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The Real Cost of One Trillion Dollars in IT Debt: Part II – The Performance Paradox

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Some of the business ramifications of the $1 trillion in IT debt have been explored in the first post of this two-part analysis. This second post focuses on “an ounce of prevention is worth a pound of cure” aspects of IT debt. In particular, it proposes an explanation why prevention was often neglected in the US over the past decade and very possibly longer. This explanation is not meant to dwell on the past. Rather, it studies the patterns of the past in order to provide guidance for what you could do and should do in the future to rein in technical debt.

The prevention vis-a-vis cure trade-off  in software was illustrated by colleague and friend Jim Highsmith in the following figure:

Figure 1: The Technical Debt Curve

As Jim astutely points out, “once on far right of curve all choices are hard.” My experience as well as those of various Cutter colleagues have shown it is actually very hard. The reason is simple: on the far right the software controls you more than you control it. The manifestations of technical debt [1] in the form of pressing customer problems in the production environment force you into a largely reactive mode of operation. This reactive mode of operation is prone to a high error injection rate – you introduce new bugs while you fix old ones. Consequently, progress is agonizingly slow and painful. It is often characterized by “never-ending” testing periods.

In Measure and Manage Your IT Debt, Gartner’s Andrew Kyte put his finger on the mechanics that lead to the accumulation of technical debt – “when budget are tight, maintenance gets cut.” While I do not doubt Andrew’s observation, it does not answer a deeper question: why would maintenance get cut in the face of the consequences depicted in Figure 1? Most CFOs and CEOs I know would get quite alarmed by Figure 1. They do not need to be experts in object-oriented programming in order to take steps to mitigate the risks associated with slipping to the far right of the curve.

I believe the deeper answer to the question “why would maintenance get cut in the face of the consequences depicted in Figure 1?” was given by John Seely Brown in his 2009 presentation The Big Shift: The Mutual Decoupling of Two Sets of Disruptions – One in Business and One in IT. Brown points out five alarming facts in his presentation:

  1. The return on assets (ROA) for U.S. firms has steadily fallen to almost one-quarter of 1965 levels.
  2. Similarly, the ROA performance gap between corporate winners and losers has increased over time, with the “winners” barely maintaining previous performance levels while the losers experience rapid performance deterioration.
  3. U.S. competitive intensity has more than doubled during that same time [i.e. the US has become twice as competitive - IG].
  4. Average Lifetime of S&P 500 companies [declined steadily over this period].
  5. However, in those same 40 years, labor productivity has doubled – largely due to advances in technology and business innovation.

Discussion of the full-fledged analysis that Brown derives based on these five facts is beyond the scope of this blog post [2]. However, one of the phenomena he highlights –  “The performance paradox: ROA has dropped in the face of increasing labor productivity” – is IMHO at the roots of the staggering IT debt we are staring at.

Put yourself in the shoes of your CFO or your CEO, weighing the five facts highlighted by Brown in the context of Highsmith’s technical debt curve. Unless you are one of the precious few winner companies, the only viable financial strategy you can follow is a margin strategy. You are very competitive (#3 above). You have already ridden the productivity curve (#5 above). However, growth is not demonstrable or not economically feasible given the investment it takes (#1 & #2 above). Needless to say, just thinking about being dropped out of the S&P 500 index sends cold sweat down your spine. The only way left to you to satisfy the quarterly expectations of Wall Street is to cut, cut and cut again anything that does not immediately contribute to your cashflow. You cut on-going refactoring of code even if your CTO and CIO have explained the technical debt curve to you in no uncertain terms. You are not happy to do so but you are willing to pay the price down the road. You are basically following a “survive to fight another day” strategy.

If you accept this explanation for the level of debt we are staring at, the core issue with respect to IT debt at the individual company level [3] is how “patient” (or “impatient”) investment capital is. Studies by Carlota Perez seem to indicate we are entering a phase of the techno-economic cycle in which investment capital will shift from financial speculation toward (the more “patient”) production capital. While this shift is starting to happens, you have the opportunity to apply “an ounce of prevention is worth a pound of cure” strategy with respect to the new code you will be developing.

My recommendation would be to combine technical debt measurements with software process change. The ability to measure technical debt through code analysis is a necessary but not sufficient condition for changing deep-rooted patterns. Once you institute a process policy like “stop the line whenever the level of technical debt rose,” you combine the “necessary” with the “sufficient” by tying the measurement to human behavior. A possible way to do so through a modified Agile/Scrum process is illustrated in Figure 2:

Figure 2: Process Control Model for Controlling Technical Debt

As you can see in Figure 2, you stop the line and convene an event-driven Agile meeting whenever the technical debt of a certain build exceeds that of the previous build. If ‘stopping the line’ with every such build is “too much of a good thing” for your environment, you can adopt statistical process control methods to gauge when the line should be stopped. (See Using 3σ  Control Limits in Software Engineering for a discussion of the settings appropriate for your environment.)

An absolutely critical question this analysis does not cover is “But how do we pay back our $1 trillion debt?!I will address this most important question in a forthcoming post which draws upon the threads of this post plus those in the preceding Part I.

Footnotes:

[1] Kyte/Gartner define IT Debt as “the costs for bringing all the elements [i.e. business applications] in the [IT] portfolio up to a reasonable standard of engineering integrity, or replace them.” In essence, IT Debt differs from the definition of Technical Debt used in The Agile Executive in that it accounts for the possible costs associated with replacing an application. For example, the technical debt calculated through doing code analysis on a certain application might amount to $500K. In contrast, the cost of replacement might be $250K, $1M or some other figure that is not necessarily related to intrinsic quality defects in the current code base.

[2] See Hagel, Brown and Davison: The Power of Pull: How Small Moves, Smartly Made, Can Set Big Things in Motion.

[3] As distinct from the core issue at the national level.

Balancing Agile – Guest Post by Alan Shalloway

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A fascinating difference exists between Agile and Business Service Management (BSM). Agile emphasizes continuous flow of value to the customer. In contrast, BSM focuses on the business – it aligns the deliverables of IT to the enterprise’s business goals. Subtle that the difference might be, the two methods evolved along quite different lines in spite of the common denominator – dealing with software.

In this guest post, Alan Shalloway – Founder and CEO of NetObjectives – discusses the implications of focusing on the business as distinct from focusing on the customer. His discussion is part of  a few thought-provoking threads he weaves around the Agile Manifesto. Alan perceives the Manifesto a product of the times. He thinks aloud whether today’s circumstances require a revised manifesto.

Alan is a man of passion. While I do not always agree with him, I have a lot of respect for his quest to find the deeper truths. Furthermore, I always learn from him. Whether you agree or disagree with the opinion Alan articulates in this post, “listening” to his thoughts is well worth your time.

Here is Alan:

The Agile Manifesto was a watershed event that has forever changed the landscape of software development.  So profound a positive impact of it has had, that few challenge whether it was actually correct.  Manifestos are often  a statement in reaction to something prevalent that needs changing.  This makes them very topical and temporal – and the exact intention needs to be restated when the landscape against which it was drawn has changed. The Agile Manifesto[1] states:

“Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan

That is, while there is value in the items on
the right, we value the items on the left more.”

At the time of its edict, this was profound and well stated.  Too many software teams were:

  • Following a process dictated to them from outside their team
  • Managing according to an extensive set of artifacts that recorded where they supposedly were well before software existed
  • Were given a set of requirements to meet with little opportunity to discuss real needs (note, points 3 and 4 in the manifesto address this point in two ways – first recognizing that customer collaboration is necessary to discover their true needs and second that it is essential to take advantage of newly discovered information)

The manifesto represented a new paradigm from which to work – one in which the team would have better control over its destiny and where it was recognized that one had to make incremental, iterative movement towards one’s goals – both in discovering the true goal and in implementing it.

Unfortunately, the perspective from which the manifesto was created, or at least the methods which first followed the manifesto, have been extremely team centric.  Not a surprise, given the paradigm at the time gave development teams too little say in their own methods.  The impact of this has been, not surprisingly, success at the teams and difficult beyond the team. It is almost axiomatic now that companies will have successful team pilots only to bog down in their enterprise agile adoption efforts or even revert back to earlier methods.

I say “not surprisingly” because several things have been left out of the agile manifesto.  These are:

  • The role of management
  • The role of process
  • The role of planning
  • And indirectly, the role of guiding principles

It could also be said that the driver for agile development is misplaced.  I do not believe “Our highest priority is to satisfy the customer through early and continuous delivery of valuable software.”  This makes software groups customer driven, not business driven.

There is a subtle, but important difference. Basically, conscious or not, the Agile Manifesto is driven with a team-centric view of satisfying customers – business and management play mostly secondary roles. Unfortunately, there is a significant difference between customer driven and business driven (see the figure entitled Alignment with Vision of Business to the right).  This is not apparent at the team level (the team is supposed to satisfy the customer).  But definitely at the business level. Not surprising, however, since the manifesto is a team perspective it states things in terms of customer value.

Ironically, it is the over-focus on the team, however, that is robbing teams of one of their greatest tools.  Clearly any method must have respecting people and providing those doing the work the ability to choose how they do their work. But this does not mean that process isn’t essential or that attending to certain laws of software development is optional.  Rather it means that process can’t be imposed on teams since to do so would both rob them of respect and almost certainly be the wrong way to do things – who knows more about how to get work done than the people doing the work themselves?

But “Individuals over process” as the first line has come to be called, makes it sound like people are it.  I do not think so – and I think this mindset has caused a lot of damage in many ways.

There is great evidence that the best approach is not merely get smart people onto a team and have them figure out how to solve their problems.  They must be properly equipped to do so.  Just being smart doesn’t mean you can solve the challenges facing you.  This should be readily apparent, but in many ways, the Agile community has mostly ignored it. Actually, there is not really an Agile community any more – there are factions that have significantly different beliefs.  For example, XP has long recognized the need for technical practices in Agile while the Scrum community is only just starting to get into what these are.

However, except for the Lean/Kanban community, few Agilists seem to espouse discovering and following the laws of product development flow (or even recognize their existence).  This, in my mind, has led to the low rate of success in scaling Scrum to the enterprise*[2]. Ironically, it is the over-focus on people that leads many in the Scrum community to assert this lack of success is a lack willingness to take the effort to improve. This is not surprising – if it’s up to the team to succeed, then when they don’t it must be something wrong with the team or their management when they fail.  I think not. I think it is the lack of understanding of the principles of software development flow.

These laws are not new.  Don Reinertsen, in his iconic book Managing the Design Factory lays out much of the rules of product development.  His more recent book The Principles of Product Development Flow: Second Generation Lean Product Development he lays out 175 of these principles.

To me, true respect for people means that one must equip them with what they need to get their job done.  Our thinking in the Agile community should change from “People over process” to one of “People times process.[3]This phrase emphasizes that if either are low, you get a low productivity.  Process does not ensure success. But a poor process requires heroes to succeed.  We’d like good, motivated, well-intentioned people to be able to succeed.

Our new agile perspective needs to include an understanding of what teams need to know to do their work. This opens up a role for managers to actively help teams get their job done and to coach them when they have challenges or lose their way.  While I will always be thankful for the Agile Manifesto, I am looking for a Business Agile Manifesto that will expand the focus from the team to the entire enterprise.

Footnotes:

[1] http://www.agilemanifesto.org

[2] Ken Schwaber, iconic leader of the Scrum community has said that “I estimate that 75% of those organizations using Scrum will not succeed in getting the benefits that they hope for from it.”

[3] I first heard this from Don Reinertsen.  Before that I used to say “People plus process.”

Measuring Agile Success Rate the Right Way

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Much has been said recently about the success/failure rate of Agile projects. In particular, a debate arose around the success rate of Scrum vis-a-vis Kanban.  For example, in a post entitled Some Day Kanban will fail 75% of the Time, colleague Jurgen Appelo states as follows:

Unfortunately, some people arguing against Scrum include these ScrumBut teams in their evaluations of the “high failure rate” of Scrum. They love quoting that “at least 75 percent of Scrum implementations fail.” And I think “Yes of course, 75% fails when that includes the teams that don’t understand what they’re doing.”

I would like to add one other “dimension” to the discussion: boundary conditions.

Any Agile initiative – Crystal, Scrum, Kanban, etc. – typically starts from a certain state of affairs of the code that has already been developed using a Waterfall method or no method at all. Even brand new projects produce code that invariably interacts with other software components that are already deployed, warts and everything. Pristine environments with no technical debt for the Agile initiative to deal with are rare.

Like it or not, the Agile initiative is saddled from the outset with a certain amount of technical debt. Code has been duplicated, rules violated, complexity ran amuck, etc. A typical enterprise software team starts with hundreds of thousands $$ in technical debt, if not millions. This debt needs to be “paid back.” Probably not over night, but certainly over a period of time. As illustrated by the following figure from Jim Highsmith, things get ugly if the debt is not paid back over an extended period of time.

in-can-you-afford-the-software-you-are-developing

The evaluation of success or failure of the Agile initiative needs to take technical debt into account. A team of 50 with an accrued technical debt of $100,000 has a much easier job on its hands transitioning to Agile than a similar size team starting with $1M in technical debt on its hands.

Whatever criteria you use to determine whether an Agile initiative has been successful, I would suggest the following boundary condition needs to be satisfied:

Technical debt at the end of the project/initiative must be significantly lower than technical debt at the start of the project.

Use the techniques outlined in Using Credit Limits to Constrain Development on Margin to calculate technical debt before and after. In addition to qualifying your Agile success, quantifying technical debt will do a lot towards improving the quality of your software.

The Agile Flywheel

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Readers of The Agile Executive have been exposed to the “All In!” strategy used by Erik Huddleston to transform the software engineering process at Inovis and make it uniquely streamlined. In this post we follow up on the original discussion of the subject to explore the effect of Agile on IT Operations. As the title implies, Agile at Inovis served as a flywheel which created the momentum required to transform IT Operations and blend the best of Agile with the best of ITIL.

This guest post was written by Ray Riescher – a Six Sigma Black Belt, Agile evangelist and a business process change agent. Ray is currently responsible for business process management and IT governance at Inovis, a  leading provider of business-to-business (B2B) e-commerce services, in Alpharetta, GA

Here is Ray:

When we converted to an Agile Scrum software methodology some 24 months ago, I never imagined the lessons I’d learn and the organizational change that would be driven by the adoption of Scrum.

I’ve lived by the philosophy that managing a business is managing its processes and that all of those processes, especially the operational processes, are interconnected.  However, I don’t think I was fully prepared for effect Agile Scrum would have on our company operations.

We dove head first into Agile Scrum and adapted to it very quickly. However, it wasn’t until we landed a very large and demanding customer that Scrum was really put to the test. New enhancements, new features, and new configurations were all needed ASAP.  Scrum delivered with rapid development and deployment in the form of releases that were moving into production with amazing velocity. Our release cadence hit warp drive and at one point we experienced several months where multiple teams’ production releases were deploying at the end of every two week sprint.

We’ve subscribed to the ITIL service support processes for Release, Change, Incident, Problem and Configuration Management. ITIL has served us well, giving us a common language and a clear understanding of process boundaries.

As the Scrum release cadence kicked in, the downstream ITIL processes had to keep up, adapt, and support the dynamics of rapid production changes.  What happened was enlightening and maybe a bit ground breaking.

The Release Management process had to reassess its reliance on artifacts for gate keeping. The levels of sign offs had to be streamlined, the heavyweight deployment documentation had to be lightened, yet the process still had to control the production release to ensure deployment success.  The rapidity of the release cycles meant that maintenance window downtime would be experienced too frequently by customers, so “rolling bounce” deployment strategies were devised and implemented.

Change requests could no longer wait for a weekly Change Management review board to approve and schedule the changes.  Change management risk models had to be relied on for accurate detection of risky changes.

Early on in this dynamic environment, we weren’t quite as good as we needed to be and our Incident Management process was put to the test.  Faster releases meant more opportunity for problems with service degradation and outages. This reality manifested itself more frequently than we’d ever experienced. Monitoring, detecting and repairing became paramount for environment stability and customer satisfaction.

What we found out was that we became very agile at this break/fix game. We developed a small team approach to managing incidents and leveraged the ITIL Problem Management process to rapidly perform root cause analysis. Once the true root cause was determined, a fix would be defined and deployed. Sometimes the fix was software related and went through the Scrum process, sometimes the fix was hardware related and went through the Configuration Management process, other times it was more operational and the fix took the form of training or corrections to procedural documentation.

The point is we’ve become agile across the entire IT spectrum. Whether it’s development via Scrum, the velocity with which we now operate our ITIL processes, or the integrated break/fix operational support processes, we are performing all of these with an agile mindset and discipline. We have small teams, working on priorities, and completing what needs to be completed now.

Scrum set the flywheel in motion and caused the rest of the IT process life cycle to respond.  ITIL’s processes still form the solid core of service support and we’ve improved the processes’ capability to handle intense work velocity. The organization adapted by developing unprecedented speed in the ability to deliver production fixes and to solve root cause problems with agility.

What I think we are witnessing is a manifestation of Agile Business Service Management; a holistic agile methodology running across the IT process spectrum that’s delivering eye popping change and tremendous results.

Definition: Agile Methodology

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Agile Methodology is actually a bit of a controversial termVarious authors consider Agile a method, as distinct from a methodology. Others, prefer methodology over method. For example, using the Merriam-Webster dictionaries, Alistair Cockburn makes the following distinction between methodology and method in Agile Software Development: The Cooperative Game :

  • Methodology: A series of related methods or techniques
  • Method: Systematic procedure

Alistair views Agile as a methodology in the sense defined above. For example, he discusses Crystal as a family of methodologies. The reader is referred to Alistair’s book for a an excellent analysis of the various aspects of methodologies. As a matter of fact, Alistair tracks down the confusion between method and methodology to certain inconsistencies between various versions of the Oxford English Dictionary.

On the other hand, best I can tell from various conversations with him, Jim Highsmith seems to prefer the term Agile Method. This preference is reflected in Agile Project Management: Creating Innovative Products. It is possible that Jim’s preference is due to writing his book from a project management perspective.

Rather than getting in-depth to the method versus methodology controversy, I would simply cite two definitions I find useful in capturing the essence of Agile methodology, or method if you prefer.

An interesting metaphor for Agile has been used by Jim Highsmith in a 2009 Cutter Advisory:

Visualize a house structure with a roof, a foundation, and three pillars… The roof is business goals — the rationale for implementing agile methods and scaling to larger agile projects. The foundation is agile values or principles — principles that need careful interpretation as to how to apply them to larger teams. And finally, the three pillars: organization, product backlog, and process/practice.

The simplicity of the metaphor makes it quite effective in communicating what Agile is in a concise way without losing the richness of the various elements in Agile.

Using Scrum as an example, colleague David Spann gives the following down-to-earth summary of the key structural components of Agile in a 2008 Cutter Executive Report:

Scrum, as a management methodology, is elegant in its design, requiring only three roles (i.e., product owner, ScrumMaster, and self-organized team), three ceremonies (sprint/iteration planning, daily Scrum/debrief, and sprint review meetings), and three artifacts (product and sprint backlogs and the burndown chart) — just-enough practical advice so agile teams do not overcomplicate the development lifecycle with too much ceremony and documentation.

Needless to say, the structural elements will change from one Agile methodology to another. However, examining an Agile methodology through the {roles, ceremonies, artifacts} “lens” is an excellent way to summarize an Agile methodology. Furthermore, it enables easy comparison between the ‘usual suspects’ of Agile – Crystal Methods, Dynamic Systems Development, Extreme Programming, Feature Driven Development, and Kanban. The reader is referred to The Business Value of Agile Software Methods: Maximizing ROI with Just-in-Time Processes and Documentation for detailed comparisons between the various methods/methodologies.

Extending a True Epiphany

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In Agile Software Development with Scrum, Ken Schwaber describes a true epiphany he experienced as a result of his 1995 meetings with DuPont’s process control experts:

They [DuPont's process control experts] inspected the system development processes that I brought them. I have rarely provided a group with so much laughter. They were amazed and appalled that my industry, system development, was trying to do its work using a completely inappropriate process control model. They said system development had so much complexity and unpredictability that it had to be managed by a process control model they referred to as “empirical.” They said it was nothing new, and all complex processes that were not completely understood required the empirical model…

… I realized why everyone in my industry had such problems building systems. I realized why the industry was in such trouble and had such poor reputation. We were wasting our time trying to control our work by thinking we had an assembly line when the only proper control was frequent and first-hand inspection, followed by immediate adjustments…

Based on this insight, I have since formulated with others the Scrum process for developing complex products, particularly software systems.

Fast forward to November 2009. During a lovely dinner in Boulder with Dean Leffingwell, we got into the subject of connecting Agile with ITIL. This conversation really registered with me. I actually recalled how years ago Ray Paquet characterized IT as a “continuous manufacturing” process. If you accept Ray’s premise, the chain {DuPont –> Scrum –> IT} is quite intriguing.

Re-reading Software Evolution recently, I was struck by the observation Tom Mens makes in the Introduction:

… due to the fact that the activity of software evolution is a continuous feedback process, the chosen process model itself is likely to be subject to evolution.

I can’t help wondering whether Tom’s observation applies to IT. If so, what are the implications with respect to IT operations and system management?!

Opinions please!

Definition: Agile Development

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The difficulty to concisely define the term Agile Development stems from the very nature of the Agile Manifesto:

  • The manifesto is a statement of values. By the very nature of values, people share them in a loose manner. Both definition and adherence (“But do they really practice Agile development?”) are qualitative and open to interpretation.
  • The manifesto values are relative. The manifesto is quite explicit in stating “… while there is value in the items on the right, we value the items on the left more:”

Individuals and interactions over processes and tools

Working software over comprehensive documentation

Customer collaboration over contract negotiations

Responding to change over following a plan

Agile development is often described in terms of the software method in use. For example, in his foreword to Agile Software Development with Scrum, Bob Martin summarizes Agile methods as “… people oriented software processes that work without getting in the way,” Martin Fowler emphasizes another aspect of Agile methods in his own foreword to the very same book:

… a new breed of software processes which are based on an empirical approach to controlling a project.

A more detailed definition is given by authors Rico, Sayani and Sone in their October 2009 book The Business Value of Agile Software Methods: Maximizing ROI with Just-in-time Processes and Documentation:

Agile methods are contemporary approaches for creating new software based on customer collaboration, teamwork, iterative development, and response to change. Combining communication and interpersonal trust with a flexible management and development framework, they contain just enough process to capture customer needs in the form of user stories and to rapidly create working software. However, the key to Agile methods are rich, high-context communications with customers along with cohesive teamwork.

On the other hand, such an authority (and signatory to the Manifesto) as Jim Highsmith does not seem to define the term Agile Development per se in the second edition of Agile Project Management: Creating Innovative Products. Instead, Jim defines Agility through two statements:

Agility is the ability to both create and respond to change in order to profit in a turbulent business environment.

Agility is the ability to balance flexibility and stability.

Likewise, in Agile Software Development, Alistair Cockburn focuses on discussing what is core to Agile, emphasizing the properties of Agility through the following citation:

Agility is dynamic, context specific, aggressively change-embracing and growth-oriented. It is not about improving efficiency, cutting costs or battening down the business hatches to ride out fearsome competitive ‘storms.’ It is about succeeding and about winning: about succeeding in emerging competitive arenas, and about winning profits, market share and customers in the very center of the competitive storms many companies now fear.

Rather than trying to reconcile all these worthy definitions, I would suggest five context-dependent approaches to the definition, as follows:

  • For the reader who tries to understand what Agile is all about: It is the mindset that really matters. Read the Agile Manifesto and the corresponding History.
  • For the reader who is anxious to put his/her hands around an Agile implementation: Pick a specific Agile method – any method – and study it with special emphasis on the roles, process and artifacts of the method. It could be Crystal, Extreme Programming (XP), Scrum, Dynamic Systems Development Method (DSDM), Kanban or any other method that shows promise as a good fit  for your specific environment. Consult 10 Steps for Starting an Agile Start-up for a down-to-earth blueprint for implementation.
  • For the reader who tries to assess whether a project team is really Agile: It is a maturity curve issue that manifests itself in quite a few disciplines. For example, see the various kinds of maturity models surveyed in the BSM Review blog. You will probably need to determine the maturity model that suits your environment and apply it to the method you are practicing.
  • For the reader who needs to explore Agile in a business context: You need not worry about the technical aspects of Agile. See the category Benefits of Agile in this blog.
  • For the reader interested in applying Agile beyond development: Extending Agile changes its definition. See the various posts on the subject by Eric Ries in Lessons Learned.

Please remember: when it comes to defining Agile Development, you have a problem of choosing, not of choice. It is the use to which you put the definition that determines the choosing.

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