Why IoT is changing Enterprise Architecture

[tl;dr The discipline of enterprise architecture as an aid to business strategy execution has failed for most organizations, but is finding a new lease of life in the Internet of Things.]

The strategic benefits of having an enterprise-architecture based approach to organizational change – at least in terms of business models and shared capabilities needed to support those models – have been the subject of much discussion in recent years.

However, enterprise architecture as a practice (as espoused by The Open Group and others) has never managed to break beyond it’s role as an IT-focused endeavor.

In the meantime, less technology-minded folks are beginning to discuss business strategy using terms like ‘modularity’, which is a first step towards bridging the gap between the business folks and the technology folks. And technology-minded folks are looking at disruptive business strategy through the lens of the ‘Internet of Things‘.

Business Model Capability Decomposition

Just like manufacturing-based industries decomposed their supply-chains over the past 30+ years (driving an increasingly modular view of manufacturing), knowledge-based industries are going through a similar transformation.

Fundamentally, knowledge based industries are based on the transfer and management of human knowledge or understanding. So, for example, you pay for something, there is an understanding on both sides that that payment has happened. Technology allows such information to be captured and managed at scale.

But the ‘units’ of knowledge have only slowly been standardized, and knowledge-based organizations are incentivized to ensure they are the only ones to be able to act on the information they have gathered – to often disastrous social and economic consequences (e.g., the financial crisis of 2008).

Hence, regulators are stepping into to ensure that at least some of this ‘knowledge’ is available in a form that allows governments to ensure such situations do not arise again.

In the FinTech world, every service provided by big banks is being attacked by nimble competitors able to take advantage of new, more meaningful technology-enabled means of engaging with customers, and who are willing to make at least some of this information more accessible so that they can participate in a more flexible, dynamic ecosystem.

For these upstart FinTech firms, they often have a stark choice to make in order to succeed. Assuming they have cleared the first hurdle of actually having a product people want, at some point, they must decide whether they are competing directly with the big banks, or if they are providing a key part of the electronic financial knowledge ecosystem that big banks must (eventually) be part of.

In the end, what matters is their approach to data: how they capture it (i.e., ‘UX’), what they do with it, how they manage it, and how it is leveraged for competitive and commercial advantage (without falling foul of privacy laws etc). Much of the rest is noise from businesses trying to get attention in an increasingly crowded space.

Historically, many ‘enterprise architecture’ or strategy departments fail to have impact because firms do not treat data (or information, or knowledge) as an asset, but rather as something to be freely and easily created and shunted around, leaving a trail of complexity and lost opportunity cost wherever it goes. So this attitude must change before ‘enterprise architecture’ as a concept will have a role in boardroom discussions, and firms change how they position IT in their business strategy. (Regulators are certainly driving this for certain sectors like finance and health.)

Internet of Things

Why does the Internet Of Things (IoT) matter, and where does IoT fit into all this?

At one level, IoT presents a large opportunity for firms which see the potential implied by the technologies underpinning IoT; the technology can provide a significant level of convenience and safety to many aspects of a modern, digitally enabled life.

But fundamentally, IoT is about having a large number of autonomous actors collaborating in some way to deliver a particular service, which is of value to some set of interested stakeholders.

But this sounds a lot like what a ‘company’ is. So IoT is, in effect, a company where the actors are technology actors rather than human actors. They need some level of orchestration. They need a common language for communication. And they need laws/protocols that govern what’s permitted and what is not.

If enterprise architecture is all about establishing the functional, data and protocol boundaries between discrete capabilities within an organization, then EA for IoT is the same thing but for technical components, such as sensors or driverless cars, etc.

So IoT seems a much more natural fit for EA thinking than traditional organizations, especially as, unlike departments in traditional ‘human’ companies, technical components like standards: they like fixed protocols, fixed functional boundaries and well-defined data sets. And while the ‘things’ themselves may not be organic, their behavior in such an environment could exhibit ‘organic’ characteristics.

So, IoT and and the benefits of an enterprise architecture-oriented approach to business strategy do seem like a match made in heaven.

The Converged Enterprise

For information-based industries in particular, there appears to be an inevitable convergence: as IoT and the standards, protocols and governance underpinning it mature, so too will the ‘modular’ aspects of existing firms operating models, and the eco-system of technology-enabled platforms will mature along with it. Firms will be challenged to deliver value by picking the most capable components in the eco-system around which to deliver unique service propositions – and the most successful of those solutions will themselves become the basis for future eco-systems (a Darwinian view of software evolution, if you will).

The converged enterprise will consist of a combination of human and technical capabilities collaborating in well-defined ways. Some capabilities will be highly human, others highly technical, some will be in-house, some will be part of a wider platform eco-system.

In such an organization, enterprise architects will find a natural home. In the meantime, enterprise architects must choose their starting point, behavioral or structural: focusing first on decomposed business capabilities and finishing with IoT (behavioral->structural), or focusing first on IoT and finishing with business capabilities (structural->behavioral).

Technical Footnote

I am somewhat intrigued at how the OSGi Alliance has over the years shifted its focus from basic Java applications, to discrete embedded systems, to enterprise systems and now to IoT. OSGi, (disappointingly, IMO), has had a patchy record changing how firms build enterprise software – much of this is to do with a culture of undisciplined dependency management in the software industry which is very, very hard to break.

IoT raises the bar on dependency management: you simply cannot comprehensively test software updates to potentially hundreds of thousands or millions of components running that software. The ability to reliably change modules without forcing a test of all dependent instantiated components is a necessity. As enterprises get more complex and digitally interdependent, standards such as OSGi will become more critical to the plumbing of enabling technologies. But as evidenced above, for folks who have tried and failed to change how enterprises treat their technology-enabled business strategy, it’s a case of FIETIOT – Failed in Enterprise, Trying IoT. And this, indeed, seems a far more rational use of an enterprise architect’s time, as things currently stand.

 

 

 

 

 

 

Why IoT is changing Enterprise Architecture

Transforming IT: From a solution-driven model to a capability-driven model

[tl;dr Moving from a solution-oriented to a capability-oriented model for software development is necessary to enable enterprises to achieve agility, but has substantial impacts on how enterprises organise themselves to support this transition.]

Most organisations which manage software change as part of their overall change portfolio take a project-oriented approach to delivery: the project goals are set up front, and a solution architecture and delivery plan are created in order to achieve the project goals.

Most organisations also fix project portfolios on a yearly basis, and deviating from this plan can often very difficult for organisations to cope with – at least partly because such plans are intrinsically tied into financial planning and cost-saving techniques such as capitalisation of expenses, etc, which reduce bottom-line cost to the firm of the investment (even if it says nothing about the value added).

As the portfolio of change projects rise every year, due to many extraneous factors (business opportunities, revenue protection, regulatory demand, maintenance, exploration, digital initiatives,  etc), cross-project dependency management becomes increasingly difficult. It becomes even more complex to manage solution architecture dependencies within that overall dependency framework.

What results is a massive set of compromises that ends up with building solutions that are sub-optimal for pretty much every project, and an investment in technology that is so enterprise-specific, that no other organisation could possibly derive any significant value from it.

While it is possible that even that sub-optimal technology can yield significant value to the organisation as a whole, this benefit may be short lived, as the cost-effective ability to change the architecture must inevitably decrease over time, reducing agility and therefore the ability to compete.

So a balance needs to be struck, between delivering enterprise value (even at the expense of individual projects) while maintaining relative technical and business agility. By relative I mean relative to peers in the same competitive sector…sectors which are themselves being disrupted by innovative technology firms which are very specialist and agile within their domain.

The concept of ‘capabilities’ realised through technology ‘products’, in addition to the traditional project/program management approach, is key to this. In particular, it recognises the following key trends:

  • Infrastructure- and platform-as-a-service
  • Increasingly tech-savvy work-force
  • Increasing controls on IT by regulators, auditors, etc
  • Closer integration of business functions led by ‘digital’ initiatives
  • The replacement of the desktop by mobile & IoT (Internet of Things)
  • The tension between innovation and standards in large organisations

Enterprises are adapting to all the above by recognising that the IT function cannot be responsible for both technical delivery and ensuring that all technology-dependent initiatives realise the value they were intended to realise.

As a result, many aspects of IT project and programme management are no longer driven out of the ‘core’ IT function, but by domain-specific change management functions. IT itself must consolidate its activities to focus on those activities that can only be performed by highly qualified and expert technologists.

The inevitable consequence of this transformation is that IT becomes more product driven, where a given product may support many projects. As such, IT needs to be clear on how to govern change for that product, to lead it in a direction that is most appropriate for the enterprise as a whole, and not just for any particular project or business line.

A product must provide capabilities to the stakeholders or users of that product. In the past, those capabilities were entirely decided by whatever IT built and delivered: if IT delivered something that in practice wasn’t entirely fit for purpose, then business functions had no alternative but to find ways to work around the system deficiencies – usually creating more complexity (through end-user-developed applications in tools like Excel etc) and more expense (through having to hire more people).

By taking a capability-based approach to product development, however, IT can give business functions more options and ways to work around inevitable IT shortfalls without compromising controls or data integrity – e.g., through controlled APIs and services, etc.

So, while solutions may explode in number and complexity, the number of products can be controlled – with individual businesses being more directly accountable for the complexity they create, rather than ‘IT’.

This approach requires a step-change in how traditional IT organisations manage change. Techniques from enterprise architecture, scaled agile, and DevOps are all key enablers for this new model of structuring the IT organisation.

In particular, except for product-strategy (where IT must be the leader), IT must get out of the business of deciding the relative value/importance of individual product changes requested by projects, which historically IT has been required to do. By imposing a governance structure to control the ‘epics’ and ‘stories’ that drive product evolution, projects and stakeholders have some transparency into when the work they need will be done, and demand can be balanced fairly across stakeholders in accordance with their ability to pay.

If changes implemented by IT do not end up delivering value, it should not be because IT delivered the wrong thing, but rather the right thing was delivered for the wrong reason. As long as IT maintains its product roadmap and vision, such mis-steps can be tolerated. But they cannot be tolerated if every change weakens the ability of the product platform to change.

Firms which successfully balance between the project and product view of their technology landscape will find that productivity increases, complexity is reduced and agility increases massively. This model also lends itself nicely to bounded domain development, microservices, use of container technologies and automated build/deployment – all of which will likely feature strongly in the enterprise technology platform of the future.

The changes required to support this are significant..in terms of financial governance, delivery oversight, team collaborations, and the roles of senior managers and leaders. But organisations must be prepared to do this transition, as historical approaches to enterprise IT software development are clearly unsustainable.

Transforming IT: From a solution-driven model to a capability-driven model

Scaled Agile needs Slack

[tl;dr In order to effectively scale agile, organisations need to ensure that a portion of team capacity is explicitly set aside for enterprise priorities. A re-imagined enterprise architecture capability is a key factor in enabling scaled agile success.]

What’s the problem?

From an architectural perspective, Agile methodologies are heavily dependent on business- (or function-) aligned product owners, which tend to be very focused on *their* priorities – and not the enterprise’s priorities (i.e., other functions or businesses that may benefit from the work the team is doing).

This results in very inward-focused development, and where dependencies on other parts of the organisation are identified, these (in the absence of formal architecture governance) tend to be minimised where possible, if necessary through duplicative development. And other teams requiring access to the team’s resources (e.g., databases, applications, etc) are served on a best-effort basis – often causing those teams to seek other solutions instead, or work without formal support from the team they depend on.

This, of course, leads to architectural complexity, leading to reduced agility all round.

The Solution?

If we accept the premise that, from an architectural perspective, teams are the main consideration (it is where domain and technical knowledge resides), then the question is how to get the right demand to the right teams, in as scalable, agile manner as possible?

In agile teams, the product backlog determines their work schedule. The backlog usually has a long list of items awaiting prioritisation, and part of the Agile processes is to be constantly prioritising this backlog to ensure high-value tasks are done first.

Well known management research such as The Mythical Man Month has influenced Agile’s goal to keep team sizes small (e.g., 5-9 people for scrum). So when new work comes, adding people is generally not a scalable option.

So, how to reconcile the enterprise’s needs with the Agile team’s needs?

One approach would be to ensure that every team pays an ‘enterprise’ tax – i.e., in prioritising backlog items, at least, say, 20% of work-in-progress items must be for the benefit of other teams. (Needless to say, such work should be done in such a way as to preserve product architectural integrity.)

20% may seem like a lot – especially when there is so much work to be done for immediate priorities – but it cuts both ways. If *every* team allows 20% of their backlog to be for other teams, then every team has the possibility of using capacity from other teams – in effect, increasing their capacity by much more than they could do on their own. And by doing so they are helping achieve enterprise goals, reducing overall complexity and maximising reuse – resulting in a reduction in project schedule over-runs, higher quality resulting architecture, and overall reduced cost of change.

Slack does not mean Under-utilisation

The concept of ‘Slack’ is well described in the book ‘Slack: Getting Past Burn-out, Busywork, and the Myth of Total Efficiency‘. In effect, in an Agile sense, we are talking about organisational slack, and not team slack. Teams, in fact, will continue to be 100% utilised, as long as their backlog consists of more high-value items then they can deliver. The backlog owner – e.g., scrum master – can obviously embed local team slack into how a particular team’s backlog is managed.

Implications for Project & Financial Management

Project managers are used to getting funding to deliver a project, and then to be able to bring all those resources to bear to deliver that project. The problem is, that is neither agile, nor does it scale – in an enterprise environment, it leads to increasingly complex architectures, resulting in projects getting increasingly more expensive, increasingly late, or delivering increasingly poor quality.

It is difficult for a project manager to accept that 20% of their budget may actually be supporting other (as yet unknown) projects. So perhaps the solution here is to have Enterprise Architecture account for the effective allocation of that spending in an agile way? (i.e., based on how teams are prioritising and delivering those enterprise items on their backlog). An idea worth considering..

Note that the situation is a little different for planned cross-business initiatives, where product owners must actively prioritise the needs of those initiatives alongside their local needs. Such planned work does not count in the 20% enterprise allowance, but rather counts as part of how the team’s cost to the enterprise is formally funded. It may result in a temporary increase in resources on the team, but in this case discipline around ‘staff liquidity’ is required to ensure the team can still function optimally after the temporary resource boost has gone.

The challenge regarding project-oriented financial planning is that, once a project’s goals have been achieved, what’s left is the team and underlying architecture – both of which need to be managed over time. So some dissociation between transitory project goals and longer term team and architecture goals is necessary to manage complexity.

For smaller, non-strategic projects – i.e., no incoming enterprise dependencies – the technology can be maintained on a lights-on basis.

Enterprise architecture can be seen as a means to asses the relevance of a team’s work to the enterprise – i.e., managing both incoming and outgoing team dependencies.  The higher the enterprise relevance of the team, the more critical the team must be managed well over time – i.e., team structure changes must be carefully managed, and not left entirely to the discretion of individual managers.

Conclusion

By ensuring that every project that purports to be Agile has a mandatory allowance for enterprise resource requirements, teams can have confidence that there is a route for them to get their dependencies addressed through other agile teams, in a manner that is independent of annual budget planning processes or short-term individual business priorities.

The effectiveness of this approach can be governed and evaluated by Enterprise Architecture, which would then allow enterprise complexity goals to be addressed without concentrating such spending within the central EA function.

In summary, to effectively scale agile, an effective (and possibly rethought) enterprise architecture capability is needed.

Scaled Agile needs Slack

Making good architectural moves

[tl;dr In Every change is an opportunity to make the ‘right’ architectural move to improve complexity management and to maintain an acceptable overall cost of change.]

Accompanying every new project, business requirement or product feature is an implicit or explicit ‘architectural move’ – i.e., a change to your overall architecture that moves it from a starting state to another (possibly interim) state.

The art of good architecture is making the ‘right’ architectural moves over time. The art of enterprise architecture is being able to effectively identify and communicate what a ‘right’ move actually looks like from an enterprise perspective, rather than leaving such decisions solely to the particular implementation team – who, it must be assumed, are best qualified to identify the right move from the perspective of the relevant domain.

The challenge is the limited inputs that enterprise architects have, namely:

  • Accumulated skill/knowledge/experience from past projects, including any architectural artefacts
  • A view of the current enterprise priorities based on the portfolio of projects underway
  • A corporate strategy and (ideally) individual business strategies, including a view of the environment the enterprise operates in (e.g., regulatory, commercial, technological, etc)

From these inputs, architects need to guide the overall architecture of the enterprise to ensure every project or deliverable results in a ‘good’ move – or at least not a ‘bad’ move.

In this situation, it is difficult if not impossible to measure the actual success of an architecture capability. This is because, in many respects, the beneficiaries of a ‘good’ enterprise architecture (at least initially) are the next deliverables (projects/requirements/features), and only rarely the current deliverables.

Since the next projects to be done is generally an unknown (i.e., the business situation may change between the time the current projects complete and the time the next projects start), it is rational for people to focus exclusively on delivering the current projects. Which makes it even more important the current projects are in some way delivering the ‘right’ architectural moves.

In many organisations, the typical engagement with enterprise architecture is often late in the architectural development process – i.e., at a ‘toll-gate’ or formal architectural review. And the focus is often on ‘compliance’ with enterprise standards, principles and guidelines. Given that such guidelines can get quite detailed, it can get quite difficult for anyone building a project start architecture (PSA) to come up with an architecture that will fully comply: the first priority is to develop an architecture that will work, and is feasible to execute within the project constraints of time, budget and resources.

Only then does it make sense to formally apply architectural constraints from enterprise architecture – at least some of which may negatively impact the time, cost, resource or feasibility needs of the project – albeit to the presumed benefit of the overall landscape. Hence the need for board-level sponsorship for such reviews, as otherwise the project’s needs will almost always trump enterprise needs.

The approach espoused by an interesting new book, Chess and the Art of Enterprise Architecture, is that enterprise architects need to focus more on design and less on principles, guidelines, roadmaps, etc. Such an approach involves enterprise architects more closely in the creation and evolution of project (start) architectures, which represents the architectural basis for all the work the project does (although it does not necessarily lay out the detailed solution architecture).

This approach is also acceptable for planning processes which are more agile than waterfall. In particular, it acknowledges that not every architectural ‘move’ is necessarily independently ‘usable’ by end users of an agile process. In fact, some user stories may require several architectural moves to fully implement. The question is whether the user story is itself validated enough to merit doing the architectural moves necessary to enable it, as otherwise those architectural moves may be premature.

The alternative, then, is to ‘prototype’ the user story,  so users can evaluate it – but at the cost of non-conformance with the project architecture. This is also known as ‘technical debt’, and where teams are mature and disciplined enough to pay down technical debt when needed, it is a good approach. But users (and sometimes product owners) struggle to tell the difference between an (apparently working) prototype and a production solution that is fully architecturally compliant, and it often happens that project teams move on to the next visible deliverable without removing the technical debt.

In applications where the end-user is a person or set of persons, this may be acceptable in the short term, but where the end-user could be another application (interacting via, for example, an API invoked by either a GUI or an automated process), then such technical debt will likely cause serious problems if not addressed. At the various least, it will make future changes harder (unmanaged dependencies, lack of automated testing), and may present significant scalability challenges.

So, what exactly constitutes a ‘good’ architectural move? In general, this is what the project start architecture should aim to capture. A good basic principle could be that architectural commitments should be postponed for as long as possible, by taking steps to minimise the impact of changed architectural decisions (this is a ‘real-option‘ approach to architectural change management). Over the long term, this reduces the cost of change.

In addition, project start architectures may need to make explicit where architectural commitments must be made (e.g., for a specific database, PaaS or integration solution, etc) – i.e., areas where change will be expensive.

Other things the project start architecture may wish to capture or at least address (as part of enterprise design) could include:

  • Cataloging data semantics and usage
    • to support data governance and big data initiatives
  • Management of business context & scope (business area, product, entity, processes, etc)
    • to minimize unnecessary redundancy and process duplication
  • Controlled exposure of data and behaviour to other domains
    • to better manage dependencies and relationships with other domains
  • Compliance with enterprise policies around security and data management
    • to address operational risk
  • Automated build, test & deploy processes
    • to ensure continued agility and responsiveness to change, and maximise operational stability
  • Minimal lock-in to specific solution architectures (minimise solution architecture impact on other domains)
    • to minimize vendor lock-in and maximize solution options

The Chess book mentioned above includes a good description of a PSA, in the context of a PRINCE2 project framework. Note that the approach also works for Agile, but the PSA should set the boundaries within which the agile team can operate: if those boundaries must be crossed to deliver a user story, then enterprise design architects should be brought back into the discussion to establish the best way forward.

In summary, every change is an opportunity to make the ‘right’ architectural move to improve complexity management and to maintain an acceptable overall cost of change.

Making good architectural moves

Know What You Got: Just what, exactly, should be inventoried?

[tl;dr Application inventories linked to team structure, coupled with increased use of meta-data in the software-development process that is linked to architectural standards such as functional, data and business ontologies, is key to achieving long term agility and complexity management.]

Some of the biggest challenges with managing a complex technology platform is knowing when to safely retire or remove redundant components, or when there is a viable reusable component (instantiated or not) that can be used in a solution. (In this sense, a ‘component’ can be a script, a library, or a complete application – or a multitude of variations in-between.)

There are multiple ways of looking at the inventory of components that are deployed. In particular, components can view from the technology perspective, or from the business or usage perspective.

The technology perspective is usually referred to as configuration management, as defined by ITIL. There are many tools (known as ‘CMDB’s) which, using ‘fingerprints’ of known software components, can automatically inventorise which components are deployed where, and their relationships to each other. This is a relatively well-known problem domain – although years of poor deployment practice means that random components can be found running on infrastructure months or even years after the people who created it have moved on. Although the ITIL approach is imminently sensible in principle, in practice it is always playing catch-up because deployment practices are only improving slowly in legacy environments.

Current cloud-aware deployment practices encapsulated in dev-ops are the antithesis of this approach: i.e., all aspects of deployment are encapsulated in script and treated as source code in and of itself. The full benefits of the ITIL approach to configuration management will therefore only be realised when the transition to this new approach to deployment is fully completed (alongside the virtualisation of data centres).

The business or usage perspective is much harder: typically this is approximated by establishing an application inventory, and linking various operational accountabilities to that inventory.

But such inventories suffer from key failings..specifically:

  • The definition of an application is very subjective and generally determined by IT process needs rather than what makes sense from a business perspective.
  • Application inventories are usually closely tied to the allocation of physical hardware and/or software (such as operating systems or databases).
  • Applications tend to evolve and many components could be governed by the same ‘application’ – some of which may be supporting multiple distinct business functions or products.
  • Application inventories tend to be associated with running instances rather than (additionally) instantiable instances.
  • Application inventories may capture interface dependencies, but often do not capture component dependencies – especially when applications may consist of components that are also considered to be a part of other applications.
  • Application and component versioning are often not linked to release and deployment processes.

The consequence is that the application inventory is very difficult to align with technology investment and change, so it is not obvious from a business perspective which components could or should be involved in any given solution, and whether there are potential conflicts which could lead to excessive investment in redundant components, and/or consequent under-investment in potentially reusable components.

Related to this, businesses typically wish to control the changes to ‘their’ application: the thought of the same application being shared with other businesses is usually something only agreed as a last resort and under extreme pressure from central management: the default approach is for each business to be fully in control of the change management process for their applications.

So IT rationally interprets this bias as a license to create a new, duplicate application rather than put in place a structured way to share reusable components, such that technical dependencies can be managed without visibly compromising business-line change independence. This is rational because safe, scalable ways to reuse components is still a very immature capability most organisations do not yet have.

So what makes sense to inventory at the application level? Can it be automated and made discoverable?

In practice, processes which rely on manual maintenance of inventory information that is isolated from the application development process are not likely to succeed – principally because the cost of ensuring data quality will make it prohibitive.

Packaging & build standards (such as Maven, Ant, Gradle, etc) and modularity standards (such as OSGi for Java, Gems for Ruby, ECMAsript for JS, etc) describe software components, their purpose, dependencies and relationships. In particular, disciplined use of software modules may allow applications to self-declare their composition at run-time.

Non-reusable components, such as business-specific (or context-specific) applications, can in principle be packaged and deployed similarly to reusable modules, also with searchable meta-data.

Databases are a special case: these can generally be viewed as reusable, instantiated components – i.e., they may be a component of a number of business applications. The contents of databases should likely best be described through open standards such as RDF etc. However, other components (such as API components serving a defined business purpose) could also be described using these standards, linked to discoverable API inventories.

So, what precisely needs to be manually inventoried? If all technical components are inventoried by the software development process, the only components that remain to be inventoried must be outside the development process.

This article proposes that what exists outside the development process is team structure. Teams are usually formed and broken up in alignment with business needs and priorities. Some teams are in place for many years, some may only last a few months. Regardless, every application component must be associated with at least one team, and teams must be responsible for maintaining/updating the meta-data (in version control) for every component used by that team. Where teams share multiple components, a ‘principle’ team owner must be appointed for each component to ensure consistency of component meta-data, to handle pull-requests etc for shared components, and to oversee releases of those components. Teams also have relevance for operational support processes (e.g., L3 escalation, etc).

The frequency of component updates will be a reflection of development activity: projects which propose to update infrequently changing components can expect to have higher risk than projects whose components frequently change, as infrequently changing components may indicate a lack of current knowledge/expertise in the component.

The ability to discover and reason about existing software (and infrastructure) components is key to managing complexity and maintaining agility. But relying on armies of people to capture data and maintain quality is impractical. Traditional roadmaps (while useful as a communication tool) can deviate from reality in practice, so keeping them current (except for communication of intent) may not be a productive use of resources.

In summary, application inventories linked to team structure, and Increased use of meta-data in the software-development process that is linked to broader architectural standards (such as functional, data and business ontologies) are key to achieving agility and long term complexity management.

Know What You Got: Just what, exactly, should be inventoried?

Achieving modularity: functional vs volatility decomposition

Enterprise architecture is all about managing complexity. Many EA initiatives tend to focus on managing IT complexity, but there is only so much that can be done there before it becomes obvious that IT complexity is, for the most part, a direct consequence of enterprise complexity. To recap, complexity needs to be managed in order to maintain agility – the ability for an organisation to respond (relatively) quickly and efficiently to changes in markets, regulations or innovation, and to continue to do this over time.

Enterprise complexity can be considered to be the activities performed and resources consumed by the organisation in order to deliver ‘value’, a metric usually measured through the ability to maintain (financial) income in excess of expenses over time.

Breaking down these activities and resources into appropriate partitions that allow holistic thinking and planning to occur is one of the key challenges of enterprise architecture, and there are various techniques to do this.

Top-Down Decomposition

The natural approach to decomposition is to first understand what an organisation does – i.e., what are the (business) functions that it performs. Simply put, a function is a collection of data and decision points that are closely related (e.g., ‘Payments ‘is a function). Functions typically add little value in and of themselves – rather they form part of an end-to-end process that delivers value for a person or legal entity in some context. For example, a payment on its own means nothing: it is usually performed in the context of a specific exchange of value or service.

So a first course of action is to create a taxonomy (or, more accurately, an ontology) to describe the functions performed consistently across an enterprise. Then, various processes, products or services can be described as a composition of those functions.

If we accept (and this is far from accepted everywhere) that EA is focused on information systems complexity, then EA is not responsible for the complexity relating to the existence of processes, products or services. The creation or destruction of these are usually a direct consequence of business decisions. However, EA should be responsible for cataloging these, and ensuring these are incorporated into other enterprise processes (such as, for example, disaster recovery or business continuity processes). And EA should relate these to the functional taxonomy and the information systems architecture.

This can get very complex very quickly due to the sheer number of processes, products and services – including their various variations – most organisations have. So it is important to partition or decompose the complexity into manageable chunks to facilitate meaningful conversations.

Enterprise Equivalence Relations

One way to do this at enterprise level is to group functions into partitions (aka domains) according to synergy or autonomy (as described by Roger Sessions), for all products/services supporting a particular business. This approach is based on the mathematical concept of equivalenceBecause different functions in different contexts may have differing equivalence relationships, functions may appear in multiple partitions. One role of EA is to assess and validate if those functions are actually autonomous or if there is the potential to group apparently duplicate functions into a new partition.

Once partitions are identified, it is possible to apply ‘traditional’ EA thinking to a particular partition, because that partition is of a manageable size. By ‘traditional’ EA, I mean applying Zachman, TOGAF, PEAF, or any of the myriad methodologies/frameworks that are out there. More specifically, at that level, it is possible to establish a meaningful information systems strategy or goal for a particular partition that is directly supporting business agility objectives.

The Fallacy of Functional Decomposition

Once you get down to the level of partition, the utility of functional decomposition when it comes to architecting solutions becomes less useful. The natural tendency for architects would be to build reusable components or services that realise the various functions that comprise the partition. In fact, this may be the wrong thing to do. As Jüval Lowy demonstrates in his excellent webinar, this may result in more complexity, not less (and hence less agility).

When it comes to software architecture, the real reason to modularise your architecture is to manage volatility or uncertainty – and to ensure that volatility in one part of the architecture does not unnecessarily negatively impact another part of the architecture over time. Doing this allows agility to be maintained, so volatile parts of the application can, in fact, change frequently, at low impact to other parts of the application.

When looking at a software architecture through this lens, a quite different set of components/modules/services may become evident than those which may otherwise be obvious when using functional decomposition – the example in the webinar demonstrates this very well. A key argument used by Jüval in his presentation is that (to paraphrase him somewhat) functions are, in general, highly dependent on the context in which they are used, so to split them out into separate services may require making often impossible assumptions about all possible contexts the functions could be invoked in.

In this sense, identified components, modules or services can be considered to be providing options in terms of what is done, or how it is done, within the context of a larger system with parts of variable volatility. (See my earlier post on real options in the context of agility to understand more about options in this context.)

Partitions as Enterprise Architecture

When each partition is considered with respect to its relationship with other partitions, there is a lot of uncertainty around how different partitions will evolve. To allow for maximum flexibility, every partition should assume each other partition is a volatile part of their architecture, and design accordingly for this. This allows each partition to evolve (reasonably) independently with minimum fixed co-ordination points, without compromising the enterprise architecture by having different partitions replicate the behaviours of partitions they depend on.

This then allows:

  • Investment to be expressed in terms of impact to one or more partitions
  • Partitions to establish their own implementation strategies
  • Agile principles to be agreed on a per partition basis
  • Architectural standards to be agreed on a per partition basis
  • Partitions to define internally reusable components relevant to that partition only
  • Partitions to expose partition behaviour to other partitions in an enterprise-consistent way

In generative organisation cultures, partitions do not need to be organisationally aligned. However, in other organisation cultures (pathological or bureaucratic), alignment of enterprise infrastructure functions such as IT or operations (at least) with partitions (domains) may help accelerate the architectural and cultural changes needed – especially if coupled with broader transformations around investment planning, agile adoption and enterprise architecture.

Achieving modularity: functional vs volatility decomposition

Achieving Agile at Scale

[tl;dr Scaling agile at the enterprise level will need rethinking how portfolio management and enterprise architecture are done to ensure success.]

Agility,as a concept, is gaining increasing attention within large organisations. The idea that business functions – and in particular IT – can respond quickly and iteratively to business needs is an appealing one.

The reasons why agility is getting attention are easy to spot: larger firms are getting more and more obviously unagile – i.e., the ability of business functions to respond to business needs in a timely and sustainable manner is getting progressively worse, even as a rapidly evolving competitive and technology-led commercial environment is demanding more agility.

Couple that with the heavy cost of failing to meet ever increasing regulatory compliance obligations, and ‘agile’ seems a very good idea indeed.

Agile is a great idea, but when implemented at scale (in large enterprise organisations), it can actually reduce enterprise agility, rather than increase it, unless great care is taken.

This is partly because Agile’s origins come from developing web applications: in these scenarios, there is usually a clear customer, a clear goal (to the extent that the team exists in the first place), and relatively tight timelines that favour short or non-existent analysis/design phases. Agile is perfect for these scenarios.

Let’s call this scenario ‘local agile’. It is quite easy to see a situation where every team, in response to the question, ‘are you doing agile?’, for teams to say ‘Yes, we do!’. So if every team is doing ‘local agile’, does that mean your organisation is now ‘agile’?

The answer is No. Getting every team to adopt agile practices is a necessary but insufficient step towards achieving enterprise agility. In particular, two key factors needs to be addressed before true a firm can be said to be ‘agile’ at the enterprise level. These are:

  1. The process by which teams are created and funded, and
  2. Enterprise awareness

Creating & Funding (Agile) Teams

Historically, teams are usually created as a result of projects being initiated: the project passes investment justification criteria, the project is initiated and a team is put in place, led by the project manager. Also, this process was owned entirely by the IT organisation, irrespective of which other organisations were stakeholders in the project.

At this point, IT’s main consideration is, will the project be delivered on time and on budget? The business sponsor’s main consideration is, will it give us what we need when we need it? And the enterprise’s consideration (which is often ignored) is who is accountable for ensuring that the IT implementation delivers value to the enterprise. (In this sense, the ‘enterprise’ could be either a major business line with full P&L responsibility for all activities performed in support of their business, or the whole organisation, including shared enterprise functions).

Delivering ‘value’ is principally about ensuring that  on-going or operational processes, roles and responsibilities are adjusted to maximise the benefits of a new technology implementation – which could include organisational change, marketing, customer engagement, etc.

However, delivering ‘value’ is not always correlated to one IT implementation; value can be derived from leveraging multiple IT capabilities in concert. Given the complexity of large organisations, it is often neither desirable or feasible to have a single IT partner be responsible for all the IT elements that collectively deliver business value.

On this basis, it is evident that how businesses plan and structure their portfolio of IT investments needs to change dramatically. In particular,

  1. The business value agenda is outcome focused and explicit about which IT capabilities are required to enable it, and
  2. IT investment is focused around the capability investment lifecycle that IT is responsible for stewarding.

In particular ‘capabilities’ (or IT products or services) have a lifecycle: this affects the investment and expectations around those capabilities. And some capabilities need to be more ‘agile’ than others – some must be agile to be useful, whereas for others, stability may be the over-riding priority, and therefore their lack of agility must be made explicit – so agile teams can plan around that.

Enterprise Awareness

‘Locally’ agile teams are a step in the right direction – particularly if the business stakeholders all agree they are seeing the value from that agility. But often this comes at the expense of enterprise awareness. In short, agility in the strict business sense can often only deliver results by ignoring some stakeholders interests. So ‘locally’ agile teams may feel they must minimise their interactions with other teams – particularly if those teams are not themselves agile.

If we assume that teams have been created through a process as described in the previous section, it becomes more obvious where the team sits in relation to its obligations to other teams. Teams can then make appropriate compromises to their architecture, planning and agile SDLC to allow for those obligations.

If the team was created through ‘traditional’ planning processes, then it becomes a lot harder to figure out what ‘enterprise awareness’ is appropriate (except perhaps or IT-imposed standards or gates, which only contributes indirectly to business value).

Most public agile success stories describe very well how they achieved success up to  – but not including – the point at which architecture becomes an issue. Architecture, in this sense, refers to either parts of the solution architecture which can no longer be delivered via one or two members of an agile team, or those parts of the business value chain that cannot be entirely delivered via the agile team on its own.

However, there are success stores (e.g., Spotify) that show how ‘enterprise awareness’ can be achieved without limiting agility. For many organisations, transitioning from existing organisation structures to new ‘agile-ready’ structures will be a major challenge, and far harder than simply having teams ‘adopt agile’.

Conclusions

With the increased attention on Agile, there is fortunately increased attention on scaling agile. Methodologies like Disciplined Agile Development (DaD) and LargE Scale Scrum (LeSS), coupled with portfolio concepts like Scaled Agile Framework (SAFe) propose ways in which Agile can scale beyond the team and up to enterprise level, without losing the key benefits of the agile approach.

All scaled agile methodologies call for changes in how Portfolio Management and Enterprise Architecture are typically done within an organisation, as doing these activities right are key to the success of adopting Agile at scale.

Achieving Agile at Scale