Some stories have been of high quality, but many weren’t intrinsically interesting to large numbers of viewers.  Audiences didn’t necessarily see themselves in the minutiae, or feel compelled to interact with raw material being offered to them.  Data journalism stories are different from commercially oriented information, which have well defined use cases specifying how people will interact with data.  Data journalism can presume people will be interested in topics simply because public data on these topics is available.  However, this data may be collected for a different purpose, often for technical specialists.  Presenting it doesn’t transform it into something interesting to audiences.

The experience of data journalism shows that not all data is intrinsically interesting or useful to audiences.  But some technologists believe that making endless volumes of data available is intrinsically worthwhile, because machines have the power to unlock value from the data that can’t be anticipated.

The notion that “data is God” has fueled the development of the semantic web approach, which has subsequently been  rebranded as “linked data”.  The semantic web has promised many things, including giving audiences direct access to information without the extraneous baggage of content.  It even promised to make audiences irrelevant in many cases, by handing over data to machines to act on, so that audiences don’t even need to view that data.  In its extreme articulation, the semantic web/linked data vision considers content as irrelevant, and even audiences as irrelevant.

These ideas, while still alive and championed by their supporters, have largely failed to live up to expectations.  There are many reasons for this failure, but a key one has been that proponents of linked data have failed to articulate its value to publishers and audiences. The goal of linked data always seems to be to feed more data to the machine.  Linked data discussions get trapped in the mechanics of what’s best for machines (de-referencable URIs,  machine values that have no intrinsic meaning to humans), instead of what’s useful for people.

The emergence of (the structured data standard used in SEO) represents a step back from such machine-centric thinking, to accommodate at least some of the needs of human metadata creators by allowing text values. But still doesn’t offer much in the way of controlled vocabularies for values, which would be both machine-reliable and human-friendly.  It only offers a narrow list of specialized “enumerations”, some of which are not easy-to-read text values. has lots of potential, but its current capabilities get over-hyped by some in the SEO community.  Just as metadata should not be considered structured data, it is not really the semantic web either.  It’s unable to make inferences, which was a key promise of the semantic web.  Its limitations show why content remains important. Google’s answer to the problem of how to make structured data relevant to people was the rich snippet.  Rich snippets displayed in Google search results are essentially a vanity statement. Sometimes these snippets answer the question, but other times they simply tease the user with related information.  Publishers and audiences alike may enjoy seeing an extract of content in search results, and certainly rich snippets are a positive development in search. But displaying extracts of information does not represent an achievement of the power of data.  A list of answers supplied by rich snippets is far less definitive than a list of answers supplied by a conventional structured query database — an approach that has been around for over three decades.

The value of data comes from its capacity to aggregate, manipulate and compare information relating to many items.  Data can be impactful when arranged and processed in ways that change an audience’s perception and understanding of a topic. Genuine data provides values that can be counted and transformed, something that doesn’t support very robustly, as previously mentioned.  Google’s snippets, when parsing metadata values from articles, simply display fragments  from individual items of content.  A list of snippets doesn’t really federate information from multiple sources into a unified, consolidated answer.  If you ask Google what store sells the cheapest milk in your city, Google can’t directly answer that question, because that information is not available as data that can be compared.  Information retrieval (locating information) is not the same as data processing (consolidating information).

“What is the point of all that data? A large data set is a product like any other. It must be maintained and updated, given attention. What are we to make of it?”  Paul Ford in “Usable Data

But let’s assume that we do have solid data that machines can process without difficulty.  Can that data provide audiences with what they need?  Is content unnecessary when the data is machine quality?  Some evidence suggests that even the highest quality linked data isn’t sufficient to interest audiences.

The museum sector has been interested in linked data for many years.  Unlike most web publishers, they haven’t been guided by and Google.  They’ve been developing their own metadata standards.  Yet this project has had its problems.  The data lead of a well known art museum complained recently of the “fetishization of Linked Open Data (LOD)”.  Many museums approached data as something intrinsically valuable, without thinking through who would use the data, and why.  Museums reasoned that they have lots of great content (their collections) and that they needed to provide information about their collections online to everyone, so that linked data was the way to do that.  But the author notes: ‘“I can’t wait to see what people do with our data” is not a clear ROI.’  When data is considered as the goal, instead of as a means to a goal, then audiences get left out of the picture.  This situation is common to many linked data projects, where getting data into a linked data structure becomes an all consuming end, without anchoring the project in audience and business needs.  For linked data to be useful, it needs to address specific use cases for people relying on the data.

Much magical thinking about linked data involves two assumptions: that the data will answer burning questions audiences have, and these answers will be sufficient to make explanatory content unnecessary.  When combined, these assumptions become one: everything you could possibly want to know is now available as a knowledge graph.

The promise that data can answer any question is animating development of knowledge graphs and “intelligent assistants” by nearly every big tech company: Google, Bing, LinkedIn, Apple, Facebook, etc.  This latest wave of data enthusiasm again raises questions whether content is becoming less relevant.

Knowledge graphs are a special form of linked data.  Instead of the data living in many places, hosted by many different publishers, the data is instead consolidated into a single source curated by one firm, for example, Bing. A knowledge graph combines millions of facts about all kinds of things into a single data set. A knowledge graph creator generally relies on other publisher’s linked data. But it assumes responsibility for validating that data itself when incorporating the information in its knowledge graph.  In principle, the information is more reliable, both factually and technically.

Knowledge graphs work best for persistent data (the birth year of a celebrity) but less well for high velocity data that can change frequently (the humidity right now).   Knowledge graphs can be incredibly powerful.  They can allow people to find connections between pieces of data that might not seem related, but are.  Sometimes these connections are simply fun trivia (two famous people born in the same hospital on the same day). Other times these connections are significant as actionable information.  Because knowledge graphs hold so much potential, it is often difficult to know how they can be used effectively.   Many knowledge graph use cases relate to open ended exploration, instead of specific tasks that solve well defined user problems.   Few people can offer a succinct, universally relevant reply to the question: “What problem does a knowledge graph solve?” Most of the success I’ve seen for knowledge graphs has been in specialized vertical applications aimed at researchers, such as biomedical research or financial fraud investigations.  To be useful to general audiences, knowledge graphs require editorial decisions that queue up on-topic questions, and return information relevant to audience needs and interests.  Knowledge graphs are less useful when they simply provide a dump of information that’s related to a topic.

Knowledge graphs combine aspects of Wikipedia (the crowdsourcing of data) with aspects of a proprietary gatekeeping platform such as Facebook (the centralized control of access to and prioritization of information).  No one party can be expected to develop all the data needed in a knowledge graph, yet one party needs to own the graph to make it work consistently — something that doesn’t always happen with linked data.   The host of the knowledge graph enjoys a privileged position: others must supply data, but have no guarantee of what they receive in return.

Under this arrangement, suppliers of data to a knowledge graph can’t calculate their ROI. Publishers are back in the situation where they must take a leap of faith that they’ll benefit from their effort.  Publishers are asked to supply data to a service on the basis of a vague promise that the service will provide their customers with helpful answers.  Exactly how the service will use the data is often not transparent. Knowledge graphs don’t reveal what data gets used, and when.   Publisher also know their rivals are also supplying data to the same graph.  The faith-based approach to developing data, in hopes that it will be used, has a poor track record.

The context of data retrieved from a knowledge graph may not be clear.  Google, Siri, Cortana, or Alexa may provide an answer.  But on what basis do they make that judgment?  The need for context to understand the meaning of data leads us back to content.   What a fact means may not be self-evident. Even facts that seem straightforward can depend on qualified definitions.

“A dataset precise enough for one purpose may not be sufficiently precise for another. Data on the Web may be wrong, or wrong in some context—with or without intent.” Bernstein, Hendler & Noy

The interaction between content and data is becoming even more consequential as the tech industry promotes services incorporating artificial intelligence.  In his book Free Speech, Timothy Garton Ash shared his experience using WolfamAlpha, a semantic AI platform that competes with IBM Watson, and that boldly claims to make the “world’s knowledge computable.”  When Ash asked WolfamAlpha “How free should speech be?”, it replied: “WolframAlpha doesn’t understand your query.”   This kind of result is entirely expected, but it is worth exploring why something billed as being smart fails to understand.  Conversational interfaces, after all, are promising to answer our questions.  Data needs to exist for questions to get answers.  For data to operate independently of content, an answer must be expressible as data. But many answers can’t be reduced to one or two values.  Sometimes they involve many values.  Sometimes answers can’t be expressed as a data value at all. This actuality means that content will always be necessary for some answers.

Data as a Bridge to Content

Data and content have different temperaments.  The role of content is often to lead the audience to reveal what’s interesting.  The role of data is frequently to follow the audience as they indicate their interests. Content and data play complementary roles.  Each can be incomplete without the other.

Content, whether articles, video or audio, is typically linear.  Content is meant to be consumed in a prescribed order.   Stories have beginnings and ends, and procedures normally have fixed sequences of steps.  Hyperlinking content provides a partial solution to making a content experience less linear, when that is desired.  Linear experiences can be helpful when audiences need orientation, but they are constraining when such orientation isn’t necessary.

Data, to be seen, must first be selected. Publishers must select what data to highlight, or they must delegate that task to the audience. Data is non-linear: it can be approached in any order.  It can be highly interactive, providing audiences with the ability to navigate and explore the information in any order, and change the focus of the information.  With that freedom comes the possibility that audiences get lost, unable to identify information of value.  What data means is highly dependent on the audience’s previous understanding.  Data can be explained with other data, but even these explanations require prior  knowledge.

From an audience perspective, data plays various roles.  Sometimes data is an answer, and the end of a task.  Sometimes data is the start of a larger activity.  Data is sometimes a signal that a topic should be looked at more closely.  Few people decide to see a movie based on an average rating alone.  A high rating might prompt someone to read about the film.  Or the person may be already be interested in reading about the film, and consults the average rating simply to confirm their own expectation of whether they’d like it.  Data can be an entryway into a topic, and a point of comparison for audiences.

Writers can undervalue data because they want to start with the story they wish to tell, rather than the question or fact that prompts initial interest from the audience.   Audiences often begin exploration by seeking out a fact. But what that fact may be will be different according to each individual.  Content needs facts to be discovered.

Data evangelists can undervalue content because they focus on the simple use cases, and ignore the messier ones.  Data can answer questions only in some situations.  In an ideal world, a list of questions and answers get paired together as data. Just match the right data with the right question.  But audiences may find it difficult to articulate the right question, or they may not know what question to ask. Audiences may find they need to ask so many specific questions to develop a broad understanding.  They may find the process of asking questions exhausting.  Search engines and intelligent agents aren’t going to Socratically enlighten us about new or unfamiliar topics.  Content is needed.

Ultimately, whether data or content is most important depends on how much communication is needed to support the audience.  Data supplies answers, but doesn’t communicate ideas.  Content communicates ideas, but can fail to answer if it lacks specific details (data) that audiences expect.

No bold line divides data from content.  Even basic information, such as expressing how to do something, can be approached either episodically as content, or atomically as data.  Publishers can present the minimal facts necessary to perform a task (the must do’s), or they can provide a story about possibilities of tasks to do (the may do’s).  How should they make that decision?

In my experience, publishers rarely create two radically alternative versions of online information, a data-centric and content-centric version, and test these against each other to see which better meets audience needs.  Such an approach could help publishers understand what the balance between content and data needs to be.  It could help them understand how much communication is required, so the information they provide is never in the way of the audience’s goals.

— Michael Andrews

Designing Glanceable Content for Partial Attention

Captive audiences are the exception, not the norm.  Audiences only rarely offer their undivided attention. How to design content for audiences who are only half paying attention is a growing challenge.

Despite wide recognition that most folks have too much on their minds, content producers continue to hope they’ll get the full attention of audiences.  According to conventional advice, if content designers make content simple and relevant, the content will earn the attention of audiences.  The messy, everyday reality that audiences face interferes with their attention, no matter how well considered and crafted the content.  The stark truth is that attention cannot always be earned. It must be bargained with.

Mounting evidence indicates audiences have less attention to dedicate to consuming content actively. Even when they want content, they don’t necessarily want the content to dominate what they are doing.  People want content they can multitask with, content that supports them as they do things like chores, exercise, or driving — instead of being the focus of what they are doing.  The rising popularity of audio books and podcasts are indications of the desire for content that supports multitasking or continuous partial attention. Streaming content doesn’t require active interaction.

Another tactic to compensate for our fragmenting attention is to make content smaller.  Many publishers seek to turn content into bite sized chunks that don’t take long to read.  Audiences nibble bite sized content when they are focused on other things, such as a conversation or a physical activity such as walking or waiting, or when they are mentally preoccupied with matters that may not be directly related to the content.

Bite sized content appears as informational cards displayed on smartphones, on the screens of smart watches, or as the snippets of audio available on voiced controlled audio platforms.  Contrary to common belief, making content bite sized does not mean it demands little attention from audiences, even if the item itself requires little time to read, watch or hear.  Attention is not the same as time to read.  Attention is part of a broader concept of content engagement.

Transcending the Four Standard Categories of Content Engagement

Content typically falls into one of four categories of engagement, according to the attention and interaction they require:

  • Content that requires constant user interface (UI) interaction and full attention.  An Xbox game might fall in this category, where the game paces the player, requiring them to interact and give their full attention.
  • Content that doesn’t require much UI interaction, but requires full attention.  An involved murder mystery story, whether  delivered as a video, audio or text, would be an example.
  • Content that requires UI interaction, but only intermittent attention.  An example is a buzzing notification on a watch or smartphone.  You can’t avoid paying attention to the notification, because it’s in your face.
  • Content that requires no UI interaction, and only intermittent attention.  Glancing at a dull sports match on a TV in a sports bar might qualify.

But another category exists, where there is intermittent attention, and only limited UI interaction.  The content is not zombie-like, where no interaction happens, but neither is it reactive, where the machine is demanding interaction.  Instead, the content allows the user to pay attention electively, and interact electively.  This is glanceable content that offers some choice.

Finding the Threshold of Significance

Chunks of content should be significant to audiences. When audiences can give their full attention, a sequence of small content chunks can be woven into a narrative that provides detailed meaning.  But when audience attention is partial, a content chunk must stand alone to convey meaning.

In some cases, content designers know the context of the audience, and can design small items of content that matches that context.  Notifications, at their best, provide an example of small items of content that match the context of the user.  It is easy to get notifications wrong.  Notifications or alerts tend to poke the user.  They are often action focused, requesting or confirming an action.  Notifications either presume attention, or demand attention.  Notifications can be hectoring, where the machine sets the pace for humans, rather than letting humans set their own pace.

In contrast to notifications, glanceable content presumes that the audience will choose for themselves when they want to look, and for how long.  The content needs to tempt the user to look at it occasionally, without being a distraction.

What makes a small chunk of information significant to users, making it worth their while?  The chunk of content needs to be specific enough to satisfy the user’s curiosity, but not require a distracting amount of work to access. The content will fail if it doesn’t get this balance right.

Apple recognizes this conundrum in its guidelines for the Apple Watch. It advocates “glanceable” content with “lightweight interactions”.  Not all interaction guidelines of the Watch meet those goals, however. Because only a small amount of content is presented, designers compensate by asking users to interact with it to get exactly the bit they want.

Compact content can be complex as well (Images from Apple Watch guidelines.)

Google has tried to get around the distraction of interaction by requiring little of it from audiences. Google Now cards provide bite sized content, where information within smartphone apps gets surfaced to a main screen.  Google Now curates information through a combination of machine learning to anticipate likely user preferences (implicit feedback), and getting explicit user feedback on these preferences.   While very clever, this approach can’t easily be extended to other publishers who lack the technical resources and embedded positioning of Google.  Not all Google Now cards are easily glanceable, either.

Bite sized content can get busy.  Many layers of information can get crammed on a display, using hieroglyphic cues.  Such displays can vainly call attention to themselves, requiring both sustained gazing and mental interpretation.  They are compact, but not truly glanceable in the sense that someone can notice, at a glance, a key nugget of interest without effort.  Audio content runs less risk of this, since it is harder to compress content at a rate faster than the brain can process (though advertisers sneakily do such things).

Content for Multitasking

Multitasking is a myth: humans don’t really focus on several things simultaneously.  We slice our attention between different targets, switching back and forth between them rapidly.  But multitasking is a powerful metaphor for how people relate to content: they commonly process content from two more sources at the same time.  To be successful, glanceable content needs to respect what the audience wants to focus on.

It’s hard to multitask when everything demands your full attention, and your active interaction.   It’s hard watch a video or listen to audio if one needs to fiddle with screen settings, if one needs to tap and swipe incessantly to hunt down a piece of information, or if one feels rattled by surprise alerts that jump out at you.  All these annoyances are the product of interaction designers who expect and demand attention and interaction from the audience.  Interaction designers often fail to consider the broader content needs of the user.  They fail to think outside the UI in which they are boxed.

Before interaction design became largely associated with Javascript tricks, there was a movement to study how to best design “ambient displays” that provided supplemental, contextual information that was easily glanceable.  A well known example was the “Ambient Orb,” a small light that changed color according to how the stock market was doing (or, if you preferred, the status of the weather).  The product provided a primitive example of how to make content glanceable and non-distracting. But the product treated the user as a passive vessel who could express little choice in what content to follow, and consequently had little substance to keep them interested.  Multitasking wasn’t a challenge, but the secondary task wasn’t very meaningful either.  Ambient displays can come up short because they only focus on ensuring content doesn’t require much attention. They don’t provide any interaction.

A better example of content that is both glanceable and requires only light interaction comes from a digital content display for radio broadcasts known as Journaline.  I’ve only recently learned about this platform, even though it has nearly a decade of history behind it.  I want to spend time discussing concepts  related to Journaline because it provides a good example of how content can be used to support different scenarios of use.  A major use case for Journaline is to provide text-based content on the screens of radios (though the same technology can be applied to video as well).  Its premise is that the text content is secondary to the broadcast audio, and should not interfere with the primary attention of the audience, who may be washing dishes or waiting at a stop light.  Journaline provides guidelines for presenting content in conditions involving low attention.  I am less concerned with whether Journaline itself will become successful commercially than I am in the lessons it can provide content designers more generally.

Designing Elective Supplemental Content

Journaline is a full content schema with its own XML-based markup language. Developed by Germany’s Fraunhofer IIS, “Journaline® is a data application for the DAB and DRM digital radio systems that provides hierarchically structured textual information. The user can easily and immediately access the topics he is currently interested in.”  Alternatively, it has been described as “teletext for digital radio.”

Journaline offers straight text-based content, without distracting elements such as gestures, animations, switches, sliders, or activity ring displays.  The simplicity of its approach makes it a powerful example to learn from.  Because of its XML foundation, the schema can reuse existing content delivered from other sources such as RSS feeds.

Imagine listening to audio content such as a radio broadcast.  You may be doing other activities while listening.  The audio content itself might make reference to other things.  So you may have a need to access supplementary content either relating to the activity you are doing (such as getting traffic information), or supplemental content relating to the audio you are listening to (such as the name of the guest being interviewed).  Journaline provides such information, which can be accessed and seen simply.  Some models of BMWs in Germany have Journaline content displays embedded in their digital radios.

Journaline delivers supplemental baseline information that is changeable, and subject to updating.  It provides a feed of content, but allows some audience control over specific items of interest. The paradigm balances the amount of content offered, and the interaction required.  Sufficient new content is provided to make the display interesting enough to glance at occasionally when convenient to do so, while enough control is offered so that audiences can choose what to get updates on without requiring a distracting amount of interaction.

Journaline identifies five content types associated with informational updates:

  1. News
  2. Sports events and results
  3. Financial information and stock market values
  4. Airport departure and arrival times
  5. Games and lottery

Many of these types involve awareness information that is not so essential that people would actively seek it.  The information is useful when presented, but not worth investing special effort.  The airport info could provide useful pre-arrival information while driving, but won’t provide the depth of details available on a smartphone once one has arrived.

Supplemental information is often related to broadcast programs.  Journaline provides the following scenarios for program related information:

  • Show background information (with optional link to a website)
  • Direct phone link to participate in chat show
  • Song info and purchases, and podcast downloads
  • Captions (radio for the hearing impaired)

I’m not sure all these are necessarily compelling use cases in the context of listening to a broadcast, but they do illustrate the range of uses of supplemental content.  Sometimes the content has a call to action that is thematically tied the primary content, and thus does not compete with it.   But the actions presented in the supplemental content are always elective — the  primary content isn’t promoting the secondary content; rather, the secondary content offers deeper support for the primary content.  Elective actions include ones that cannot be anticipated or expected (such as a desire to call the station), and  options that allow the audience to defer tasks for later, such as the ability to order something to check out later.  When done properly, low-key options let the audience choose the right time to take action, instead of pestering them to act when it may be inconvenient.

Dynamic Information Architecture

Journaline is effective in how it manages information, a kind of dynamic information architecture.  Like traditional information architecture, it provides elements that give audiences choices to navigate and select.  But it is also dynamic, where the elements themselves change as new information is available.  It blurs what’s navigation and what’s content.  Items in a list can update continuously, and can be navigational links to more details about the item.

Journaline works on a feed model, but it doesn’t simply provide a chronological stream.  Instead, content items are pushed out when updates happen, and are stored and available to view when no updates are required.  What’s available to view will be the most up-to-date content about preselected topics.  These topics can be selected (pulled) either through navigation, or by following topics based on simple metadata components such as keywords or geotags (such as content related to things near me).

Journaline uses simple structures for content.  Items are broken into two parts: an initial summary and detail, which could be a headline followed by an explanatory statement. Summaries can be collected by topic.  This simple structuring allows for three basic layers of information: a consolidated view of several items, a general view of a specific item, and a more detailed statement about the item.

The below figure shows how different levels of information relate to each other.

Levels of Glanceable Content
Levels of Glanceable Content

There are three basic levels of content, and three options to reveal content.  Conceptually, they provide different windows for glancing at content.

The highest level provides an overview or digest.  Screens may contain several headlines that relate to a common keyword, or may provide a table of information on a common theme that is updated continually, such as a scoreboard showing teams and scores.

At the mid level, the screen will show a summary of information related to an event or topic. This will typically be a headline, or a caption relating to the audio or to a displayed image.

At the lowest level, items will present details supporting the headline.

Content is revealed in different ways.  Some content is static and fully visible at a glance.  Such content will never require more than a single glance.  But other content is disclosed incrementally over time.

Overview or digest content will refresh frequently. Each headline displayed may serve a predefined role.  A digest on a topic may be composed of different headlines that are presented in a list, where each item is assigned a slot.  A slot— a fixed position on the list —  shows a headline message that changes.  For example, people might track several related items, perhaps headlines relating to an election.  The first slot in the list relates to the keyword “candidate X” and will change throughout the day according to whatever the most important event is relating to candidate X.  A scorecard is a list in table form, where the left column is fixed with the names of teams currently playing, while the right side automatically updates with the most current score.  When the content item refreshes in a predefined spot, it makes it easy for the audience to glance at the content and notice what’s current and new.

Scorecard table, with scores updated continuously
Scorecard table, with scores updated continuously

A different behavior for refreshing content occurs when messages are revealed in stages.  Perhaps several ideas need to be shown that won’t all fit in a single short headline.  Several techniques can be used to reveal such content.  One technique is the news ticker, which displays a running headline.  Text captions for audio may be presented karaoke style.  Another technique is to rotate short headline snippets every few seconds.  These techniques can be useful for background content.  They can be digested with a quick glance, and will change enough to encourage curiosity.  If, however, they contain essential information that must be monitored continually, then they will become distracting.  The staged revelation of content provides a wide frame for audiences to follow, but can demand more attention from them.

Sometimes the audience will want more information than can be presented in a glance.  They want a detail to complete the information hinted at in the headline.  A full message might fit on a single small screen, but if not, then scrolling is required.  Even gesture-based scrolling entails interaction by the user, and requires longer attention spans as well.  This option exists to provide audiences with choice.  They choose this degree of distraction, and will hopefully be in a position to focus away from other activities and content.

Short-burst Content: A Challenge for Content Designers

Audiences routinely experience short bursts of content.  Events are chronicled with live tweets.  Siri and Alexa provide short answers to queries.  All these options offer great benefits to audiences, but they aren’t always the best option in a given situation.  Sometimes short messages require too much attention to follow, or too much work to specify what one wants.

According to the cliché, timing is everything.  When audiences face so many demands on their attention, content timing matters significantly.  Content designers should consider ways content can work in harmony with the activities of audiences, instead of competing with these activities.  Disruption, while cool for startups, is annoying to audiences.  Supplemental content can be a new category of content that supports other activities, instead of being its own activity.  To realize this possibility requires the embracing a radical idea: accepting that the secondary content presented may not be the primary motivation of your audience.  Because audiences in many circumstance are only willing and able to offer partial attention, the secondary content needs to be sufficiently interesting and timely to merit glancing.

The content design challenge is discovering how to get audiences to glance at content that they don’t feel they need to see, but will want to view, if only briefly.  Content that’s useful, without being demanding.  How can we create glanceable moments?

Journaline offers two ideas worth exploring more.  First, it shows the value of creating a hub for content that features topics that change, and that are of interest to audiences.  People will revisit the hub to glance at what changes have happened.

Second, it shows the possibilities of using a secondary channel (in this case text) to augment the primary content channel (audio) to support elective, secondary tasks.  Audiences are primarily interested in the content in the primary channel, and only some will be interested in the secondary channel content at any given time, since they may be preoccupied with other matters.  This technique can easily be used with video, by providing additional real-time or more personalized secondary information relating to the video content that some people will be interested in glancing at, and which is short enough not to distract long from the primary video content.

Designing for partial attention can seem like a loss of control, since there are no guarantees a specific message will reach a specific person.  But it also represents an opportunity to reach audiences who may not be otherwise available.  And it encourages us to think about the fundamentals of how to attract audiences and build their interest in content.

— Michael Andrews