I think that the push for considering an alternative means to engage in EA is, indeed, already underway. In organizations who treat “people as strategy” (e.g., SEMCO, Whole Foods, HCL, Topcoder), wherein people are give broad self-directed control of creating and executing strategy in real time, the notion that one will “translate business vision and strategy into effective enterprise change” (note: this is Gartner’s traditional short hand definition of EA) is impossible unless one has the ability to see what is happening “on the ground” “in real-enough time” so that senior leaders can determine whether the outcomes are the ones that are desired. In order to determine whether the outcomes being created without a “command and control” model over the actions and activities of people who operate the business, the senior leaders require an ability to visualize, simulate and optimize the target state.
This is difficult for traditional management, and traditional EA, to grasp.
The 4-Hour Workweek may, at first, seem to be a “tongue-in-cheek” book; but, in reality, it describes how one can provide broad parameters and rules of engagement in which external entities operate in a manner that delivers outcomes that suit the author: making money and freeing up his time to engage in other endeavors. This is, in this case, instructive because the author has created a solution that allows him to “play the game” of managing his businesses only 4-hours per week, determining whether the decisions that his “players” are making are creating a future in line with broad goals. This author is not, however, crowdsourcing innovation but he is creating a game that it is easy and fun to play as the business leader.
In neither of the previous cases are the noted businesses able to visually predict what might happen when game mechanics (the underlying rules, rewards and feedback systems of a game) are applied under differing conditions. However, that’s not to say that
I’d also like to note that our research from Linda Cohen and Gartnter’s outsourcing team further illustrates the need for EA efforts to disengage from the desires to have control over outsourced solutions and, rather, predict how the state of those decisions impacts the future state and under varying conditions in which the business finds itself. A controlled planning approach, like our traditional definition of EA, is no longer relevant under such a scenario. This requires new tools and techniques.
We have worked with companies creating home-grown ADL (architectural description language) solutions to vet conceptual state behaviors with physical state behaviors under experience-based scenarios. This is rudimentatry and does not create visualization, but it’s evidence that simulation and optimization is underway yoday.
It is still early days, for example, with solutions like iRise that support the visualization of requirements. Combined with technologies from simulation and optimization solutions (vendors include AnyLogic; Avolution; Business Genetics; Global 360; IBM; Lanner; Lintra; Nimbus; Pegasus; ProSim; Salamander Technologies; Simul8; Software AG; Tibco; Troux; xjTek) and techniques (for example, discrete modeling, agent-based modeling, monte-carlo, bayesian networks, regression analysis, system dynamics and linear programming) it’s not too far fetched to anticipate an EA team that :
- Creates sets of conceptual visualization “components/services/patterns/reference models/etc” that are consistent with a target future state in order to guide requirements/project teams
- Simulates the effect upon the physical environment in which multiple scenarios create different target future states
- Guides requirements/project teams with preconceived visual “components/services/patterns/reference models/etc”
- Predicts the target state that’s being created at particular milestones during the SDLC
- Updates a repository of the implemented solution that reflects the new physical state metadata
- Proves that the business and IT development can move at a faster rate of speed and predict the future state/capabilities of the business relative to its goals/mission/strategy using game mechanics and other simulation and optimization techniques.
In Gartner’s EA Hype Cycle, 2010 (note: link for Gartner clients only), we defined “visualization, simulation and optimization for enterprise architecture” as the virtual lifelike representations of the behaviors or characteristics used to predict behavior among components comprising the enterprise (business processes, applications, data, technologies) in conceptual, logical and physical systems, including interactions with their environments, over multiple states in time (current and multiple future and transitional states) for the purposes of determining the best-fit future state.
The bar is set high with an expectation that the simulation and optimization environment itself can seek optimal states and identify obstacles given the changing parameters of the attributes of components within the model that are not driven by experiential-based scenario conditions created by subject matter experts alone. This contention supports the use of game mechanics to move beyond experience-based scenarios to predict the future state.
This means that simulation and optimization techniques for enterprise architecture seek to understand the implications of changes across the combinatorial behaviors of components given condition maximums and minimums, over time, at across each level of decomposition: conceptual, logical and physical. The difference versus simulation techniques applied elsewhere is twofold:
- The simulation and optimization methodologies are not based on experiential-based scenario trade-off and impact analysis alone.
- The simulation and optimization results are represented visually, not just as a report of where and when obstacles exist.
The challenge for the application of game mechanics that take advantage of tools who can provide discrete simulation solutions is that they are, today, not fully enterprise in scope, or provide answers to problems only understood by subject matter experts typically engaged in engineering, manufacturing and quality assurance or to support regulatory reporting requirements. Point solutions include techniques that support retail industry solutions (for example, retail execution optimization and trade promotion optimization), advanced analytics in the insurance industry and in the public sector, immersive learning environments, product portfolio optimization, store replenishment and inventory optimization, transportation predictive analytics and simulation, and marketing performance management.
BPMS solutions in simulation and optimization have relied on Monte Carlo, probabilistic solutions to experiential-based scenarios for processes and their related financial, people and data components, but have yet to include infrastructure components in their simulations and have not raised their game to the level of the entirety of the enterprise itself.
The allure of such promise to enterprise architects can be seen today in, for example, the Ph.D. dissertations of Dr. Christopher G. Glazner of MIT, on “Understanding Enterprise Behavior Using Hybrid Simulation of Enterprise Architecture” and Dr. David Dreyfus’ dissertation completed at Boston University entitled, “Digital Cement: Information System Architecture, Complexity, and Flexibility.” Both took different approaches to simulation and optimization, and are illustrative of leading-edge techniques in their 2009 dissertations. Andrew Haldane’s team at the Bank of England has created models to understand, visualize and simulate the effect of financial markets on the Bank of England. Dr. Andrew Lo, director of the MIT Laboratory for Financial Engineering, is leading an effort to look at markets as adaptive systems using such techniques.
The uptake and interest in such approaches are on the very early radars of leading-edge EA practitioners but, given the increased capabilities of BPMS tools supporting simulation and visualization techniques, we expect that this will change the game in terms of how enterprise architects plan for, and communicate, the potential for change and the results of change.
In the EA Hype Cycle, 2010, we noted that these technologies and techniques have entered the Hype Cycle just past the technology trigger, are transformational in nature, emerging, and penetrating <1% of the target audience. However, this space bears watching.
When strategy is created in real time, by people thousands of miles from your corporate leaders, what do enterprise architects do? Think about that as you reconsider this posting and its impact upon your day to day job in the years to come.
Read Complimentary Relevant Research
Predicts 2017: Artificial Intelligence
Artificial intelligence is changing the way in which organizations innovate and communicate their processes, products and services. Practical...
View Relevant Webinars
The Mobile Scenario: Taking Mobility to the Next Level
The definition of "mobile" in the post-app era will involve new interactions such as bots and conversations, new devices such as wearables...
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.