This blog’s title (and URL) begin to feel a bit narrow. This seems better.
People are smart. Not all of us all the time, certainly not me. But by and large, people are smart. This seems odd when considering that we frequently encounter less-than-desirable situations that often have endured for a long time.
But people often know what could be done to improve even such long-lasting situations — this knowledge just isn’t put into action. Jeffrey Pfeffer and Robert I. Sutton discuss this phenomenon in their book The knowing-doing gap: how smart companies turn knowledge into action and in a related article.
From this perspective, it becomes obvious that addressing such less-than-desirable situations as a knowing problem is unlikely to be effective — the people affected likely have all the knowledge required to improve the situation. Addressing such situations as a doing problem, i.e. working with people to put their knowledge into action, seems likely to be much more effective…but only if the people affected think a problem exist, want to do something about it, and want (my) help in doing so.
Now putting the knowledge in this post into my actions would be a big step toward closing one of my major knowing-doing gaps.
I had the opportunity to begin learning about content strategy in the last two months or so.
I’ll probably have more to say on how this changed my perspective in thinking about a lot of things in another post. Sarah Wachter-Boettcher’s book Content everywhere: strategy and structure for future-ready content got me started and Jonathon Colman’s Epic List of Content Strategy Resources pointed me to more great resources on the topic.
I might have stumbled across the term content strategy in Milan Guenther’s book Intersection: how enterprise design bridges the gap between business, technology and people. The enterprise design framework uses 20 aspects (organised in 5 layers) of design work in an enterprise context. The second layer, anatomy, includes these aspects: actors, touchpoints, services, content.
I was happy to find a strong element of service design in the framework, but thought emphasising the content aspect odd. Well, maybe for mostly digital services that made sense… But what about (physical) evidence? But then I’ve had issues with overemphasising service evidence, too.
By now I see the point in discussing content (strategy) at this layer in the framework, but I still feel uneasy about the (state of the discussion) of physical objects in service design. (Or am I just not reading the right stuff or talking to the right people?) Thinking of physical objects (including goods, physical products) as vehicles for provisioning services (as discussed by Dave Gray, among others) seems promising. Physical objects can certainly also be vehicles for delivering content. (We could also view content delivery as a type of services.) And then there’s a role for physical objects in a service evidence context (in a narrow sense, please).
Is it time to bring these thoughts together and elevate the discussion of physical objects in service design?
2014-09-21: Tom Graves has written a brilliant post titled From Product To Service.
There’s a lot of talk about the customer lifecycle and the benefits of paying attention to it out there.
A typical customer lifecycle goes like this: A potential customer discovers our offerings, learns about them and our value proposition, buys our offerings and thus becomes an active or current customer, and finally stops buying our offerings and thus becomes a former customer.
But is this really a typical customer lifecycle?
I don’t think so anymore. I think this more typical: A potential customer is born, grows up, lives through adulthood, grows old, and finally dies.
Obviously, this is quite coarse-grained and could be detailed by adding many significant events.
The first lifecycle above really describes the relationship between a customer and a business (and this only in a very limited way).
Paraphrasing Chris Potts, businesses need to decide how they want to appear in their customers’ activities, experiences and lives. I think this a lot easier to do when taking the second perspective on the idea of a customer lifecycle.
Both. Sort of.
Recently the topic of whether to take a journey-first or a content-first approach to delivering digital user experiences came up. The former seems to be favoured by more “traditional” user experience designers while the latter is favoured by many content strategist. No surprise here.
For now, my take is this:
I think I want to start with the customer experience in a conceptual, coarse-grained and probably channel-independent manner. A concept map (or a service ecology map, despite this grandstanding name) is a good basis from which to start mapping a customer journey.
Digging into more detail, increasing the focus on content seems useful, both in terms of detailing the content model and the actual content.
In turn, the content model as well as representative content elements can be an effective basis for designing the actual user journeys for a digital service.
Thoughts, please? Thanks.
I have been interested in domain modelling for a long time. Analysis Patterns by Martin Fowler, Domain-Driven Design by Eric Evans and Streamlined Object Modeling by Jill Nicola, Mark Mayfield & Mike Abney greatly influenced my thinking and (some of) my work.
(The fundamental concepts described in Streamlined Object Modeling might actually be some of the most under-appreciated ideas in software development and information modelling.)
While I understood the benefits of good domain models early on, I can only recall one project incorporating a domain model into its software. Even when technology was able to effectively support implementing domain models, many developers seemed happy to read and write data structures to persistent storage manually and to manipulate these data structures with imperative code. To project managers, these things were probably too abstract, too invisible and too far removed from the myriad of immediate concerns they had to deal with in parallel.
And, of course, I probably didn’t make my point as well as I could have.
I was intrigued when I learned that content strategist had discovered domain modelling (and in particular Eric Evans’ work) for their purposes. Content strategists, if you read this, go and read Analysis Patterns and Streamlined Object Modeling, too — I’ll still be here when you’re done.
As I’m learning about content strategy and content management systems, I get a hunch (hope?) that this might actually be another chance to bring the benefits of domain modelling to the enterprise. This might be another chance to benefit from structured, connected and annotated information, and achieving objectives by interpreting these connections and annotations rather than writing lots and lots of imperative statements in code, process charts, rule lists or, for some of us, PowerPoint and Excel.
Content is likely to be much more tangible and immediately accessible to stakeholders than domain models ever where — as almost everyone has an opinion as to what needs to happen on the corporate website, I’m confident many stakeholders can be nudged into having an interest in content.
Let’s see how far I get this time…
This has been a recurring theme in my work so I figured I’d write about it:
Information models (domain models, object models, data models, content models) are typically subject to many different forces influencing their designs, and some of these forces can act in opposing directions. Some of these forces are specific to the problem at hand and its context while others are more generic and keep showing up in my work.
This post is about some of these more generic forces. (Or maybe its only about two potentially useful approaches.)
Avoiding redundancy vs. ease-of-use: Avoiding redundancy pushes towards fine-grained models in which classes and instances can be (re-) used in different contexts. A fine-grained model can be difficult to understand and may be difficult to use for developers, application/information managers and end-users. Ease-of-use pushes towards coarse-grained models which may be easier to understand but have a higher risk of inconsistencies if data is kept redundantly.
Instance-based vs. class-based differentiation: Class-based differentiation introduces different classes (and often inheritance hierarchies) to models in order to represent specific concepts. A high number of different classes can make a model unwieldy, difficult to understand and difficult to use, especially when the intent of and differences between classes are not described well. In contrast, instance-based differentiation represents specific concepts through instances of generic classes. In order to so, the model often has to introduce additional classes, e.g. for type, state or group objects. The resulting model typically has a simpler fundamental structure (fewer classes for core elements), but a necessarily higher level of abstraction can also make the model difficult to understand.
A few thoughts occured to me in this context:
Information access and modification are different concerns and might warrant different approaches: Command-Query Responsibility Segregation is one approach that might help here.
Information access & modification by administrators, developers and end-users are different concerns and might warrant different approaches:
In my experience (yours will vary), software systems tend to structure data according to development/runtime concerns, with some allowance being made for end-user concerns. Administration concerns tend to get little attention. Interestingly, this seems to be somewhat different for content management systems, likely because content managers are an essential end-user group in this context. CMSs are built to administer information in one structure and make it available in many different structures.
Could content management systems help address the different needs of administrators, developers and end-users? Even of administrators, developers and end-users of other integrated software systems?
And could this also have beneficial side effects with respect to automated testing, continuous integration & delivery, etc?