Tag Archives: domain model

Molecular Content and the Separation of Concerns

Our current ways of writing and producing web content seem ill-prepared for the needs of the future.  Content producers focus on planning articles or web pages, but existing approaches aren’t sufficiently scalable or flexible.  Web publishers need to produce growing quantities of increasingly specific content.  High volume content still requires too much human effort: the tedious crafting of generic text, and/or complicated planning that often provides inadequate flexibility.    Content producers, using tools designed for creating articles, lack a viable strategy for creating content that machines can use in new contexts, such as voice interfaces.  Before audiences see the content, machines need to act on it.  It’s time to consider the machine as a key audience segment, instead of as a incidental party.

Within the content strategy community, a discussion is starting about making content “molecular”.  Content molecules are  fundamental building blocks that can be combined and transformed in various ways.  The concept still lacks a precise definition.  But it seems  a compelling metaphor for thinking about content, given the diverse directions content is being pulled.   Molecules connect together, like Tinkertoys™.  Forces (business, technical, and consumer preferences) are pulling content in different directions.  How to connect molecules of content together is a pressing issue.  The metaphor of molecular content offers a chance to reimagine how content is created, and how it can serve future needs.

“A molecule of content is the smallest stable autonomous part of content, with a unique purpose. Molecules, of varying purpose, can be built into stable compounds of content in order to form meaning and provide a purpose.” — Andy McDonald and Toni Byrd-Ressaire

Content needs to serve many functions.  It must provide coherent narratives as it always has done.  But it is also needed in short bursts as well.  Content will need be interactive: responding to user requests, anticipating needs, updating in real time as circumstances change.  Molecular content is fundamentally different from previous ideas of “modular” content.  Modular content are static standalone chunks of words.  Molecular content is data-aware: responsive, interactive and updatable.  Molecular content is connected to logic, and gets involved in the context it is used.

In the future we can expect content will need to be:

  • Able to be speak and listen
  • Capable of animation to show a change in state
  • Able to communicate with machine sensors
  • Capable of changing its tone of voice according to the user’s state of attention.

The notion of shape shifting words seems fantastic — especially to authors accustomed to controlling each word as it appears in a text.  But words are really just a special form of data — meaningful data.  Computers can manipulate data in all manner of ways.

Content for human communication can be more complex than common computer data.  Existing data practices will contribute to the foundations of molecular content.  But they will need to be extended and enhanced to support the unique needs of words and writing.

Writers don’t like considering words as data.  They raise two objections.  First, they consider words as more nuanced  than data.  Second, they worry that the process of writing will resemble programming.  Their concerns are valid.  No progress will happen if solutions are too rigid, or too complex.  At the same time, writers need to prepare for the possibility that the process of web writing will fundamentally change.

Four Hidden Activities in Writing

Writing is a tacit process, rarely subject to analytic scrutiny.    We’re aware we form sentences involving subject, verbs and objects.  These get joined together into paragraphs, and into articles.  But the process can be so iterative that we don’t notice separate steps. When writers talk about process, they generally refer to rituals rather than workflows.

If we break down the writing process, we see different activities:

  1. Making statements (typically sentences)
  2. Choosing the subjects to write about in statements.
  3. Organizing these statements into a flow.
  4. Making judgments through implicit references.

Implicit references are the stealthiest.  In our speech and writing, humans summarize thoughts.  We may make implicit statements, or render an explicit judgment that saves us from having to list everything (fro example, saying “the best…”, or “good…” ).  People who work with data talk about enumeration — basically, creating a definitive list of every value (e.g, the seven days of the week.)  When we talk, we assume shared knowledge rather than repeat it.  We assume others don’t want a complete list of every city in a country, they just want to know the largest cities.

If anything, technology has made the writing practice blur together these activities even more.  When we can rewrite on the screen, we can cut and replace with abandon.  The stringing together of words becomes unconscious.  Any attempt to make the process more explicit, and managed, can feel limiting, and is often met with resistance.

One of the big unanswered questions about molecular content is how to write it.  Molecular content will likely require a new way of creating content. First, we need to examine the process of web writing.

The Three Writing Workflows

Any web writing process needs to address several questions:

  • What things (proper nouns or entities) do you want to discuss?
  • What statements do you want to make about these things?
  • How to structure these statements (what template to use)?

These questions can be addressed in three different sequences or workflows:

  1. Author-driven
  2. Template-driven
  3. Domain-driven.

The author-driven approach treats writing as a craft, rather than as a process. The sequence starts with a blank page. The author writes a series of statements.  He structures these statements.  Finally, he may later tag things mentioned in the text to identify entities.

The author-driven sequence is:

  1. Write statements
  2. Structure text
  3. Tag text

The template-driven sequence starts with a template.  This approach is gaining popularity, with products like GatherContent providing templated forms that authors can fill-in.  The structure is pre-determined.  It is not unlike filling in an online job application.  The author needs to add text inside boxes on a form.  Later, the text can be tagged to identify entities mentioned.

The template-driven sequence is:

  1. Choose template or structure
  2. Write statements
  3. Tag Text.

The template-driven approach can sometimes allow the reuse of some blocks of text.  But often, the goal of templates is to organize content and facilitate the inputting of text.  A common organization can provide consistency for  audiences viewing different content.  But such structure doesn’t itself reduce the amount of writing required if the content has a single-use.

The domain-driven sequence starts by choosing what entities (people, places or things) the author plans to discuss.  Entities are the key variables in content.  They are what people searching for content are most likely to be seeking. Shouldn’t we know what entities we will be talking about before we start writing about them?  Once the entities are chosen, authors write statements about them.  They consider what can be said about them.  Writers can organize these statements by associating them with containers that provide structure.  Unlike the other approaches, authors don’t need to worry about tagging, because the entities are already tagged.

The domain-driven sequence is:

  1. Choose entities (pre-tagged)
  2. Write statements
  3. Choose containers for structure.

Domain-driven content writes content around entities. In other approaches, identifying what entity is mentioned is an afterthought, during tagging.  Because it is entity focused, domain-driven content is well matched to the needs molecular content.

Molecular Content through Domain-driven Writing

Domain-driven content is not new, but it is still not widely known.  I wrote about content and domains as they relate to Italian wine several years ago when I was living in Italy.  Happily, a new book has just come out by Carrie Hane and Mike  Atherton called Designing Connected Content talks about domain-driven content in detail.   It’s an excellent place to start to learn how to think about planning content from a domain perspective.  For writers wanting to understand how domains can influence writing, I recommend the blog by Teodora Petkova, who has been writing about this topic extensively.

Domain-driven writing may seem hard to envision.  How will one choose entities to write about before writing?  Perhaps writers could tap to select available entities, much like they tap to select available airline seats.  The tool could be connected to an open source knowledge graph that describes entities; a growing number of knowledge graphs are available.

The entities selected could be at the top of a screen, reminding the writer about what they should be writing about.  It could help writers remember to include details, or explore connections between topics they might not think to explore.  A tool could even helpfully offer a list of synonyms for entities, so that writers know what vocabulary they can draw on to discuss a subject.  Maybe it could even recommend some existing generic text about related entities, and the author could decide if that text is appropriate for discussing the entities they have chosen.

Domain-driven writing is a good fit for factually rich content.  It’s not an approach to use to write the next great novel.  Novelists will stick with the author-driven, blank page approach.

Much web writing is repetitive.  The details change, but the body of the text is the same. Domain-driven content puts the focus on those details.  It isolates the variables that change, from those that are constant.  In bring attention to the context that variables appear.

Even when the text of statements changes, domain-driven content allows factual data to be reused in different content.  If a product is mentioned in various content, the price can always appear beside the product name, no matter where the product name appears.

Two approaches to domain-driven writing are available, which can be used in combination.  The first approach creates reusable statements that are applicable to many different entities. The second approach allows custom statements for specific entities.

The first approach aims to standardize recurring patterns of writing by:

  1. Decomposing existing writing into common segments or chunks
  2. Normalizing or standardizing the text of these segments
  3. Reusing text of segments.

Instead of writing to make the text original-sounding, writers focus on how to make the text simpler by reducing the variation of expression.  The emphasis is on comprehension.  It is not about boosting attention by aiming for originality or the unexpected.

Let’s imagine you have a website about caring for your dog.  You have content about different topics, such as grooming your dog, or training your dog.  Dogs come in different breeds.  Does the breed of dog change anything you say about training advice?  Do you need to customize a paragraph about training requirements for specific breeds?  The website might have a mix of generic content that applies to all breeds, with some custom content relating to a specific breed.  The delta in the content helps to flush out when a specific breed is a special case in some way.

The second approach is start with entities — the key details your content addresses.  Instead of thinking about grand themes and then the details, you can reverse the sequence.  In our dog website example, we start with an collection of entities related to dogs.  This is the dog domain.  What might you want to say about the dog domain?  A tool could help you explore different angles.  You might choose a breed of dog to start with, perhaps a poodle.  The tool could show you all kinds of concepts connected to a poodle.  These concepts might be start of statements you’d want to make.  The tool would resemble a super-helpful thesaurus.  It would highlight different connections.  You could see other breeds of dog that are either similar or dissimilar.  Seeing those entities might prompt you to write some statements comparing different breeds of dog.  You might see concepts connected with dogs, such as traveling with dogs.  You could even drill down into sub-concepts, such as air travel or car travel.  The experience of traveling with a dog by air is different according to breed: for example, a Dachshund verses a Saint Bernard.  If you need to write statements to support specific tasks, the domain can help you identify related people, organizations, things, events, and locations — all the entities that are involved in the domain.

Domain-driven content is scalable.  You can start with statements about specific things, and then consider how you can generalize these statements.

Slide from a presentation by Rob Gillespie

Molecular Content: How Content Gets Liberated

Molecular content needs to be highly flexible.  To deliver such flexibility, different components need to play well with the rest of the world. One can’t overstate the diversity that exists currently in the web world.  Millions of individuals and organizations are trying to do various things, developing new solutions.   Diversity is increasing.  New platforms, new syntaxes, new channels, new programming languages, new architectures must all be accommodated.

We need to let go of the hope that one tool can do everything.  Tightly-coupled systems are seductive because they seem to offer everything you need in one place. Tightly-coupled systems give rise to authoring-development hybrid tools such as Dreamweaver or FontoXML.  Content, structure and logic all live together in a single source, which seems convenient.  But your flexibility will be limited by what those tools allow you to do.

The ideal of single sourcing of content is becoming less and less viable.  Requirements are becoming too elaborate and varied to expect a monolithic collection of files following a unified architecture to address all needs.  A single model for publishing web content can’t cope with everything being thrown at it.  Models are brittle.  We need systems where different functions are handled in different ways, depending on shifting circumstances and diverse preferences.   When you use a single model, others will reject what’s good about an approach because they hate what’s limiting about it.

Web publishing is becoming more decoupled.  Headless CMSs separate the authoring environment from content management and delivery.  Content management systems offer APIs that allow unbundled delivery of content.  Even the authoring process is getting unbundled, with new tools that specialize in distributed input, collaborative editing and offline workflows.

While the trend toward decoupling is gaining momentum, most attempts are limited in scope.  The don’t fundamentally change how content is created, or how it can be available.  They rely on the current writing paradigm, which is still document focused.  No one yet has developed solutions that make content truly molecular.

Molecular content will require a radical decoupling of systems that process content.  The only way to create content that is genuinely future-ready is to remove dependencies that require others to adopt legacy approaches and conventions.  Systems need to be adaptable, where parties involved in producing web content can swap out different sub-processes as new needs and better approaches emerge.

The Backend of Molecular Content: Separation of Concerns

It is challenging to talk about a concept as novel as molecular content without addressing how it would work.  I want to introduce a concept followed by developers called “the separation of concerns” and discuss how it is relevant to content.

Suppose a developer wanted to code a heading that said “Everyone is talking about John.”  In old-school HTML, developers would hard-code content, structure, and in-line logic together in a single HTML file.  Here’s what single source content might look like:

<html>

<body>

<h1>Everyone is talking about<div id="person"></div>.</h1>

<script>

document.getElementById("person").innerHTML = "John";

</script>

</body>

</html>

The file is hard to read, because everything is smushed together.  In a single file, we have content, structure, a variable, and a script.  It may sound efficient to have all that description in one place, until you realize you can’t reuse any of these elements.  It is brittle.

In modern practice, webpages are built from different elements: content files, templates, and separate scripts providing common logic.  Even metadata can be injected into a webpage from an outside file.  This decoupling allows many-to-many relationships.  One webpage may call many scripts, and one script may serve many webpages.  This is an example of the separation of concerns.

To separate concerns means that code is organized according to its purpose.  It is easier to maintain and reuse code when common things with similar roles are grouped together.

Let’s consider the different concerns or dimensions of how content is assembled.  The dimensions that computer systems consider to assemble content is in many respects similar to how authors assemble content.  They are:

  • Content variables
  • Narrative statements
  • Containers for content
  • Logic relating to content

These different concerns can be managed separately.

Variables

Variables are the energy in the content.  Because they vary, they are interesting.  Humans are hardwired to notice stuff that changes.

Variables live within statements.  Suppose wish aloud to our companion, Google, on our smartphone.  We say: “Ok, Google.  Get me a flight between Paris and Hong Kong for less than $500 in the first week of March.”  We have numerous variables in that one statement.  We have destinations (Paris and Hong Kong), price ($500) and time (first week of March).  Which of those is negotiable?  When we think about variables as being subject to negotiation, we can see how statements might change.

Variables animate statements the way atoms animate molecules, to use a metaphor.

Variables are frequently proper nouns or entities, which are visible in the content.   Such variables are descriptive metadata about the content.  A price mentioned in a statement is an example.

Some variables are not visible.  They are background information that won’t show up in a statement, but will be used to choose statements.  Such variables are often administrative metadata about content.  For example, to know if a statement is new or old, we could access a “published on” date variable.

What makes variables powerful is that they can be associated with each other.  These are not random words like the ones  used by a random phrase generator.  Variables follow patterns, and form associations.

For example, if we wanted to describe a person, we start by thinking about the variables associated with a person.

Person :

  Name: John,

  Gender: Male,

  Profession: Painter

A different person will have same variables, but with different variable values.  We can keep adding variables that might be useful.  This is the factual raw material that can be used in our content.

An important point about variables is that they can be represented different ways.  Because we want to separate concerns as much as possible, the variable lives separately from statements, instead of being embedded in them.  Because variables are separate, they can be transformed to serve different needs.  We don’t worry about what syntax is used.  It could be JSON, or YAML or Turtle.  When variables exist separately, their syntax can be easily converted.  We also don’t worry about what schema is used.  We can use different schemas, and note the equivalences between how different schemas refer to a variable.  We can reassign the name of a variable if required.  Maybe we want to refer to a person’s job instead of a person’s profession.  Not a problem.

Statements

Statements are generally text, though they could be an audio or video clip, or even an SVG graphic.  I’ll stick to text, since it is most familiar and easiest to discuss.

Statements will often be complete sentences, perhaps several related sentences.  But they could be shorter phrases such as a slogan or the line of a song.  Statements can be added to other statements.  Each line of a song can be joined together to produce a statement conveying the song’s full lyrics.

Statements become powerful when used in multiple places. Statements can accommodate visible variables to produce statement variations.  Some statements won’t use variables, and will be the same wherever they appear.

Statements are the basic molecules of content.  Some statements will be short, and some will be long.    The length depends on how consistent the information is.  We can use variables to produce statement variations, but the statements themselves stay consistent.  When we need need to talk about certain variables only in some situations, then new statements are needed.

Let’s look at how statements can incorporate variables.  We will use the person variables from our previous example.

Statement_1 : “Everyone is talking about {Person.Name}, the popular {Person.Profession}.”

Statement_2: “{Person.Name}’s Big Moment”

These are two alternatively worded statements that could be made about the same person.  Maybe we want to use them in different contexts.  Or we want to test which is more popular.  Or maybe they will be both used in the same article.  Because these statements are independent, they can be used in many ways.

I’ve used “pseudocode” to show how variables work within statements.  If we have many persons, we can be selective about which ones get mentioned.

But the syntax used to represent the text can follow any convention.  It could be plain text, or a subset of Markdown.  We are only interested in representing the information, not how it is structured or presented.  The information is independent of structure.  There’s no in-line markup.

Structure

Structure is how statements are arranged and represented.  Structure is one of the two ways that content molecules get “bonded” to other molecules (the other way is logic, to be discussed next).

Statements and structures have a many-to-many relationship.  That means the same statement can be used in many different structures, and a single structure can accommodate more than one statement.

A simple example (again using pseudocode) will show how statements get bonded into structures.  It is as simple as dropping the statement into a structural element.

/// Structure_1

<h1> {Statement_1}</h1>

/// But it could be instead

/// Structure_2

<h2> {Statement_1} </h2>

A single statement could be applied to many structures, including image captions or email headers.

As we consider a wider range of content, we can see how statements need to be used in different templates.  For example, the same transcript may need to appear as text of interview, and as subtitles of a video.

Structure should not be hard-coded into statements the way XML markup and CSS-selectors tend to do.  That limits the reuse of statements.

Molecular content should be independent of any specific structure, and able to adapt to various structures. We need structure flexibility.  Statements need to change structural roles.  We are accustomed to thinking about a statement having a fixed structural role.

Logic

Logic provides instructions about what content to get.  It may be a script (a few steps to do), a query (a command to find values of a certain kind), or a function (a reusable set of instructions).

Logic processes content to characterize it.  For example, if the content is about the “top” movies this week, the logic does a query to determine and display what the top-grossing films are.  Logic allows computers to make implicit statements, just like writers do, which makes the text sound more natural.

“With content molecules, content is separated not only from the presentation, but from the business logic, that is from the way the content is processed and manipulated.”  Alex Mayscheff

Logic is another way content molecules can bond together.  When logic is applied to statements, logic plays a matchmaking role.

Logic can also be applied to variables.  It can help to decide the right values to include in a statement.

A common example is when a query of a database generates a list.  The query asks the top 10 best selling literary fiction titles, and a statement is returned with 10 titles in a list.

Logic can provide more than simply reporting data.  As software gets smarter, it will be able to make more natural sounding statements.

Consider a simple example.  If we know the gender of a person, we can create new variables indicating the appropriate person pronoun and possessive pronoun to use.  Expressed in pseudocode, it might work like this:

Function(genderPronoun)

If Person.Gender == Male

  Assign

    PersonalPronoun -> He

    PossessivePronoun -> His

Else

  Assign

    PersonalPronoun -> She

    PossessivePronoun -> Her

Endif

Logic can summarize variables so they are easier for humans to comprehend.  If we only rely on variables, we have to see the values exactly has they are recorded.   In earlier examples, the variable was directly injected into a statement.  The variable says: When you get here, put a certain value here.

Using logic, a variable can call a function.  The function instructs: When you get here, figure out the appropriate value to put here.  This gives much more flexibility for the scope of values that can be used in statements.

Because the logic is separated from the variables and the statements, we don’t care what form of logic is used.  It might be PHP, Python or Javascript.  Or a query language such as SQL or Sparql.  Or some new AI algorithm.  Developers might combine different programming languages, so that different ones can perform specialized roles.  It is a very different situation than exists when content is encoded in XML, forcing developers to rely on XSLT or some other XML-focused language.

Systematizing What’s Routine

My excursion into the coding of molecular content may give a false impression that writers will need to code in the future.  I hope that is not the case.  Nearly everyone I know agrees that code is distracting when appearing in writing.  Ideally, the separation of concerns means that code won’t appear in statements.

What’s been missing are systems that make it easy for writers to reuse facts (variables), statements (content chunks), and templates (structures).  Systems should let writers add some logic to their writing without worrying about the programming behind it, perhaps by choosing some pre-made “recipes” that can be dragged into text and inserted.  I’ve seen enough different efforts to simplify systems (from Jekyll to IFTTT to automated suggestions) to believe writer-friendly tools to support molecular content are possible.  But new systems emerge only when a large community believes there is a better way.  No one person, or company, can build and sell a new system, much less force its adoption.

When I started out working in the web world, all user interface screens were individually designed.  Each one needed to be crafted and tested individually.  Each screen was a precious creation.  Eventually, the UX community realized that approach was madness.  UX folks weren’t able to keep up with the volume of screens that users needed to see.  And UX staff were recreating the same kinds of screens again and again.  Eventually, the UX community adopted components, patterns and templates.  They created systems that could scale.  Original, new designs are needed only in highly novel situations, such as new device platforms or enabling interaction technology.  The rest can be reused and repeated.

Atom Design methodology by Brad Frost

UX designers now talk about a concept called atomic design.  Atomic design sounds related to molecular content.

The transformation of UX design is still ongoing, but it’s impressive in what has been achieved already.  One might expect designers would be resistant to technology.  Many studied graphic design in art schools, using colored markers.  When applying their graphics knowledge to web design, they saw the benefits of reusable CSS, adopted plug-and-play Javascript frameworks, and started building component libraries. Much of the progress was the work of various individuals trying to solve common problems.  Only recently have companies started marketing complete solutions for UI component management.  Designers still like to sketch, but they don’t expect screen design to be a manual craft.

I’ve long been puzzled why so many art school grads can happily embrace technology, while so many writers have an anti-technology attitude.  Designers have found how technology can extend their productivity immensely. I hope writers will discover the same.  A craft approach to writing is wonderful for novels, but insane for producing corporate web content.

Most of the original structured writing approaches built in XML are tightly-coupled, resulting in systems that are both inflexible and overly complex.  A more loosely-coupled system, based on a separation of concerns, promises to be more flexible, and can be less complex, since adopters can choose the capabilities they need and are willing to learn.  Designers have benefitted from open systems, such as CSS patterns, Javascript frameworks, and other publicly available, reusable components. Designers can choose what technology they want to use, often having more than one option. Writers need open systems to support their work as well.

—  Michael Andrews

Wine, Content, and Domain Models

Suppose your organization wants to become the preeminent source of information about a topic. It aims to give audiences the ability to look at any dimension of a topic they might be interested in. How would you offer this?

To deliver informationally rich content, numerous content items need to be associated to one another. Content needs to be modular, with components that work together. But how do these things relate to each other? Where does one start?

Content models define how units of content should interact. Content modelling can be difficult to grasp and practice, partly because it is not a single uniform method. It encompasses a spectrum of related approaches that can be adapted to different needs.

People sometimes start to model their content before they know all the content they really need. They focus on what content has been already created, and not explore what content is not yet available that might be of interests to users.

Content models are often more robust when they are backed by a domain model. A domain model enables content designers to untangle a messy topic and explore and define requirements and design solutions.

The role of content modelling

A content model is the end goal of a domain model. Rachel Lovinger has been instrumental in developing and advocating the practice of content modelling, so I will rely on her definition. She states: “A content model documents all the different types of content you will have for a given project. It contains detailed definitions of each content type’s elements and their relationships to each other.” She recommends using content models to bridge perspectives on a team.

“A content model helps clarify requirements and encourages collaboration between the designers, the developers creating the CMS, and the content creators.” — Rachel Lovinger

In addition to facilitating project delivery, content models improve how content is delivered to audiences. Content models can enable personalization, adaptive content, and content APIs. Cleve Gibbon, a collaborator of Rachel Lovinger in evangelizing content models, notes: “Great APIs are founded upon solid models. So if you’re building a Content API, be sure to create a content model FIRST that conveys the required level of structure and meaning.”

The spectrum of content modelling

Models can represent different dimensions of a topic: either conceptual, or formal and structural. Content models can indicate how to assemble content components. But first one needs to know how solidly your content types are defined.

On one end of the spectrum, you may have well defined, fixed content. In such cases, one can develop what Deane Barker calls a relational content model. He defines it as “the concept of how different, separately-managed pieces of content relate to each other.  (This is distinct from ‘discrete content modeling,’ which is how you structure a single piece of content.” He explains the goal as “the idea of taking multiple discrete content objects (articles, sections, issues) and ‘rolling them up’ into a more complex content object (publication).”

On the other end of the spectrum, you may have fluid content, where the exact requirements are still emerging and many different hubs of content are possible. In such cases, a domain focused, ontology based form of modelling can be helpful. This approach has been used by the BBC for several large projects. Mike Atherton emphasizes the importance of the domain of the topic in content models: “A content model maps our subject domain, not our website structure.” He advises: “Concentrate on modelling real (physical and metaphysical) things not web pages.”

One way to consider the differences in a content model and a domain model is the metadata they emphasize. Rachel Lovinger states: “The Content Model is primarily concerned with structural metadata, while the Domain Model is largely concerned with descriptive metadata.”

A domain model and content model are complementary. A domain model helps you describe things that will be represented by content, while the content model helps you structure the content. Using both allows you to understand the relationship of a real world entity with a content entity.

A domain model is a useful place to start when content does not yet exist, or one is looking for a fresh redesign of content. Domain models may be considered as the prequel to content models. By focusing on entities in the real world, and the relationships between these entities, one can see opportunities to develop content associated with these entities, and what elements would be needed for that content. The correspondence of domain entity type, and content type, is illustrated in the table.

The relationship between a domain model and content model
The relationship between a domain model and content model

Domain models in the real world: Italian wine

Domain models can clarify one’s understanding of a topic, and offer insights into how different items of information relate to each other. Domain modelling emerged as strategy in software development to bridge analysis and design of complex business domains by using a shared verbal and visual language between experts, endusers and developers. Domain models can be especially useful for complicated and messy topics. They would seem perfect for understanding Italian wine.

When you live in Italy, as I do, understanding Italian wine is a practical problem. Wine is ubiquitous, but understanding Italian wine is not self-evident. Walk into an Italian wine store and you are confronted with walls of bottles whose contents are largely unrecognizable. It’s not that all wine is difficult to understand. When I lived in New Zealand, I had a good idea what different wines were about. It’s Italian wine that is the challenge.

The famous wine critic Hugh Johnson once wrote: “the already bewildering complexity of Italian wines has become tangled enough to drive a critic to drink.” Italian wine is particularly hard to understand because of its heterogeneity. Even the imposition of standardized nomenclature to designate where a wine is from results in a bewildering array of non-standard implementations of these standards. Idiosyncratic traditions, politics, and rogue approaches mean that wines are described in great detail, but in richly differing ways.

At the core of why Italian wine is difficult to decipher is its product architecture: how specific wines are labelled. Consumers need an easy way to know the basic characteristics of a wine based on its label. Do consumers think of wine in terms of where it’s from (Burgundy) or what grapes it is made from (Chardonnay)?[1]  High volume wine producers have attempted to solve the product architecture problem by promoting brand awareness of a grape variety or a region. What happens when consumers are not familiar with either the origin name or the grape name?

Unfortunately, Italian wine labels are uncharacteristically difficult to decipher. Italian labels will show the producer + (grape variety and/or geographic indication) + year. That seems reasonable enough, until the consumer realizes that the only items on the label they might recognize are the digits of the year. Even if they have familiarity with another proper name on the label, that is not sufficient to make a selection decision.

The most significant piece of information about the kind of wine is indicated by the grape variety and/or the geographic indication (a regional designation similar to an appellation in France). Between these two items, there are nearly 1000 different varietals and zones that indicate the basic composition of the wine. [2]  To get a sense of how good the wine is, the most reliable information is the producer, and the year of vintage. Yet there are many thousands of wine producers in Italy of varying abilities, and the correlation of product quality to year of vintage is very specific to the variety of wine and where it was produced.

The complexity of Italian wine would seem tailored for digital content. But existing digital-only information sources on the web tend to be shallow — both in terms of their range of attributes, and their selective coverage.

Good information about wines, producers, and regions are available from several well known printed guides, such as those by L’Espresso, Gambero Rosso, Touring Club, Bibenda, and Slow Food. Despite the editorial quality of the content, the information is not as usable as it could be. Depending on the specific organization of the book, the information is stovepiped in one way or another. The editors of each guide assumes a fixed path of entry that generally leads to a producer profile. Users are expected to think like the editors to uncover information of interest to them.

In some cases there are iPad versions of these printed guides, but they don’t feel natively digital, and require lots of tapping to move around from screen to screen. They are less usable than the print version, because they are slower to move through, and one’s orientation can get lost when hopping between screens. The content, while structured editorially, is not structured digitally with digital metadata. There is no ability to move laterally through the content: navigation is hierarchical. Unfortunately shovelware that ports a printed product and dumps it into a tablet format is too common, due to the false promises embedded in Adobe InDesign.

What users need is not simply a catalog of items, but a way to make sense of the bigger picture, in addition to exploring the detail. The heavy focus on profiles means that the user doesn’t see easily how these items relate to other things. They also miss seeing collective behaviors of similar items, which is possible when one digitally aggregates items sharing the same metadata. Thinking through these relationships and behaviors is one benefit of domain modelling.

Understanding the domain

How do people think about a subject? Mike Atherton suggests: “Experts map the world, users mark points of interest.” It helps to know how experts think about a topic like wine, and then during design, figure out what more typical users consider high priority goals. What aspects of wine do people consider significant? How might different aspects be pulled together into interesting items of content?

The topic of wine is distinctive because many people want to become experts, in contrast to other products. Getting information about the product is rarely a perfunctory task, but a connoisseurial pastime. Some people want to develop a broad knowledge about all styles of wine, while other people want to have a deep knowledge about a few specific producers or product areas, perhaps tied to places they go on holiday. Many things people might be interested in are non-obvious. For example, soil characteristics can influence how a grape variety tastes. Others may be interested in the environmental credentials of a producer.

How to break things down so they can be managed

The most important task when developing a domain model is to identify appropriate entities. An entity is a thing, either tangible or conceptual, with a distinct identity. It’s not the same as an existing item of content — the content may not exist yet. Entities, to use the words of Cleve Gibbon, are “first class citizens in the business domain” — they are the actors in the drama on the stage.

Entities have attributes — characteristics. Attributes do not necessarily become a field in the content, but they often do. That decision needs to be made when the content is designed. Taste is certainly an attribute of wine, but is not necessarily a field in a description of a wine.

Once entities have been identified, it is necessary to determine where to put attributes, and whether to break entities into smaller units. Often, one discovers intermediate zones that straddle two entities. The horticultural characteristics of the vineyard reflect the interaction of the producer and the wine produced. The interplay between region and varietal defines the vintage for a given year. These intermediate areas may not deserve to be entities themselves, but one should consider how to make sure their role remains visible.

What a domain model for Italian wine looks like

It is helpful to first consider the relationships between entities, then examine the attributes associated with each entity.

When looking at entities, two things are important. First, how many instances are there for each entity type? The entity map shows that most of the entities, there are hundreds or even thousands of instances. This large number suggests that establishing meaningful relationships between entities will be important if users are to be successful navigating through such a large volume of content. Second, what is be essential character of relationships between entities? We want to know how many connections there are between entities: the more connections to other entities, the richer the potential interaction of information. We also want to know if the relationship between entities is a one-to-one relationship, a one-to-many relationship, or a many-to-many relationship. The “crow’s feet” in our entity map indicates numerous many-to-many relationships. That may make the design of content a bit more challenging, but it also indicates many interesting connections. Our content is a valuable resource when it’s not easy to see these connections in one’s head.

Relationships between different entity types associated with the domain of Italian wine.
Relationships between different entity types associated with the domain of Italian wine.

Next, explore the attributes associated with each entity. The goal is to identify and associate attributes of entities. Each entity has a number of attributes. Some will be short fields, others will involve longer text descriptions. There is no right number of attributes, provided all attributes are meaningful. The number of attributes to implement in design will depend on both business and design decisions. There will be a business decision concerning the cost of acquiring the information related to the attribute, and the usefulness to consumers of that information. There will also be a design decision relating to which attributes to expose to which audiences.

Typical attributes of each entity type relating to domain of Italian wine
Typical attributes of each entity type relating to domain of Italian wine

Our model shows attributes that are commonly associated with the domain of Italian wine. For example, it can be interesting to know the number of bottles produced of a wine. That can indicate how widely available the wine is to buy, or perhaps its scarcity (that one needs to reserve purchase). Some wine guides will indicate the total number of bottles according to producer, while others will indicate total number of bottles by label. This difference means that one can answer different questions, such as who is the largest producer within a geographic indication zone, or who is the largest producer of a specific kind of wine. Ideally, one would like data at both the producer and product levels, but that may not be easy to obtain for all producers.

Lessons from domain modelling

Even though domain modelling attempts to represent the real world, reality is often less orderly than we would like it to be.

Not everything can be easily expressed as a regularized attribute. Audiences will want to know: What does the wine taste like? It would be wonderful to provide a reliable, easy-to-understand way to explain taste that allows easy comparison between wines, zones, and producers. Sadly, taste is — surprise — a bit subjective. Different experts will say different things about the same wine, even when they agree on an overall judgment. Terminology is not standard either. The same words can mean different things. Critics may use the word “cherry” to describe a taste as “spicy black cherry” or as “cherry rhubarb.” There is no controlled vocabulary for wine, no limited set of descriptors with precisely defined and agreed meanings.

Example of geographic designation zones within a single region.  Screenshot from a certification body website.
Map of geographic designation zones within a single region. Screenshot from a certification body website.

By their nature, models simplify reality. The geographic indication signifies where a wine is made, and the criteria by which it is made. Whereas most geographical entities are based on either political administrative geography or physical geography, geographic indications exist outside these frameworks. A geographic indication can straddle two administrative regions. It can exist in two different, discontinuous locations. Some geographic indication zones have subzones. Wine producers also can behave in complex ways. Sometimes a wine producer is a brand “house” that has vineyards in several locations, or a consortium that sources from different vineyards. The informational details associated with these exceptions may not be important to users, and can add design complexity.

The identity of items can be constructed in several ways. One needs to be able to distinguish one entity from others belonging to the same entity type — items need to be uniquely identified. Despite the challenges of deciphering Italian wine, specific entities fortunately are identified with meaningful, human readable names, rather than numeric product codes. The domain model can use existing identifiers, which are based on several approaches:

  • Collectively defined names (the names of regions, geographic indications, and grape varieties), though some producers use alternate names for grape varieties.
  • Self described (the name of producer), though sometimes producers choose to use both a house and proprietor name
  • Inherited identity (the environmental profile for a producer)
  • Names composed of compound attributes , such as dry sparkling rosato as a wine category entity.

Thinking about design

The domain model can support early design discussions. Many questions that are interesting to audiences will span two or more different entities. For example:

  • What year produced the best wine from a region?
  • What geographic indication commands the highest average prices?
  • What grape varieties produce the most wine?
  • What wines for a given year and geographic designation are ready to drink?

Some answers require computations of structured data. Questions of interest to audiences need to be translated into content types that will be represented in the content model.

In addition to supporting interesting exploration, the design needs to support common tasks. The domain model helps to identify information available to support common tasks. Some common points of entry audiences will seek when exploring wine include:

  • By rating
  • By price
  • By category
  • By variety

Users often focus on one specific criteria when starting the process of seeking information. In some cases, these are entities, in others, these are attributes. Considering task starting points can help identify potential groupings of content elements. Depending on the depth of content, these groupings may not be manageable for users without providing additional parameters to narrow the pool of candidate content. The most salient criteria is not the only factor that’s important to the user.

In contrast to starting points, another perspective is to consider the end goal of the task. Examining the end goal, the content designer can consider the orientation of different users. Users of wine information may be:

  • Bottle centric — interested in the characteristics of specific bottles of wine
  • Producer centric — interested in the story of the producer, perhaps with an intention of visiting them
  • Food centric — mostly interested in wine styles as a complement to food dishes.

Domain depth and domain scope

The depth of a domain reflects both the number of attributes for an entity type, and quantity of items. Both aspects can impact the design. The quantity of items will influence content types that presents lists and links. The number of attributes will impact content type structures for content items.

Content designers decide how much of the domain model to present to users. A fixed content type may show all attributes as part of in content type. With a flexible content type, attributes may be optionally available, or have serval variations. Designers may choose progressive disclosure of content that hides details, which are revealed only when wanted. Or they may implement an adaptive approach, where different variations of content types are shown depending on the interests of an audience segment, or device formats.

The other aspect of the domain model, thus far unmentioned, is how it might connect with other domains. The domain model offers the possibility of enlarging the scope addressed by considering related domains. Different variations of content may draw on common content, while including different content as well (see diagram). Three different apps may share common core content. But they provide different functionality depending on their focus (touring vineyards, pairing wine with food, or knowledge enhancement of wine). The domain model can also be used to guide the planning of releases of content and functionality.

Relationship between the depth of a domain, and its scope.  Content can be deep, covering many attributes.  And content can we wide, connecting with other domains.
Relationship between the depth of a domain, and its scope. Content can be deep, covering many attributes. And content can we wide, connecting with other domains.

Relating entities: Comparisons to other approaches

Domain modelling is not the only approach to sorting through complex content. Before closing this discussion, it is worth talking about two other well known approaches that look and behavior similarly, but have some differences.

Faceted search, an approach popular in library science and information architecture, allows users to locate specific content by filtering on facets. Facets can be attributes or entities. The idea is that users can locate content that has the qualities of A & B & C. Faceted search is a popular technique, common on ecommerce sites, and is often helpful. The utility of the technique rests on several assumptions. First, faceted search assumes users know the two to four most important criteria, and will get a manageable set of results. If the set of results is large, users generally take a satisficing approach, happy with the first result encountered that is minimally acceptable. Second, faceted search presumes that each facet is independent of each other, which in the case of wine isn’t true. It is possible to get null sets if facets aren’t deep. While faceted search has been implemented on some wine ecommerce sites, it is not an effective approach for helping users discover content they might be interested in but not know about, and tends to focus on a limited range of aspects.

Linked data is an approach to modelling content that has close associations to domain modelling, thanks to the BBC’s integration of the two approaches. To simplify, linked data allows users to find content with characteristic A that has B, which has C. Organizing content using a linked data approach has both benefits and drawbacks. One drawback is that queries can be path dependent. Whether results appear promising or discouraging depends on how you construct the query. Linked data queries are generally more open ended than predefined structured queries that answer fixed questions with predictable sets of results. A bigger concern is that linked data treats all aspects of an entity as other entities, and each entity gets its own page. But not all attributes are meaningful entities — things worthy of their own content destination. On the positive side, linked data is good for what-else questions. One can link outside of a domain to other domains, such as to geophysical data.

Model behavior

Models aren’t reality, according to the cliche. Domain models may appear esoteric to some people, given that they aren’t actually something implemented directly, but are an input to other deliverables. To get buy-in for domain models, it may be best to use it as a discussion document, and note that it will evolve into the content model. While it l lacks the appeal of being code-ready, a domain model can play an important role on a project. It can uncover hidden requirements and opportunities, help align different stakeholders around a common vision, and accelerate the design process.

— Michael Andrews


  1. Chardonnay grapes originated in Burgundy. Even though most people associate Burgundy with red wine, there are also white Burgundy wines made from Chardonnay.  ↩
  2. A canonical list of varietals and zones is available from the databases of the intergovernmental wine organization OIV http://www.oiv.int/oiv/info/enbasededonneesIG  ↩