What is Precision Content and Why Do We Need It?

In this article:

A look at how our brains work with information
Exploring information typing and how Precision Content information types can extend structured authoring across the enterprise
Exploring how we work with that XML — more specifically, how the Precision Content writing methodology can help to provide better clarity and precision to information.

Managing Information

Paperless workplace idea, e-signing, electronic signature, document management. Businessman signs an electronic document on a digital document on a virtual notebook screen using a stylus pen.

We need to start with the question: Do we really have a problem that needs solving?

I suggest that we are faced with managing much larger volumes of information and content than ever before. The traditional ways of dealing with information, such as sharing information without a content strategy, isn’t working to solve the needs of our audience. We need better ways to create and work with more precise and concise information across the enterprise, not just in technical publications.

To put this into perspective, consider the life and exploits of a distinct relative. Let’s call him “Lewis”.

Lewis, living in the 17th century, would have been exposed to as little information as there is a single issue of the New York Times. There was considerably less information to work with within their entire lifetime. We’ve really come a long way. With the volume of information we create, we need to find better ways to get the right information in the hands of the right people at the right time.

Recently, we commissioned a study with the Association for Information and Imaging Management to explore how information is created across enterprises. They surveyed over 300 companies giving us insight into how organizations employed rigor in their content creation.

I want to draw attention to the conclusion that the greatest advancements in information management really don’t lie in the technology as much as in the content itself. This is important when we look at how we’re managing terabytes, gigabytes, or petabytes, of information in large organizations and how we’re going to unlock the knowledge within that content. I contend that using technology to solve our content problems might not be as effective as we need it to be. Our content problems require content solutions. All the technology in the world isn’t going to make our content better. We need to look at how we write content.

Advancements in AI and intelligent systems challenge us to make our existing content available for multiple use-cases. To begin, we need standards for intelligent systems that are portable and useful across applications. Ideally we need our content to be as findable, usable, and reusable as possible. Information, in this context, is an aggregation of different sets of data used within an organization. Knowledge is information that we’ve contextualized for a specific audience to elicit a specific user response. We call this intelligent content. It is not limited to one purpose, technology, or output. It is semantically rich, structurally rich, and semantically aware, making it discoverable, reusable, reconfigurable, and adaptive. This helps us serve two fundamental areas where structure is important.

The human brain: structured information makes it easier for us to be able to find, understand, use and retain information.
Technical systems and Extensible Markup Language (XML): structured information helps to make that technology easy to integrate, search, process, and reuse information.

When we are working with Microsoft Word or Excel or many other authoring tools, we’re actually working with XML. Through structured authoring, we want to expose that XML to define the meaning of content, not just the presentation of that information. To accomplish that,we are working with Darwin Information Typing Architecture (DITA). DITA is topic based structured authoring architecture used by hundreds of technical communication organizations around the globe.

To understand more about DITA, let’s unpack the acronym. “D” stands for Darwin. The intention was to describe the possibility of sharing information across organizations, across departments, and across industries. The goal was to create a common vocabulary so we could deliver source XML developed within a DITA mechanism from a software provider to our OEMs, to our partners, and to our clients. The intention was to create a way to share information when organizations didn’t share industry-specific vocabularies. With a DITA framework, the receiving organization could still publish that information along with other information within their repositories. This was one of the original intentions of DITA. We haven’t seen that potential evolve to a great extent over the last couple years.

Next, “information typing”. Information typing is how we break down information based on the semantic meaning of that content. We use those categorizations to create source content that we can deliver to books, websites, telephone, smartphones, and microwave ovens. Information is durable and reusable because we’ve not based our organization on a book model, but rather on a topic based model and content meaning.

Finally, “architecture”. Architecture in this context refers to structures that allow organizations to reuse content. Although these structures are not necessarily standard within XML, they have become a core part of the data architecture.

Although very useful, the DITA format has become increasingly complex and intimidating. I have come to realize that DITA is not well integrated into many organizations. Common complaints we encounter with DITA implementations include:

There are too many tags
It is too complex to work with
Information reuse and working with conditions and profiling

Within an enterprise the organization of files and information can become a complex issue to solve. For example, half finished documents and multiple copies are often stored in network drives along with valuable information. In a Content Management Systems (CMS), repositories often become disorganized and there is no plan on how to keep it clean. In this scenario where files have been indiscriminately stored, there is a lot of data to work with and it can be challenging to determine what the topic is of a given document.

Working with data through topic based authoring we often see that our topics are way too large. What’s really required, when we examine content repositories, is some amount of rigor, discipline, and governance, around how content is created through adhering to content standards.

When we surveyed the AME members about content standards, our results revealed that no fewer than 20% of organizations follow any real standards around the creation of content. Almost 40% of organizations do not have standards at all. Yet, a large percentage of them believe that they should. Across organizations, like the ones surveyed, we believe that XML offers an effective agnostic structured framework and open standard. It is not specific to one vertical or even specifically technical communication. What it lacks is a robust authoring methodology.

For years now I have been exploring how we could, for instance, take information mapping within XML to create a new content standard. That is how I started developing the Precision Content content standard. We have created a solution that’s built for better broad based content collaboration, Lifecycle Management, content classification and multi-channel publishing. I also adapted DITA XML to this writing methodology by creating a specialization that streamlined the number of tags and tightened up the structures to make it easier for authors to work with.

At Precision Content, we have three very specific laws that form the basis of our standard.

Utility: Content must be in a form that’s useful. When we evaluate content, is it really what you need? Do you need something that’s more specialized or more tailored? There’s a great range of utility, and a great range of things that we can do with content depending on what format it’s in. So we need to understand what the need is to be able to understand what sort of utility is required from that content.
Maintainability: All content must be managed. If we don’t institute rigour around how content is managed, in terms of workflow and governance, peer review, or editing, systems begin to fall apart.
Usability: Content must be written to suit its function. This is the area where many implementations fail: they have not adapted the writing methodology specifically to writing in topics within DITA.

Based on these laws, we have built a framework that brings together four cognitive principles, language arts, and information types to form a robust methodology for ensuring the utility, maintainability, and usability of content within an organization. Let’s explore each aspect of this framework, beginning with the four cognitive principles.

4 Cognitive Principles

Our standard builds on an understanding of how our brains work with information. There are four cognitive principles for organizing content: consistency, chunking, relevance, and labeling.

Consistency: We filter out all the noise and look for patterns to associate meaning to.
Chunking: We break up content visually into smaller groups to organize the information.
Relevance: We look for how words relate to one another to organize them.
Labeling: We identify what those types of information words represent and label those groupings.

When we present information in a structured way, we make it easier for people to work with that information.

Language Arts

We divide language arts into two very distinct camps. One is language arts for personal response. The other is language arts for information. When we look at language for personal response we are talking about rhetoric and persuasive writing. This is represented in the way we learned to write in secondary school, where we wrote essays and persuasive articles that were intended to engage the reader. Language arts for information, on the other hand, is content that’s not meant to be read, but instead is meant to be used. With language for information, we don’t want an engaging experience with the reader. We want to present the reader with information that they can easily use. Typically, the reader is expected to scan, find the answer, and stop reading.

Now with these two different types of writing, we have different methods and rules around how we write content. Looking at language arts for information, for example, we can review typical IKEA instructions. They are very visual and easy to use. After using them to assemble furniture, there’s no additional function they serve. Now, if we tried to use language arts for personal response to write IKEA instructions, they would look very different. It could be very well written information. But again, it would be information that’s not written to suit its purpose. It may be beautiful writing, but it’s not meeting the usability requirements.

Another relevant example would be the job application process. The resume and the cover letter both contain much of the same information. Except, the resume is highly structured and the cover letter is a letter like any other. Looking from across the room, you can spot two piles of paper and see that one is a pile of resumes and see that the other is a pile of letters, but you don’t know what type of letter it is. You have no idea whether it’s a cover letter for a resume, or it’s a letter to your grandmother, they all look exactly the same. We can’t determine any semantic meaning out of the structure of a cover letter.

Of course, a cover letter is written to the hiring manager. You want to engage that hiring manager and convince them that you’re the person to be invited for an interview. A resume on the other hand, can be used by anybody in the organization and often is when they’re looking for candidates for a specific job or looking for existing candidates for other jobs in the organization. The cover letter is a great example of language arts for personal response, while the resume represents language arts for information.

Information Types

Next let’s look at information types. We need to understand the desired user responses for the information we are offering. For instance, we use reference topics when we need the reader to know something. We use task topics when we need the reader to do something. And, we use concept topics when we need the reader to understand something. There are very important distinctions in these types of information and how we write for them. How we present a type of information is the difference between knowing and understanding something.

Whenever we read information, we need to understand context. We need to fill in blanks using pre-existing information or pre existing knowledge. There are three specific areas of the brain and three very specific types of memory that we work. The first is procedural memory. Procedural memory is probably the most robust type of memory that we have. In fact, we rely on procedural memory to recall things almost on a subconscious level. We don’t actively need to recall procedural memory. It’s just always there ensuring we know how to drive a car, how to ride a bike, or how to set the table. All of these activities are held in our procedural memory and we rely on that memory to fill in gaps within task based information. When we explore working memory, the frontal cortex rapid access memory, we find that it is very malleable and it changes all the time.

When we share information with people when we want them to know something, then we offer it as a reference topic. We present information for what they need to know without the requirement that they necessarily understand it. For understanding, we rely on a different type of memory. This is called semantic memory, part of our declarative memory, where we store knowledge and understanding of things that we may not have actually experienced firsthand. The content in this category represents the highest cognitive load that we place on our readers, because we are offering new concepts the reader needs to understand. With the Precision Content framework, we’ve taken concept, task, and reference and we use these types to organize information. We have broken down content into smaller blocks and sub blocks within these information types. We have also introduced two new topic types or two new information types to round out our model: process and principle.

For process, we’ve specialized it from task and what it does, to explain how things work by introducing the concept of stages, actors, and actions. Next, principle information is information that we use to advise people on things that they need to do or not do. We have introduced new semantics around applicability, outcome, and resolution.

Looking at content types by function, we have:

Reference content that describes things the reader needs to know.
Task types which instruct readers on how to do things.
Concept types explain things the reader needs to understand.
Process types demonstrate to the reader how things work.
Principle types of information which advise the reader what they need to do or not do and when.

Looking at these information types, we chunk information into blocks within topics to assemble information, then into maps and publications. Blocks are bite sized chunks of information that contain one main idea. They allow you to organize information into logical sections. So, one idea, one intended user response, one information type. Every block or section within a topic gets an information type. We apply titles, and rigor around those titles, for every block, and we use sub-blocks to break up large blocks of information. This allows us to create better structured building blocks for information. This process is useful beyond creating new publications or new information products. Using this method makes it easier to reuse content or blocks within other topics in new information products.

When constructing topics, we take blocks and we put them together in a primary block, which is a short description or abstract. We add additional blocks in the body that support that particular topic. Each one of these blocks within this task has its own type of information reference, principal reference, or task. We have rules around writing effective titles. These apply to section titles and blog titles as well, so that we’re consistent in how we label information to make it easily scannable and understandable. With a rule for each type of information, the reader will quickly understand what type of information they’re looking at. They can easily see if it’s the content that they need to be looking at.

Most importantly, it is essential to understand the intended user response when organizing content. As such Precision Content has created a robust authoring methodology. Using four cognitive principles to organize information, separating information into languages for personal response and language arts for information, classifying information based on five distinct information types, and focusing on content authoring at the block and topic level, we’ve created a framework based on rigor and the principles of utility, maintainability, and usability. This methodology has enabled our clients to step back from applying more technology to their content problems and instead, examine, strategize, and apply a rigorous framework to how they write and maintain their enterprise content, saving them time, money, and opening up possibilities for content reuse in emerging platforms like chatbots and other conversational interfaces.

Is your content ready for what comes next?

There’s no time like the present to get started with preparing your content for the next wave of technologies so that you can move forward with intelligent and scalable solutions — Contact us to start the conversation!

About the Author

Rob Hanna co-founded Precision Content in 2015 to pursue his goals to produce tools, training, and methods that will help organizations make their high-value content instantly available to all that need it including customers, staff, partners, and even other information systems that need to consume that content. Driving this development is the Precision Content® Writing Methods, based on the best-available research over the last 50 years into how the brain works with information. Today Rob leads his highly-skilled team of content strategists, information architects, writers, trainers, and developers to serve the needs for digital transformation for businesses across North America.