A demand-based orientation redefines the way productivity is is defined and recognized — replacing the concerns of the supply mindset used by providers with the demand mindset used by consumers to evaluate their engagement with the influence of a company. The following figure illustrates the drill-down of concerns from Return on Investment in what the company does to the value attributes of its encountered activity and behavior. The concerns are highly summarized and contrasted between the supply orientation and the demand orientation.
The full-blown industry of innovation management consulting includes the usual complement of academia; professional services including training; the business media; and experienced in-house FTEs. At the same time, measures of success more often seem to exist only within the small percentage of organizations that already perform innovation well, somewhat like trade secrets. And from the outside, once we exclude the top ten lists, it is difficult to distinguish between exceptional success and repetitive success among all others. Finally, much more is heard about the difficulty of being successful than about the confidence of becoming successful. Since innovation is now a default imperative in most businesses getting press, this situation is not one that will remain unchanged.
Changing it leads to the need for identifying the key factors and indicators involved in successes and failures, and being able to compare them. But an interesting and persistent problem is that so far, despite years of effort, both practitioners and motivated researchers offer such a wide range of differences in descriptions, explanations, methods and priorities for addressing the capability to innovate. This might simply reflect the true level of complexity of innovating successfully, but what we don’t know is whether the apparent complexity is originating in certainty or in uncertainty.
The challenge of reaching consistency in matching different ideas and approaches to successful outcomes also reflects two familiar problems. One is the problem of not being aware of what is unknown yet needs to be known. The other is the difference between correlation and causation. In both cases, guidance is needed for deciding what to look for and what to notice about it. And per the subsequent discovery, is innovation more like chess, or more like physics?
In the following discussion, an Archestra framework has derived from first surveying how people talk about innovation, with a special emphasis on what distinctive but general topics have most often recurred as “highly important” across the breadth of roles and environments cited. The initial perspective is very high level:
The further exploration is essentially about the underlying semantics that cross circumstantial boundaries of the speaker’s roles and environments. We classified the recurring discussion subjects and topics into two groups of meaning: one about the general subject of opportunity to innovate; and the other about the general subject of actually generating the innovation. We also took each group’s individual topics as placeholders to each be interpreted into corresponding issues about intent and about execution.
Opportunity topics and their related terms:
Actualization topics and their related terms:
The final interpretive work cross-references the opportunity versus actualization issues for intent, and the opportunity versus actualization issues for execution, yielding two corresponding frameworks for the “front end” of innovation capability and the “back end” of innovation capability, respectively. These frameworks call out the specific factors contributing to the capability to innovate. See the document, An Innovation Capability Assessment Framework for the detailed discussion of the frameworks.
All organizations face the necessity of dealing with IT transformation and therefore the uncertainty of IT’s evolution. Specifically, transformation produces and includes three huge variations of information technology availability: modification, diversity and innovation. However this variety does not mean that there is no logic to employ for making and synchronizing progress within IT’s transitory state.
Real Time Service Management means both managing real-time services in runtime and exercising continual management in real-time.
The main challenge addressed is the unpredictability of the overall environment within which technology users make choices and commit action on their own.
For a user of information, the diversity of automation technologies represents multiple ways to gain a given outcome. However, the ongoing pace and processes of continual change create a significant risk of reducing the convenience of diversity to the confusion of complexity.
The combination of modifications, innovations and diversity resists predictability and optimization. Yet the users’ concept of value is based on their confidence in getting the kind of thing that they need at the time to do what they are trying to do.
This pits the users’ circumstantial awareness of apparent options against a knowledgeable awareness of the real options (processes, infrastructure and support) that affect the users’ intentions. Additionally, the number and variety of users makes demand a problem of scale as well as timing.
Therefore the issue is, what kind of logic successfully manages the accessibility, scaling and manipulation of the IT environment? Management continually profiles the current state of user-service interaction to make decisions about where and why to exert influence. In effect, that makes strategy cultural.
The user’s overall desired experience of technology is not hard to articulate. As opposed to opportunistic usage, deliberate foreseen usage questions numerous factors that make up a degree of confidence in making choices.
The user’s intuitive perspective on technology options is highly pronounced in real-time demand. That perspective includes a set of typical concerns imposed on the pool of options. Staging the fulfillment of demand amounts to promoting manageable offers, expectations and constraints that users recognize.
Management organizations plan to enable diversity to satisfy the user, exerting influence on how the user’s requirements align across their concerns.
Management influence, as seen above, can systematically take on all of the currently transformative aspects of technology usage and direct them towards predictably enabling affects on users. The management organization also has the responsibility to generate this coverage at whatever scale the business requires. Scalability focuses the organization on infrastructure, process and support as means to enable the key real-time fulfillment workflows for translating demand into received provisions.
Applying the workflows at the necessary scale is realized through a set of basic capabilities that are exercised on a continual basis. All solutions for managing technology utilization are understandable in terms of their contribution to these capabilities.
DevOps works with the presumption that Operations are directly involved in the interpretation of requirements and use cases driving the development of solutions.
Including that perspective, this design model for a service presents a high-level abstraction that isolates the supply and demand sides of service production but aligns them within the decomposition and assignments of requirements, deriving the design implementation from the use case through sourcing and into development.
An extended discussion of the DevOps influence on the design model is found on Slideshare as DevOps and the IT Consumer.
All Archestra models and frameworks are subject to revision at any time.
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