Planning Around Corners: How AI Reveals What’s Coming Next

Sometimes, running an enterprise feels a bit like trying to build a house during an earthquake. One day, you’re dealing with disruptive technology and changing customer needs, next, it’s budget pressures and fears of a recession looming. Just when you think you’ve got your footing, government policy changes abruptly and forces you to rethink everything….

Sequestration Planning May Illuminate Value Engineering via Enterprise Architecture

 The next 5 months portend a spectacle of US Congressional battles to be waged ahead of the pending, mandatory “sequester” – automatic, mandatory federal government spending reductions of about $1 Trillion over 9 years, in non-exempt, discretionary appropriations, set to take effect 1/2/2013. Forward-thinking planners in government IT organizations, in large Programs that depend upon IT, and among the Systems Integration (SI) community are likely to dust off Enterprise Architecture skills for analyzing budget cut implications across their IT investment portfolios, and possible cost savings opportunities to offset them. Leveraging a methodical, EA-guided approach to both assess impacts and adjust spending priorities, while illuminating new areas of savings, is a sure route to mitigating serious risks and delays to delivery of critical citizen services.

Whether you’re dusting off the existing EA artifacts, or need to take a very rapid, optimized route to constructing initial enterprise IT models, the driving principle at this time will be rapid, absolute reduction in complexity with a clear line-of-sight to cost savings. “Complexity” here simply refers to an inefficient or needlessly detailed volume of time and resources applied to deliver IT solutions – time spent re-engineering processes, building redundant interfaces and monitors, installing hardware & software in a piecemeal fashion.

Driving complexity, and therefore introducing cost savings, out of engineered systems is the central tenet of “Value Engineering”  – “the optimization of a system’s outputs by crafting a mix of performance (function) and costs”. Essentially, deliver the same capabilities with better value by driving down the cost to build and/or operate. Section 52.248-1 of the FAR (Federal Acquisition Regulations) describes the “Value Engineering Clause” that is inserted into many large Federal IT contracts – enabling the contractor to propose changes to the system being developed (i.e. a “Value Engineering Change Proposal”, or VECP). If the proposal is accepted, the actual or collateral savings derived by the government (through cost modification to the contract) can be shared with the contractor. It’s a win-win opportunity for the government, system beneficiaries and the contractor community to discover and propose engineering changes that will lower costs, yet still deliver the same or better results.

Many VECPs are submitted for technology assets – i.e. contained systems that may work better when newer, less-costly components are substituted…like missile systems or electronic devices. This may be because the contractor typically is the sole source of knowledge and research concerning how to optimize and build the components, the “value” and function of the asset is very clear (i.e. it’s delivered and explodes on target) and constant innovation is a demand of the environment. VECPs are also submitted for IT systems and programs, though it’s much more difficult to identify and propose the specific cost savings or cost avoidance that might result – since IT systems are frequently dependent upon many external or interfaced elements, vendor products and processes.

An EA-centric review of a program or line-of-business IT investment may quickly yield insight that would lead to specific value engineering opportunities, and therefore reductions in IT costs. For example, a particular set of information may be created, shared and recreated across several systems, using different processes, datastores and technologies. An segmented EA approach driving down from the particular business, process and information domains, may quickly illuminate targets of opportunity for database or interface consolidation – and therefore potential consolidation of supporting technologies (i.e. storage, networking, processors). This may lead to optimized technical operations and business process performance, which can be clearly mapped back to the Enterprise Architecture to validate that governance and mission requirements are (still) met, cross-enterprise risks are mitigated, and IT investment portfolios and procurement activities are properly adjusted or re-aligned.

With this kind of information, a VECP could be constructed that very clearly proposes both program-specific and collateral (i.e. across the rest of the enterprise) savings resulting from introduction of state-of-the-art consolidating technologies (for example, pre-integrated, self-contained and consolidated database engineered systems, perhaps cloud-enabled). At the very least, an EA view can help identify and prioritize targets of opportunity for Value Engineering that may become part of an effective and timely sequestration response – both before and after such an event, and in fact as part of the annual capital planning and investment control (CPIC) processes.

Sequestration Planning May Illuminate Value Engineering via Enterprise Architecture

 The next 5 months portend a spectacle of US Congressional battles to be waged ahead of the pending, mandatory “sequester” – automatic, mandatory federal government spending reductions of about $1 Trillion over 9 years, in non-exempt, discretionary appropriations, set to take effect 1/2/2013. Forward-thinking planners in government IT organizations, in large Programs that depend upon IT, and among the Systems Integration (SI) community are likely to dust off Enterprise Architecture skills for analyzing budget cut implications across their IT investment portfolios, and possible cost savings opportunities to offset them. Leveraging a methodical, EA-guided approach to both assess impacts and adjust spending priorities, while illuminating new areas of savings, is a sure route to mitigating serious risks and delays to delivery of critical citizen services.

Whether you’re dusting off the existing EA artifacts, or need to take a very rapid, optimized route to constructing initial enterprise IT models, the driving principle at this time will be rapid, absolute reduction in complexity with a clear line-of-sight to cost savings. “Complexity” here simply refers to an inefficient or needlessly detailed volume of time and resources applied to deliver IT solutions – time spent re-engineering processes, building redundant interfaces and monitors, installing hardware & software in a piecemeal fashion.

Driving complexity, and therefore introducing cost savings, out of engineered systems is the central tenet of “Value Engineering”  – “the optimization of a system’s outputs by crafting a mix of performance (function) and costs”. Essentially, deliver the same capabilities with better value by driving down the cost to build and/or operate. Section 52.248-1 of the FAR (Federal Acquisition Regulations) describes the “Value Engineering Clause” that is inserted into many large Federal IT contracts – enabling the contractor to propose changes to the system being developed (i.e. a “Value Engineering Change Proposal”, or VECP). If the proposal is accepted, the actual or collateral savings derived by the government (through cost modification to the contract) can be shared with the contractor. It’s a win-win opportunity for the government, system beneficiaries and the contractor community to discover and propose engineering changes that will lower costs, yet still deliver the same or better results.

Many VECPs are submitted for technology assets – i.e. contained systems that may work better when newer, less-costly components are substituted…like missile systems or electronic devices. This may be because the contractor typically is the sole source of knowledge and research concerning how to optimize and build the components, the “value” and function of the asset is very clear (i.e. it’s delivered and explodes on target) and constant innovation is a demand of the environment. VECPs are also submitted for IT systems and programs, though it’s much more difficult to identify and propose the specific cost savings or cost avoidance that might result – since IT systems are frequently dependent upon many external or interfaced elements, vendor products and processes.

An EA-centric review of a program or line-of-business IT investment may quickly yield insight that would lead to specific value engineering opportunities, and therefore reductions in IT costs. For example, a particular set of information may be created, shared and recreated across several systems, using different processes, datastores and technologies. An segmented EA approach driving down from the particular business, process and information domains, may quickly illuminate targets of opportunity for database or interface consolidation – and therefore potential consolidation of supporting technologies (i.e. storage, networking, processors). This may lead to optimized technical operations and business process performance, which can be clearly mapped back to the Enterprise Architecture to validate that governance and mission requirements are (still) met, cross-enterprise risks are mitigated, and IT investment portfolios and procurement activities are properly adjusted or re-aligned.

With this kind of information, a VECP could be constructed that very clearly proposes both program-specific and collateral (i.e. across the rest of the enterprise) savings resulting from introduction of state-of-the-art consolidating technologies (for example, pre-integrated, self-contained and consolidated database engineered systems, perhaps cloud-enabled). At the very least, an EA view can help identify and prioritize targets of opportunity for Value Engineering that may become part of an effective and timely sequestration response – both before and after such an event, and in fact as part of the annual capital planning and investment control (CPIC) processes.

Rhizome: On Dilemmas in Enterprise Architecture Planning

In the field of IS and management we often put forward a certain conception of the organisation, the social. In contemporary business consulting and management academia, the organisation is often conceptualised as a hierarchical open system with a certain body of knowledge supplying the management system with rational decision making. Other alternative, academic approaches are influenced by literature studies and Gadamer’s hermeneutics (Gadamer, 1975), promoting the need for understanding and context, the particular, rather than the universal and manageable. Emerging from these two spectra, each fighting for their own conception of subjectivity and objectivity, Anglo-Saxon and continental philosophy, each defining the criteria for truth and meaning, one uncovers systems theory and cybernetics which proposes a model for generalising structures and properties between different phenomena in the world: Bertalanffy (Bertalanffy, 1969) suggests a unified cybernetic model for living, mechanic, and social systems, whereas von Foerster (Von Foerster, 2003) and Luhmann (Luhmann, 1995) suggest a second order model based on constructed observation and interpretation. In organisation studies, first and second order systems theory each postulate their own conception or construction of social reality: Parsons defines the social as actions or events referring to each other within a structural organising of social functions, whereas Luhmann flips the tin can with a functional organisation of social structures based on communication, reproducing and sustaining itself through Maturana and Varela’s concept of autopoiesis (Maturana & Varela, 1980).

Amidst IS management’s–and thus EA’s–attempt to establish a common, trans-disciplinary foundation for research, there appears to be an ontological schism of what the social is and organisations really are. Is it a collective intelligence or logic of rational decision making? Is it a reactive, intersubjective collective attempting to make sense of the world in hindsight through history and culture? Or is it a system or a construction of a system that organises, structures, or communicates through constant adaption and recursive reproduction only by reference to its own recursion and reproductivity? The latter approach dissolves the former two boundaries by creating a boundary of distinction even more important than the understanding subject itself at the edge of every possible system. It is the distinction between system and environment that generates or fabricates meaning and truth, but it comes at the cost of reducing our very own processes of cognition and sensemaking to a set of vibrating antennas or satellites mounted at the fragile surface of every human system.  

An ontology of the social is thus far from complete. Enterprise Architecture (EA) seeks to address this by building layers of abstraction and control, thereby assuming that static systems models of socio-technical relations yield manageability and transparency. Accountability is achieved by linking formal role descriptions to process models and system landscapes, often positioned in a well-defined hierarchy and stored in a database repository for later reference and reuse. In order to reuse ‘best practices’ and assure a certain level of maturity in framework and methodology, enterprises often implement their architecture practice against existing reference frameworks and enterprise meta models. Frameworks such as FEAF even include a CMMI-like maturity model for EA, which assesses the success of architecture program by measures such as completeness and integration. OMB, the US Federal Office of Management and Budget, has furthermore published a set of measures of architectural completeness for evaluating US Federal Agencies. The highest achievement, level 5, is the architectural utopia in which the organisation practicing EA corrects its own business failures by architectural inspection. Architecture is here synonymous with optimising an organisation.


Given the above reflections on what the social really is, is it really philosophically reasonable to suggest that a stable, decomposable, hierarchical model, which most enterprise meta-models really are, is capable of building a comprehensive model of the social? Is it really meaningful to stretch virtually any organisation, be it government or private, along a five-level diagram and measure it by how well-described architectural elements are? And what happens when the Federal agency hits the ceiling after 5? Those are clever and important questions that information and organisation science ought to ask. Unfortunately, that is seldom the case. Maturity models, in the classic form of a five-step ladder, are an inherent part of any contemporary management/IS theory: process maturity, architecture maturity, service maturity, integration maturity. The five-step Capability Maturity Model (Paulk, 1995) has its roots in systems engineering carried out by engineers building space shuttles for NASA. As universal as it may be, the problems, issues, and solutions faced by modern organisations are far more muddy, messy, an ill-defined than those originally faced by defence contractors and DoD bureaucrats. Such fast-paced, deep problems are also characterised as wicked problems (Rittel & Webber, 1973):
  1. Wicked problems have no definitive formulation. One can infer that the problem exists, but will never be able to fully document the problem.
  2. The solution to a wicked problem is “good or bad” not “true or false”.
  3. Every possible solution is a one-shut operation as every solution attempt will leave a trace which cannot be undone.
  4. Each wicked problem is unique and may eventually be the symptom of another, underlying wicked problem.
Through my previous research, I have suggested a systems theoretical approach towards understanding and explaining EA. Systems theory is helpful towards describing the messy complexity of social and communicative structures. Second order systems theory adds a rich, dynamic theory for understanding communication in- and outside organisation by describing the exchange of utterances between human actors in search of meaning (Jensen, 2010). I believe, however, that these two key conceptions of enterprise planning and governance can furthermore be extended into a general theory of EA by including Deleuze’s theory of the rhizome.

Deleuze (Deleuze & Guattari, 1988) describes the rhizome structure (Deleuze & Guattari, 1976) as a meaningful alternative to uncovering complex structures, be they social or biological. Western society, Deleuze explains, has built its historicity and philosophy on the basis of binary structures: true-false, yes-no, top-bottom, maturity-immaturity. Contemporary EA frameworks are, in fact, highly binary: layers separated by clear boundaries, processes with a start and end, structured organisation charts and capability maps with a top and bottom. The rhizome is a viable alternative since it assumes an inherent complexity of what it is intended to describe. The rhizome is constantly transforming and morphing itself, making it virtually impossible to map out its structure completely at any point in time. This is exactly how wicked problems occur. Wicked, messy problems could, in fact, be described as rhizomatic structures. The rhizome structure applies well to the socio-technical nature of organisations as well, as the dissipative relationships between humans, technology, and organisation structures form a complex, dynamic, and transforming entity with no clear, formal, or necessarily logical order. This rhizomatic relationship is probably best explained in the field of technology adoption and diffusion in private enterprises where traditional positivist approaches to management and innovation struggle to explain how and why technology trends emerge and behave. This reflection on Deleuze leads to the following important claim:

Organisations are complex, dissipative structures constantly transforming complex, human knowledge and social relationships. A rhizomatic systems model satisfies such conception of organisational reality. Hence, Enterprise Architecture, in its search for whole-of-enterprise views, should adopt rhizomatic theory for uncovering and understanding the true messiness of organisations as socio-political habitats.


Understanding Enterprise Architecture as a rhizomatic systems practice, however, must come at the cost of killing certain darlings. The first darling is the idea of organisations as stable structures operating on explicit, verifiable knowledge, which in turn can be divided into clear architectural layers and segments. The second darling is the conception of a universal maturity model explaining the natural progression towards “EA nirvana”. There is no such one.
  1. Layers, segments, and hierarchical models depart from a Westernised, binary view of the world. Layering suggests decomposability and abstraction of organisational complexity. A rhizome does not have such properties. The messy, social facets of organisational life cannot be decomposed or functionally abstracted. The social does not have a single function and thus cannot be functional. Wicked problems, as they emerge from social interactions and organisational problems, are rhizomatic and cannot be explained fully through rationalist models.
  2. Maturity models are inherently binary. They suggest a natural progression towards the optimal stage 5 somewhat similar to a tree as it stretches its branches towards the rising sun. The rhizome is the exact opposite of a tree structure as its roots and shoots grow and form in any direction, shrouding and shifting its original structure. Social structures, apart from the general statistical patterns uncovered by social psychology, do not follow universal laws of transformation or branching—and hence it is impossible and meaningless to suggest a generic, universalistic maturity model of social behaviour in EA adoption and planning. There is no such nirvana of Enterprise Architecture—and if there ever were, it would be constantly shifting and transforming depending on the current managerial climate, problems of planning, and struggle for control inside the organisation. Exactly this relationship of management, planning, and control is rhizomatic as well.

For Enterprise Architecture to fully explain these sacrifices, it must adopt a view of the enterprise as a non-linear, interconnected multiplicity, for which structures can only be meaningfully traced and described in hindsight. Traces always remain interpretations. Enterprise modelling involves tracing organisation structures, but as these structures are traced and interpreted, they suddenly shift and transform into a different multiplicity. Enterprise Architecture is thus a semiotic practice of tracing and interpreting organisations as complex signs. The output, the long-term plans, roadmaps, and meta-models, are merely simplified pictures of these dissipative signs. Only by accepting these aspects of enterprise reality, can Enterprise Architecture truly characterise the challenges and solutions in strategic planning and enterprise management.  

References:
Bertalanffy, L. v. (1969). General System Theory; Foundations, Development, Applications. New York,: G. Braziller.
Deleuze, G., & Guattari, F. (1976). Rhizome : Introduction. Paris: Éditions de Minuit.
Deleuze, G., & Guattari, F. (1988). A Thousand Plateaus : Capitalism and Schizophrenia. London: Athlone Press.
Gadamer, H.-G. (1975). Truth and method. London: Sheed & Ward.
Jensen, A. O. (2010) Government Enterprise Architecture Adoption: A Systemic-Discursive Critique and Reconceptualisation. Copenhagen Business School.
Luhmann, N. (1995). Social Systems. Stanford, Calif.: Stanford University Press.
Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and Cognition : the Realization of the Living. Dordrecht, Holland ; Boston: D. Reidel Pub. Co.
Paulk, M. C. (1995). The Capability Maturity Model : Guidelines for Improving the Software Process. Reading, Mass.: Addison-Wesley Pub. Co.
Rittel, H. & Webber, M. (1973), `Dilemmas in a General Theory of Planning’, Policy Sciences 4.
Von Foerster, H. (2003). Understanding Understanding : Essays on Cybernetics and Cognition. New York: Springer.

Rhizome: On Dilemmas in Enterprise Architecture Planning

In the field of IS and management we often put forward a certain conception of the organisation, the social. In contemporary business consulting and management academia, the organisation is often conceptualised as a hierarchical open system with a certain body of knowledge supplying the management system with rational decision making. Other alternative, academic approaches are influenced by literature studies and Gadamer’s hermeneutics (Gadamer, 1975), promoting the need for understanding and context, the particular, rather than the universal and manageable. Emerging from these two spectra, each fighting for their own conception of subjectivity and objectivity, Anglo-Saxon and continental philosophy, each defining the criteria for truth and meaning, one uncovers systems theory and cybernetics which proposes a model for generalising structures and properties between different phenomena in the world: Bertalanffy (Bertalanffy, 1969) suggests a unified cybernetic model for living, mechanic, and social systems, whereas von Foerster (Von Foerster, 2003) and Luhmann (Luhmann, 1995) suggest a second order model based on constructed observation and interpretation. In organisation studies, first and second order systems theory each postulate their own conception or construction of social reality: Parsons defines the social as actions or events referring to each other within a structural organising of social functions, whereas Luhmann flips the tin can with a functional organisation of social structures based on communication, reproducing and sustaining itself through Maturana and Varela’s concept of autopoiesis (Maturana & Varela, 1980).

Amidst IS management’s–and thus EA’s–attempt to establish a common, trans-disciplinary foundation for research, there appears to be an ontological schism of what the social is and organisations really are. Is it a collective intelligence or logic of rational decision making? Is it a reactive, intersubjective collective attempting to make sense of the world in hindsight through history and culture? Or is it a system or a construction of a system that organises, structures, or communicates through constant adaption and recursive reproduction only by reference to its own recursion and reproductivity? The latter approach dissolves the former two boundaries by creating a boundary of distinction even more important than the understanding subject itself at the edge of every possible system. It is the distinction between system and environment that generates or fabricates meaning and truth, but it comes at the cost of reducing our very own processes of cognition and sensemaking to a set of vibrating antennas or satellites mounted at the fragile surface of every human system.  

An ontology of the social is thus far from complete. Enterprise Architecture (EA) seeks to address this by building layers of abstraction and control, thereby assuming that static systems models of socio-technical relations yield manageability and transparency. Accountability is achieved by linking formal role descriptions to process models and system landscapes, often positioned in a well-defined hierarchy and stored in a database repository for later reference and reuse. In order to reuse ‘best practices’ and assure a certain level of maturity in framework and methodology, enterprises often implement their architecture practice against existing reference frameworks and enterprise meta models. Frameworks such as FEAF even include a CMMI-like maturity model for EA, which assesses the success of architecture program by measures such as completeness and integration. OMB, the US Federal Office of Management and Budget, has furthermore published a set of measures of architectural completeness for evaluating US Federal Agencies. The highest achievement, level 5, is the architectural utopia in which the organisation practicing EA corrects its own business failures by architectural inspection. Architecture is here synonymous with optimising an organisation.


Given the above reflections on what the social really is, is it really philosophically reasonable to suggest that a stable, decomposable, hierarchical model, which most enterprise meta-models really are, is capable of building a comprehensive model of the social? Is it really meaningful to stretch virtually any organisation, be it government or private, along a five-level diagram and measure it by how well-described architectural elements are? And what happens when the Federal agency hits the ceiling after 5? Those are clever and important questions that information and organisation science ought to ask. Unfortunately, that is seldom the case. Maturity models, in the classic form of a five-step ladder, are an inherent part of any contemporary management/IS theory: process maturity, architecture maturity, service maturity, integration maturity. The five-step Capability Maturity Model (Paulk, 1995) has its roots in systems engineering carried out by engineers building space shuttles for NASA. As universal as it may be, the problems, issues, and solutions faced by modern organisations are far more muddy, messy, an ill-defined than those originally faced by defence contractors and DoD bureaucrats. Such fast-paced, deep problems are also characterised as wicked problems (Rittel & Webber, 1973):
  1. Wicked problems have no definitive formulation. One can infer that the problem exists, but will never be able to fully document the problem.
  2. The solution to a wicked problem is “good or bad” not “true or false”.
  3. Every possible solution is a one-shut operation as every solution attempt will leave a trace which cannot be undone.
  4. Each wicked problem is unique and may eventually be the symptom of another, underlying wicked problem.
Through my previous research, I have suggested a systems theoretical approach towards understanding and explaining EA. Systems theory is helpful towards describing the messy complexity of social and communicative structures. Second order systems theory adds a rich, dynamic theory for understanding communication in- and outside organisation by describing the exchange of utterances between human actors in search of meaning (Jensen, 2010). I believe, however, that these two key conceptions of enterprise planning and governance can furthermore be extended into a general theory of EA by including Deleuze’s theory of the rhizome.

Deleuze (Deleuze & Guattari, 1988) describes the rhizome structure (Deleuze & Guattari, 1976) as a meaningful alternative to uncovering complex structures, be they social or biological. Western society, Deleuze explains, has built its historicity and philosophy on the basis of binary structures: true-false, yes-no, top-bottom, maturity-immaturity. Contemporary EA frameworks are, in fact, highly binary: layers separated by clear boundaries, processes with a start and end, structured organisation charts and capability maps with a top and bottom. The rhizome is a viable alternative since it assumes an inherent complexity of what it is intended to describe. The rhizome is constantly transforming and morphing itself, making it virtually impossible to map out its structure completely at any point in time. This is exactly how wicked problems occur. Wicked, messy problems could, in fact, be described as rhizomatic structures. The rhizome structure applies well to the socio-technical nature of organisations as well, as the dissipative relationships between humans, technology, and organisation structures form a complex, dynamic, and transforming entity with no clear, formal, or necessarily logical order. This rhizomatic relationship is probably best explained in the field of technology adoption and diffusion in private enterprises where traditional positivist approaches to management and innovation struggle to explain how and why technology trends emerge and behave. This reflection on Deleuze leads to the following important claim:

Organisations are complex, dissipative structures constantly transforming complex, human knowledge and social relationships. A rhizomatic systems model satisfies such conception of organisational reality. Hence, Enterprise Architecture, in its search for whole-of-enterprise views, should adopt rhizomatic theory for uncovering and understanding the true messiness of organisations as socio-political habitats.


Understanding Enterprise Architecture as a rhizomatic systems practice, however, must come at the cost of killing certain darlings. The first darling is the idea of organisations as stable structures operating on explicit, verifiable knowledge, which in turn can be divided into clear architectural layers and segments. The second darling is the conception of a universal maturity model explaining the natural progression towards “EA nirvana”. There is no such one.
  1. Layers, segments, and hierarchical models depart from a Westernised, binary view of the world. Layering suggests decomposability and abstraction of organisational complexity. A rhizome does not have such properties. The messy, social facets of organisational life cannot be decomposed or functionally abstracted. The social does not have a single function and thus cannot be functional. Wicked problems, as they emerge from social interactions and organisational problems, are rhizomatic and cannot be explained fully through rationalist models.
  2. Maturity models are inherently binary. They suggest a natural progression towards the optimal stage 5 somewhat similar to a tree as it stretches its branches towards the rising sun. The rhizome is the exact opposite of a tree structure as its roots and shoots grow and form in any direction, shrouding and shifting its original structure. Social structures, apart from the general statistical patterns uncovered by social psychology, do not follow universal laws of transformation or branching—and hence it is impossible and meaningless to suggest a generic, universalistic maturity model of social behaviour in EA adoption and planning. There is no such nirvana of Enterprise Architecture—and if there ever were, it would be constantly shifting and transforming depending on the current managerial climate, problems of planning, and struggle for control inside the organisation. Exactly this relationship of management, planning, and control is rhizomatic as well.

For Enterprise Architecture to fully explain these sacrifices, it must adopt a view of the enterprise as a non-linear, interconnected multiplicity, for which structures can only be meaningfully traced and described in hindsight. Traces always remain interpretations. Enterprise modelling involves tracing organisation structures, but as these structures are traced and interpreted, they suddenly shift and transform into a different multiplicity. Enterprise Architecture is thus a semiotic practice of tracing and interpreting organisations as complex signs. The output, the long-term plans, roadmaps, and meta-models, are merely simplified pictures of these dissipative signs. Only by accepting these aspects of enterprise reality, can Enterprise Architecture truly characterise the challenges and solutions in strategic planning and enterprise management.  

References:
Bertalanffy, L. v. (1969). General System Theory; Foundations, Development, Applications. New York,: G. Braziller.
Deleuze, G., & Guattari, F. (1976). Rhizome : Introduction. Paris: Éditions de Minuit.
Deleuze, G., & Guattari, F. (1988). A Thousand Plateaus : Capitalism and Schizophrenia. London: Athlone Press.
Gadamer, H.-G. (1975). Truth and method. London: Sheed & Ward.
Jensen, A. O. (2010) Government Enterprise Architecture Adoption: A Systemic-Discursive Critique and Reconceptualisation. Copenhagen Business School.
Luhmann, N. (1995). Social Systems. Stanford, Calif.: Stanford University Press.
Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and Cognition : the Realization of the Living. Dordrecht, Holland ; Boston: D. Reidel Pub. Co.
Paulk, M. C. (1995). The Capability Maturity Model : Guidelines for Improving the Software Process. Reading, Mass.: Addison-Wesley Pub. Co.
Rittel, H. & Webber, M. (1973), `Dilemmas in a General Theory of Planning’, Policy Sciences 4.
Von Foerster, H. (2003). Understanding Understanding : Essays on Cybernetics and Cognition. New York: Springer.

Selling Federal Enterprise Architecture (EA)

Selling Federal Enterprise Architecture

A taxonomy of subject areas, from which to develop a prioritized marketing and communications plan to evangelize EA activities within and among US Federal Government organizations and constituents.

Any and all feedback is appreciated, particularly in developing and extending this discussion as a tool for use – more information and details are also available.
“Selling” the discipline of Enterprise Architecture (EA) in the Federal Government (particularly in non-DoD agencies) is difficult, notwithstanding the general availability and use of the Federal Enterprise Architecture Framework (FEAF) for some time now, and the relatively mature use of the reference models in the OMB Capital Planning and Investment (CPIC) cycles. EA in the Federal Government also tends to be a very esoteric and hard to decipher conversation – early apologies to those who agree to continue reading this somewhat lengthy article.

Alignment to the FEAF and OMB compliance mandates is long underway across the Federal Departments and Agencies (and visible via tools like PortfolioStat and ITDashboard.gov – but there is still a gap between the top-down compliance directives and enablement programs, and the bottom-up awareness and effective use of EA for either IT investment management or actual mission effectiveness. “EA isn’t getting deep enough penetration into programs, components, sub-agencies, etc.”, verified a panelist at the most recent EA Government Conference in DC.

Newer guidance from OMB may be especially difficult to handle, where bottom-up input can’t be accurately aligned, analyzed and reported via standardized EA discipline at the Agency level – for example in addressing the new (for FY13) Exhibit 53D “Agency IT Reductions and Reinvestments” and the information required for “Cloud Computing Alternatives Evaluation” (supporting the new Exhibit 53C, “Agency Cloud Computing Portfolio”).

Therefore, EA must be “sold” directly to the communities that matter, from a coordinated, proactive messaging perspective that takes BOTH the Program-level value drivers AND the broader Agency mission and IT maturity context into consideration.

Selling EA means persuading others to take additional time and possibly assign additional resources, for a mix of direct and indirect benefits – many of which aren’t likely to be realized in the short-term. This means there’s probably little current, allocated budget to work with; ergo the challenge of trying to sell an “unfunded mandate”.

Also, the concept of “Enterprise” in large Departments like Homeland Security tends to cross all kinds of organizational boundaries – as Richard Spires recently indicated by commenting that “…organizational boundaries still trump functional similarities. Most people understand what we’re trying to do internally, and at a high level they get it. The problem, of course, is when you get down to them and their system and the fact that you’re going to be touching them…there’s always that fear factor,” Spires said.

It is quite clear to the Federal IT Investment community that for EA to meet its objective, understandable, relevant value must be measured and reported using a repeatable method – as described by GAO’s recent report “Enterprise Architecture Value Needs To Be Measured and Reported“.

What’s not clear is the method or guidance to sell this value. In fact, the current GAO “Framework for Assessing and Improving Enterprise Architecture Management (Version 2.0)”, a.k.a. the “EAMMF”, does not include words like “sell”, “persuade”, “market”, etc., except in reference (“within Core Element 19: Organization business owner and CXO representatives are actively engaged in architecture development”) to a brief section in the CIO Council’s 2001 “Practical Guide to Federal Enterprise Architecture”, entitled “3.3.1. Develop an EA Marketing Strategy and Communications Plan.” Furthermore, Core Element 19 of the EAMMF is advised to be applied in “Stage 3: Developing Initial EA Versions”. This kind of EA sales campaign truly should start much earlier in the maturity progress, i.e. in Stages 0 or 1.

So, what are the understandable, relevant benefits (or value) to sell, that can find an agreeable, participatory audience, and can pave the way towards success of a longer-term, funded set of EA mechanisms that can be methodically measured and reported? Pragmatic benefits from a useful EA that can help overcome the fear of change? And how should they be sold?

Following is a brief taxonomy (it’s a taxonomy, to help organize SME support) of benefit-related subjects that might make the most sense, in creating the messages and organizing an initial “engagement plan” for evangelizing EA “from within”. An EA “Sales Taxonomy” of sorts. We’re not boiling the ocean here; the subjects that are included are ones that currently appear to be urgently relevant to the current Federal IT Investment landscape.

Note that successful dialogue in these topics is directly usable as input or guidance for actually developing early-stage, “Fit-for-Purpose” (a DoDAF term) Enterprise Architecture artifacts, as prescribed by common methods found in most EA methodologies, including FEAF, TOGAF, DoDAF and our own Oracle Enterprise Architecture Framework (OEAF).

The taxonomy below is organized by (1) Target Community, (2) Benefit or Value, and (3) EA Program Facet – as in:

“Let’s talk to (1: Community Member) about how and why (3: EA Facet) the EA program can help with (2: Benefit/Value)”.
Once the initial discussion targets and subjects are approved (that can be measured and reported), a “marketing and communications plan” can be created.

A working example follows the Taxonomy.

Enterprise Architecture Sales Taxonomy
Draft, Summary Version
1. Community

1.1. Budgeted Programs or Portfolios
Communities of Purpose (CoPR)
1.1.1. Program/System Owners (Senior Execs) Creating or Executing Acquisition Plans

1.1.2. Program/System Owners Facing Strategic Change
1.1.2.1. Mandated
1.1.2.2. Expected/Anticipated

1.1.3. Program Managers – Creating Employee Performance Plans
1.1.4. CO/COTRs – Creating Contractor Performance Plans, or evaluating Value Engineering Change Proposals (VECP)

1.2. Governance & Communications
Communities of Practice (CoP)

1.2.1. Policy Owners
1.2.1.1. OCFO
1.2.1.1.1. Budget/Procurement Office
1.2.1.1.2. Strategic Planning

1.2.1.2. OCIO
1.2.1.2.1. IT Management
1.2.1.2.2. IT Operations
1.2.1.2.3. Information Assurance (Cyber Security)
1.2.1.2.4. IT Innovation

1.2.1.3. Information-Sharing/ Process Collaboration (i.e. policies and procedures regarding Partners, Agreements)

1.2.2. Governing IT Council/SME Peers (i.e. an “Architects Council”)
1.2.2.1. Enterprise Architects (assumes others exist; also assumes EA participants aren’t buried solely within the CIO shop)
1.2.2.2. Domain, Enclave, Segment Architects – i.e. the right affinity group for a “shared services” EA structure (per the EAMMF), which may be classified as Federated, Segmented, Service-Oriented, or Extended

1.2.2.3. External Oversight/Constraints
1.2.2.3.1. GAO/OIG & Legal
1.2.2.3.2. Industry Standards
1.2.2.3.3. Official public notification, response

1.2.3. Mission Constituents
Participant & Analyst Community of Interest (CoI)

1.2.3.1. Mission Operators/Users
1.2.3.2. Public Constituents
1.2.3.3. Industry Advisory Groups, Stakeholders
1.2.3.4. Media

2. Benefit/Value
(Note the actual benefits may not be discretely attributable to EA alone; EA is a very collaborative, cross-cutting discipline.)

2.1. Program Costs – EA enables sound decisions regarding…
2.1.1. Cost Avoidance – a TCO theme
2.1.2. Sequencing – alignment of capability delivery
2.1.3. Budget Instability – a Federal reality

2.2. Investment Capital – EA illuminates new investment resources via…
2.2.1. Value Engineering – contractor-driven cost savings on existing budgets, direct or collateral
2.2.2. Reuse – reuse of investments between programs can result in savings, chargeback models; avoiding duplication
2.2.3. License Refactoring – IT license & support models may not reflect actual or intended usage

2.3. Contextual Knowledge – EA enables informed decisions by revealing…
2.3.1. Common Operating Picture (COP) – i.e. cross-program impacts and synergy, relative to context
2.3.2. Expertise & Skill – who truly should be involved in architectural decisions, both business and IT
2.3.3. Influence – the impact of politics and relationships can be examined
2.3.4. Disruptive Technologies – new technologies may reduce costs or mitigate risk in unanticipated ways
2.3.5. What-If Scenarios – can become much more refined, current, verifiable; basis for Target Architectures

2.4. Mission Performance – EA enables beneficial decision results regarding…
2.4.1. IT Performance and Optimization – towards 100% effective, available resource utilization
2.4.2. IT Stability – towards 100%, real-time uptime
2.4.3. Agility – responding to rapid changes in mission
2.4.4. Outcomes –measures of mission success, KPIs – vs. only “Outputs”
2.4.5. Constraints – appropriate response to constraints
2.4.6. Personnel Performance – better line-of-sight through performance plans to mission outcome

2.5. Mission Risk Mitigation – EA mitigates decision risks in terms of…
2.5.1. Compliance – all the right boxes are checked
2.5.2. Dependencies –cross-agency, segment, government
2.5.3. Transparency – risks, impact and resource utilization are illuminated quickly, comprehensively
2.5.4. Threats and Vulnerabilities – current, realistic awareness and profiles

2.5.5. Consequences – realization of risk can be mapped as a series of consequences, from earlier decisions or new decisions required for current issues
2.5.5.1. Unanticipated – illuminating signals of future or non-symmetric risk; helping to “future-proof”
2.5.5.2. Anticipated – discovering the level of impact that matters

3. EA Program Facet
(What parts of the EA can and should be communicated, using business or mission terms?)

3.1. Architecture Models – the visual tools to be created and used
3.1.1. Operating Architecture – the Business Operating Model/Architecture elements of the EA truly drive all other elements, plus expose communication channels

3.1.2. Use Of – how can the EA models be used, and how are they populated, from a reasonable, pragmatic yet compliant perspective? What are the core/minimal models required? What’s the relationship of these models, with existing system models?

3.1.3. Scope – what level of granularity within the models, and what level of abstraction across the models, is likely to be most effective and useful?

3.2. Traceability – the maturity, status, completeness of the tools
3.2.1. Status – what in fact is the degree of maturity across the integrated EA model and other relevant governance models, and who may already be benefiting from it?

3.2.2. Visibility – how does the EA visibly and effectively prove IT investment performance goals are being reached, with positive mission outcome?

3.3. Governance – what’s the interaction, participation method; how are the tools used?
3.3.1. Contributions – how is the EA program informed, accept submissions, collect data? Who are the experts?

3.3.2. Review – how is the EA validated, against what criteria?

 Taxonomy Usage Example:

 

1. To speak with:
a. …a particular set of System Owners Facing Strategic Change, via mandate (like the “Cloud First” mandate); about…
b. …how the EA program’s visible and easily accessible Infrastructure Reference Model (i.e. “IRM” or “TRM”), if updated more completely with current system data, can…
c. …help shed light on ways to mitigate risks and avoid future costs associated with NOT leveraging potentially-available shared services across the enterprise…
2. ….the following Marketing & Communications (Sales) Plan can be constructed:
a. Create an easy-to-read “Consequence Model” that illustrates how adoption of a cloud capability (like elastic operational storage) can enable rapid and durable compliance with the mandate – using EA traceability. Traceability might be from the IRM to the ARM (that identifies reusable services invoking the elastic storage), and then to the PRM with performance measures (such as % utilization of purchased storage allocation) included in the OMB Exhibits; and
b. Schedule a meeting with the Program Owners, timed during their Acquisition Strategy meetings in response to the mandate, to use the “Consequence Model” for advising them to organize a rapid and relevant RFI solicitation for this cloud capability (regarding alternatives for sourcing elastic operational storage); and
c. Schedule a series of short “Discovery” meetings with the system architecture leads (as agreed by the Program Owners), to further populate/validate the “As-Is” models and frame the “To Be” models (via scenarios), to better inform the RFI, obtain the best feedback from the vendor community, and provide potential value for and avoid impact to all other programs and systems.
–end example —

Note that communications with the intended audience should take a page out of the standard “Search Engine Optimization” (SEO) playbook, using keywords and phrases relating to “value” and “outcome” vs. “compliance” and “output”. Searches in email boxes, internal and external search engines for phrases like “cost avoidance strategies”, “mission performance metrics” and “innovation funding” should yield messages and content from the EA team.

This targeted, informed, practical sales approach should result in additional buy-in and participation, additional EA information contribution and model validation, development of more SMEs and quick “proof points” (with real-life testing) to bolster the case for EA. The proof point here is a successful, timely procurement that satisfies not only the external mandate and external oversight review, but also meets internal EA compliance/conformance goals and therefore is more transparently useful across the community.

In short, if sold effectively, the EA will perform and be recognized. EA won’t therefore be used only for compliance, but also (according to a validated, stated purpose) to directly influence decisions and outcomes.

The opinions, views and analysis expressed in this document are those of the author and do not necessarily reflect the views of Oracle.

An Actionable Common Approach to Federal Enterprise Architecture

The recent “Common Approach to Federal Enterprise Architecture” (US Executive Office of the President, May 2 2012) is extremely timely and well-organized guidance for the Federal IT investment and deployment community, as useful for Federal Departments and Agencies as it is for their stakeholders and integration partners. The guidance not only helps IT Program Planners and Managers, but also informs and prepares constituents who may be the beneficiaries or otherwise impacted by the investment. The FEA Common Approach extends from and builds on the rapidly-maturing Federal Enterprise Architecture Framework (FEAF) and its associated artifacts and standards, already included to a large degree in the annual Federal Portfolio and Investment Management processes – for example the OMB’s Exhibit 300 (i.e. Business Case justification for IT investments).

A very interesting element of this Approach includes the very necessary guidance for actually using an Enterprise Architecture (EA) and/or its collateral – good guidance for any organization charged with maintaining a broad portfolio of IT investments. The associated FEA Reference Models (i.e. the BRM, DRM, TRM, etc.) are very helpful frameworks for organizing, understanding, communicating and standardizing across agencies with respect to vocabularies, architecture patterns and technology standards. Determining when, how and to what level of detail to include these reference models in the typically long-running Federal IT acquisition cycles wasn’t always clear, however, particularly during the first interactions of a Program’s technical and functional leadership with the Mission owners and investment planners. This typically occurs as an agency begins the process of describing its strategy and business case for allocation of new Federal funding, reacting to things like new legislation or policy, real or anticipated mission challenges, or straightforward ROI opportunities (for example the introduction of new technologies that deliver significant cost-savings).

The early artifacts (i.e. Resource Allocation Plans, Acquisition Plans, Exhibit 300’s or other Business Case materials, etc.) of the intersection between Mission owners, IT and Program Managers are far easier to understand and discuss, when the overlay of an evolved, actionable Enterprise Architecture (such as the FEA) is applied.  “Actionable” is the key word – too many Public Service entity EA’s (including the FEA) have for too long been used simply as a very highly-abstracted standards reference, duly maintained and nominally-enforced by an Enterprise or System Architect’s office.

Refreshing elements of this recent FEA Common Approach include one of the first Federally-documented acknowledgements of the “Solution Architect” (the “Problem-Solving” role). This role collaborates with the Enterprise, System and Business Architecture communities primarily on completing actual “EA Roadmap” documents. These are roadmaps grounded in real cost, technical and functional details that are fully aligned with both contextual expectations (for example the new “Digital Government Strategy” and its required roadmap deliverables – and the rapidly increasing complexities of today’s more portable and transparent IT solutions.  We also expect some very critical synergies to develop in early IT investment cycles between this new breed of “Federal Enterprise Solution Architect” and the first waves of the newly-formal “Federal IT Program Manager” roles operating under more standardized “critical competency” expectations (including EA), likely already to be seriously influencing the quality annual CPIC (Capital Planning and Investment Control) processes. 

Our Oracle Enterprise Strategy Team (EST) and associated Oracle Enterprise Architecture (OEA) practices are already engaged in promoting and leveraging the visibility of Enterprise Architecture as a key contributor to early IT investment validation, and we look forward in particular to seeing the real, citizen-centric benefits of this FEA Common Approach in particular surface across the entire Public Service CPIC domain – Federal, State, Local, Tribal and otherwise. Read more Enterprise Architecture blog posts for additional EA insight!

The EA Body of Voices

What have been the most important things in enterprise architecture throughout 2013? I am sure anything you can come up with have been covered by the EA blogger community. So, to get an overview of what has been happening in 2013 in EA, take a look at the 2013 archives of EA Voices. With an average …

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Glossary

A collection of Enterprise Architecture terms and definitions from a variety of sources: EA3 Cube, Introduction to Enterprise Architecture, The Common Approach to Federal Enterprise Architecture (FEAF-II), ISO 42010:2011 and TOGAF 9. 00