The Content Philosopher

Architecting Information and Engineering Content

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From time to time, online discussions turn upon themselves to talk about their own organization and utility. These discussions thus turn into explorations of information and the patterns to which it can incline or perhaps to which it should be guided. As one example, a recent article appeared from Forrester Research on the topic if Information Architecture (IA) and its struggle to gain a foothold of initial credibility amid the main corridors of Information Technology (IT) management, together with the associated sub-specialties of Enterprise Architecture (EA) and Master Data Management (MDM).

Source: Visual Complexity

That Information Architecture has not made much progress in this struggle can be traced in part to its nascent state of maturity as a discipline. This immaturity is itself not too surprising given the magnitude and scope of its purview and the deeply inter-disciplinary nature of its work. One overview of Information Architecture that I find interesting is provided by Michael Cummings at Interaction-Design. Within this survey, the roots of Information Architecture are traced back to Richard Saul Wurman who coined the phrase back in 1975 and who saw the Information Architect as “someone who enables data to be transformed into understandable information”. This comment by Wurman is particularly useful because it highlights a key distinction – between the data from which information is fashioned and the information domain itself. This seems to be a distinction that has been lost in many discussions of Information Architecture – although it is a distinction I will endeavour to re-introduce.

As I have set out in previous posts, and in particular Managing Information and The Truth about Content, I have gone to some lengths to highlight the difference between potential information, which I term “content”, and the information itself, which I describe as being “transactional”. I go to these pains not to be difficult but rather because I contend that it is essential that we approach the problem of managing and optimizing information domains from two directions – from the outside-in, and from the inside-out.

To my mind, most of what we see in Information Architecture is grounded in seeing information from the outside looking in – focusing on how information transactions can be organized, discovered, presented, and used. This is a profoundly important endeavour. However, the success of this endeavour will be forever limited unless another perspective is invoked. This other perspective is what I have termed Content Engineering and it goes out of its way to see information transactions from the inside looking out – focusing on how information transactions are formed and adapted as the product of what is ideally a carefully designed, closely managed and efficiently automated environment. As information volumes expand, as they are at an accelerating rate, it is becoming increasingly important that we consider both sides of this equation – and this means, in most cases, new levels of attention being accorded to the content processes that underpin the more visible information processes.

In some ways, this discussion could be pursued as one that sketches out how Information Architecture could evolve to look more intently as what it inside the information transactions. While this may be an abundantly practical thing to do, perhaps simply to avoid spawning too many new disciplines, I would be tempted to maintain some measure of distinction between these two perspectives of outside-in and inside-out. Within actual projects, I actually do find that we leverage quite different strategies and tools when we are establishing the Information Architecture that dwells upon the surface as opposed to when we are engineering the content processes in the strata below.

This is not to suggest that the complexities manifest within the Information Architecture will be less than those seen within the content layers. In fact, the reverse is more commonly true as once we are looking at the domain of information transactions we are confronted with the universe of instantiations and with it the bewildering transactional volumes and multifaceted business implications. In the domain of the Content Engineering, we have the opportunity to apply selection and prioritization criteria, abstraction techniques and automated content processes so that information transactions can be expressly designed to address identified requirements.  The requirements that Content Engineering sets out to address, it is important to highlight, are defined by the Information Architecture activity and the work of understanding what information is needed, by whom, and to what end in terms of supporting a task or satisfying a need. With the requirement declared, the Content Engineering activity can be pursued to establish the most effective and efficient way to address the requirement. Content Engineering is thus focused on process optimization and, once again, when we are dealing with phenomenal scales of information volumes it is critical that enterprises find the efficiencies that the optimization of content processes can introduce.

The Forrester Research report highlights the fact that Information Architecture has made scant progress at winning the respect and attention of the mainstream Information Technology (IT) management regimes. There are indications that the value of, and growing need for, Information Architecture is acknowledged but, as a discipline, Information Architecture still looks nebulous to those who inhabit more specialized, or entrenched, disciplines. If Information Architecture has not yet won its due recognition, then it must be conceded that the domain of Content Engineering is barely even visible. Despite the mountains of cash expended in the direction of information technology, in truth few organizations have been forced to think deeply about their information domains and therefore to consider seriously how they should practice the discipline of Information Architecture. Fewer enterprises still have been pushed by information challenges to the point where they need to systematically optimize their information performance and to do so by looking deeply into the content sources and processes behind their information – into the formative patterns that produce the information itself. However, this is all starting to change as the age of digitization picks up steam and the rate of information expansion fully outstrips our current abilities to cope. In general, the time has come for us to step up the pace with which the interrelated disciplines of Information Architecture and Content Engineering are advanced, adopted and applied.

Seven Steps to intelligent Content

With increasing frequency, I have been returning to the subject of intelligent content. This time, we will consider by what steps content can be rendered intelligent. In preparing to take these steps, one has presumably established the rationale for the investment and the importance of this rationale will become apparent as we see just how involved this migration can be. If there is a central point to be made about increasing the intelligence of an organization’s content is that it is not only about the content – it is about the system within which that content will exist and the process within which it will evolve. Content can only truly be intelligent when it is an integral part of an intelligent process that is in turn supported by intelligent technology components and business capabilities. In order to achieve this type of alignment and integration, an organization must undertake a series of cyclical iterations, with each cycle progressively elevating and validating the intelligence of the content resources, technology components and business capabilities – as well as the process that unites them.

The Seven Steps to Intelligent Content

1. Strategy
   The process generally starts with the establishment of a goal – a vision of new capabilities. This vision will frequently emerge from accumulating experience and the quality of this experience improves as the intelligence of the content, components and capabilities increases. The framing of new strategic goals is hard work but it actually gets easier the more intelligent your environment becomes – and this because the quality of the feedback received from operations improves wherever intelligent content, components and capabilities are involved. This is a key way in which these steps are in fact part of a continuous cycle. For the purposes of elevating the intelligence of content, a key outcome from this step is the characterization of the type of content that will be needed to support the future vision.
    
2. Analysis
   Analysis entails the studying of the key entities in a domain and their interactions. What this directs us to consider is the fact that the analytical step is much more than what we would typically call “content analysis” although it definitely includes that as well. The analytical effort must look at the system stakeholders (including authors, users, customers, owners), content sources, historical usage metrics, revision and reuse patterns, and technology infrastructure elements. No one piece makes much sense in isolation. Now this is where one of the key challenges lies – analysis can become an overwhelming activity where the sheer volume of details presents an insoluble problem. This is where progressive iteration comes in. By selecting a specific set of content resources and associated processes, a project team can proceed through the following steps so as to achieve concrete results. And by doing so, the team is putting in place intelligent components that will provide enriched feedback from operational activities and this will make future analysis much easier. In a way, each venture into intelligent content is a bit like lowering a sonar buoy into an ocean of hidden processes and, over time, you begin to get a picture of what lies beneath.
    
3. Design
   Undertaken within a manageable scope boundary, this activity designs the content structures, information products and services, measurement systems, technology components, business capabilities, and integrating processes. Establishing a manageable scope boundary is in fact a critically important preparation – otherwise the scale of work that must be undertaken will be so great that too much time, and too many dollars, will be expended before any results are seen. From a business perspective, this is something to avoid. Even from a purely technical perspective, this is something to avoid because if operational feedback is postponed for too long then all the investments, from design to deployment, can become unhinged from reality and therefore be suspect. It is also important that the design tools, methodologies and notations being used are sufficiently lightweight and accessible to allow the team to work quickly and to continuously engage colleagues from a variety of domains and disciplines.
    
4. Exploration
   Of all the steps in this process, the most important is exploration. If analysis and design are advanced quickly, as they should be, then it becomes necessary that they be supplemented by exploratory investments in mock-ups, prototypes, demonstration scenarios, and initial working capabilities. Where intelligent content is the goal, I tend to emphasize this step even more than I would for any other type of technology investment. This is because intelligent content, and the associated components and capabilities, tend to be new for most organizations and there is a learning curve to follow that is best facilitated by hands-on experience. What is also a little different in my treatment of this otherwise familiar step is that I prefer to see it receive a substantial allocation of schedule, expertise and financial resources. I also like to see it taken well beyond prototypes and demos – to the point where the initial capabilities are being trialed by real users and tested by real business stakeholders. This is a topic I have addressed many times in the past including in a whitepaper on XML Business Templates.
    
5. Transformation
   Based on the foregoing a series of investments are made to transform content assets, technology components and business capabilities – all with the view to establishing a higher level of intelligence in how the organization as a whole performs. Each of these dimensions represents a challenge with the adaptation of business capabilities being the most daunting – in large part because the types of activities that will be introduced, and the types of new services that become possible, will be completely unfamiliar to the community of business stakeholders. The transformation of content assets will be a challenge all on its own partly because projects often assume that this is the only thing they will need to change in order to realize the benefits associated with an intelligent system. The transformation of content is also a challenge because historically, content has existed in forms, and played roles, that did not require very much intelligence at all. Transforming the technology components in most cases involves introducing new types of technology into organizations and this too can represent a substantial challenge because it will require that those responsible for technology management take the time to learn about a new class of technology tools. The importance of the exploration step becomes more clear as we see how it can be used to progressively tackle some of these challenges by involving the various stakeholders in ways that they can understand and evaluate.
    
6. Validation
   In a comically large number of content management projects, validation is associated with the simple parsing of the transformed content assets against the content model that had been established in the design step. What is even more comical is the fact that many projects do not even perform this simple check. Validation, however, is a much more expansive concept than just parsing content against a given schema, although it includes that. By validation, I mean the complete validation of the target system which means that verification is performed on content structures, on the semantic details of the content, on the functional behaviour of the content within various technology components, and ultimately the performance of the overall system as it conducts various end-to-end processes. It is usually as part of this validation step that a variety of quality control mechanisms are introduced into the content processes so that the performance of the system can be metered and measured so as to provide useful feedback. Again, this clearly entails substantial investment and it should incline projects to tailor the scope of their individual investments so that the undertakings remain manageable in size.
    
7. Deployment
   The content, components and capabilities realized through the proceeding steps are at this point deployed into production. As has been coming into focus as we consider each step in turn, these deployments should be small increments that can be monitored, managed and measured individually. Ideally, there would be a regular stream of releases with this keeping an organization’s exposed information services in a state of continuous flux – what I guess we would call a “continuous beta”. The feedback mechanisms architected into deployed content, components and capabilities, as they proliferate, will increasingly come to provoke strategic insights and over time an organization will get better at working through these seven steps so as to act on those insights. In time, and perhaps accidentally, the overall system wherein content becomes useful information will become fundamentally more intelligent.

As things begin to fall into place, and the cyclical process of steps becomes something of an institutional habit, the ascent begins and the results that come into view will be understood as genuinely remarkable. On the more mundane level, the scoping of individual efforts is of paramount importance – the journey towards intelligent content, and with it intelligent components and capabilities, is a long one. The cycle of steps must be repeated often. Not that anyone has the option of sitting out and declaring that the old ways will do just fine. That would be a little like opting out of evolution and becoming some form of Relicosaurus that is oblivious to the fact that it is slated for extinction.

As Richard Dawkins once argued, in support of the compelling logic of the theory of evolution, small steps are the only way to climb “mount improbable”. It is better to succeed beyond all expectations within a limited domain, and thereby to provide an example for other initiatives to emulate, than to partially succeed against a much larger scope. This is why I have sketched out these seven steps as a sequence that is continuously repeated. As I used to tell my children when they were trying to move large volumes of toys from one location to another - many small successes is better than one big disaster.