My new white paper, Disciplined Agile Delivery: An Introduction
, is now available free of charge from IBM.com. The paper overviews the Disciplined Agile Delivery (DAD) process framework, a hybrid comprised of strategies from Scrum, XP, Agile Modeling, and other agile methods which is people first, learning oriented, and enterprise aware. DAD is the basis from which you can scale agile.
- Context counts - The Agile Scaling Model
- People first - People, and the way they interact with each other, are the primary determinant of success for a solution delivery project.
- Learning-oriented - The DAD process framework promotes the ideas that team members should collaborate closely and learn from each other, that the team should invest effort to learn from their experiences and evolve their approach, and that individuals should do so as well.
- Hybrid - DAD adopts and tailors strategies from Scrum, XP, Agile Modeling, UP, Kanban, and many others. It addresses many of the issues Mark Kennaley discusses in SDLC 3.0.
- IT solution focused - DAD teams produce potentially consumable solutions every construction iteration. This extends Scrum's "potentially shippable" strategy to explicitly address usability/consumability plus the fact that we're really delivering full solutions not just software.
- Goal-driven delivery life cycle - The DAD lifecycle is focused on delivery, not just construction. Furthermore it is goals-driven, the DAD process framework suggests various strategies to fulfill those goals but does not prescribe specific practices.
- Risk and value driven - The DAD lifecycle is risk and value driven. It extends Scrum's value-driven lifecycle which produces potentially shippable software each sprint/iteration so that it explicitly includes light-weight milesstones such as ensuring stakeholder consensus as to the scope of the project early in the lifecycle, proving the architecture with working code early in the lifecycle, ensuring sufficient functionality exists before transition, and ensuring production readiness before actual release of the solution.
- Enterprise aware - The DAD process framework promotes the ideas that DAD teams should work closely with their enterprise architecture groups to ensure they leverage and evolve the existing infrastructure, adopt and follow corporate guidelines, and work to the overall organizational vision. DAD teams are self organizing with appropriate governance.
I've been getting a lot of questions lately about applying the acceleration metric
in practice. So, here's some answers to frequently asked questions:
1. How do I monetize acceleration?
This is fairly straightforward to do. For example, assume your acceleration is 0.007 (0.7%), there are five people on the team, your annual burdened cost per person is $150,000, and you have two week iterations. All these numbers are made up, but you know how to calculate acceleration now and IT management had darn well better know the average burdened cost (salary plus overhead) of their staff. So, per iteration the average burdened cost per person must be $150,000/26 = $5,770. Productivity improvement per iteration for this team must be $5,770 * 5 * .007 = $202. If the acceleration stayed constant at 0.7% the overall productivity improvement for the year would be (1.007)^26 (assuming the team works all 52 weeks of the year) which would be 1.198 or 19.8%. This would be a savings of $148,500 (pretty much the equivalent of one new person). Of course a 20% productivity increase over an entire year is a really aggressive improvement, regardless of some of the claims made by the agile snake oil salesman out there, although at 10-15% increase is a reasonable expectation. What I'd really want to do is calculate the acceleration for the year by comparing the velocity from the beginning of the year to the end of the year (in Western cultures I'd want to avoid comparing iterations near to the holidays). So, if the team velocity the first week of February 2008 was 20 points, now the same team's velocity the first week of February 2009 was 23 points, that's an acceleration of (23-20)/20 = 15% over a one year period, for a savings of $112,500.
2. Is acceleration really unitless?
For the sake of comparison it is. The "units" are % change in points per iteration, or % change in points per time period depending on the way that you want to look at it. Because it's a percentage I can easily monetize it, as you see above, and use it as a basis of comparison.
3. How do I convince teams to share their data?
This can be difficult. Because acceleration is easy to calculate for agile teams, and because it's easy to use to compare teams (my team has .7% acceleration whereas other teams down the hall from mine have accelerations of .3% and -.2% of teams), people are concerned that this metric will be used against them. OK, to be fair, my team might be OK with this. ;-) Seriously though, this is a valid fear that will only be addressed by an effective governance program
based on enablement, collaboration, and trust instead of the traditional command-and-control approach. Management's track record regarding how they've used measurements in the past, and how they've governed in general, have a great effect on people's willingness to trust them with new metrics such as acceleration. The implication is that you need to build up trust, something that could take years if it's possible at all.
4. Why does this work for agile teams?
Agile teams are self organizing, and an implication of that is that they will be held accountable for their estimates. Because of this accountability, and because velocity is a vital input into their planning and estimation efforts, agile teams are motivated to calculate their velocity accurately and to track it over time. Because they're eager to get their velocity right, and because acceleration is based on velocity, there's an exceptionally good chance that it's accurate.
5. What about function points or similar productivity measures?
Function points can be calculated for projects being developed via an agile approach, or other approaches for that matter, but it's a very expensive endeavor compared to calculating acceleration (which is essentially free) and likely will be seen as a bureaucratic overhead by the development team. My rule of thumb is that if you're not being explicitly paid to count function points (for example the US DoD will often pay contracting companies to create estimates based on function point counts) then I wouldn't bother with them.
6. What about calculating acceleration for iterative project teams?
Iterative project teams, perhaps following Rational Unified Process (RUP)
, can choose to calculate and track their velocity and thereby their acceleration. The key is to allow the team to be self organizing and accountable for their estimates, which in turn motivates them to get their velocity right just like agile teams (RUP can be as agile as you want to make it, don't let anyone tell you differently).
7. What about calculating acceleration for traditional project teams?
In theory this should work, in practice it is incredibly unlikely. Traditional teams don't work in iterations where working software is produced on a regular basis, they're typically not self organizing, and therefore there really isn't any motivate to calculate velocity (even if they do, there is little motivation to get it right). Without knowing the velocity you can't calculate acceleration. If you can't trust the velocity estimate, and I certainly wouldn't trust a traditional team's velocity estimate, then you can't trust your acceleration calculation. So, my fall back position to calculate productivity improvement would be to do something like function point counting (which is expensive and difficult to compare between teams due to different fudge factors used by different FP counters) and then looking at change in FPs delivered over time.
8. How can I apply this across a department?
It is fairly straightforward to roll up the acceleration of project teams into an overall acceleration measure for a portfolio of teams simply by taking a weighted average based on team size. However, this is only applicable to teams that are in a position to report an accurate acceleration (the agile and iterative teams) and of course are willing to do so.
9. What does a negative acceleration tell me?
If the acceleration is negative then productivity on the team is going down, likely an indicator of quality and/or team work problems. However, you don't want to manage by the numbers so you should talk to the team to see what's actually going on.
10. What does a zero acceleration tell me?
This is an indication that the team's productivity is not increasing, and that perhaps they should consider doing retrospectives at the end of each iteration and then acting on the results from those retrospectives. Better yet they can "dial up" their process improvement efforts by adopting something along the lines of IBM Rational Self Check
Modified by ScottAmbler
For several years now I've written various articles and newsletters on the topics of estimating and funding strategies for software development projects, and in particular for agile software development projects. Whenever I talk to people about agile software development, or coach them in how to succeed at it, some of the very first questions that I'll be asked, particularly by anyone in a management role, is how to fund agile software development projects. Apparently a lot of people think that you can only apply agile strategies on small, straightforward projects where it makes sense to do a time and materials (T&M) approach. In fact you can apply agile strategies in a much greater range of situations, as the various surveys
that myself and others are showing time and again. My goal with this blog posting is to summarize the various strategies for, and issues surrounding, the funding of agile software development projects.
There are three basic strategies for funding projects, although several variations
clearly exist. These strategies are:
- "Fixed price". At the beginning of the project develop, and then commit to, an initial estimate based on your up-front requirements and architecture modeling efforts. Hopefully this estimate is given as a range, studies have shown that up-front estimating techniques such as COCOMO II or function points are accurate within +/- 30% most of the time although my July 2009 State of the IT Union survey found that on average organizations are shooting for +/- 11% (their actuals come in at +/- 19% on average, but only after doing things such as dropping scope, changing the estimate, or changing the schedule). Fixed-price funding strategies are very risky in practice because they promote poor behavior on the part of development teams to overcome the risks foisted upon them as the result of this poor business decision. It is possible to do agile on a fixed budget but I really wouldn't recommend it (nor would I recommend it for traditional projects). If you're forced to take a fixed-price approach, and many teams are because the business hopes to reduce their financial risk via this approach not realizing that it actually increases their risk, then adopt strategies that reduce the risk.
- Stage gate. Estimate and then fund the project for given periods of time. For example, fund the project for a 3-month period then evaluate it's viability, providing funding for another period of time only to the extent that it makes sense. Note that stages don't have to be based on specific time periods, they could instead be based on goals such as to intiate the project, prove the architecture with working code, or to build a portion of the system. Disciplined agile methods such as Open Unified Process have built in stage-gate decision points which enable this sort of strategy. When the estimation technique is pragmatic, the best approaches are to have either the team itself provide an estimate for the next stage or to have an expert provide a good gut feel estimate (or better yet have the expert work with the team to develop the estimate). Complex approaches such as COCOMO II or SLIM are often little more than a process facade covering up the fact that software estimating is more of an art or a science, and prove to be costly and time consuming in practice.
- Time and materials (T&M). With this approach you pay as you go, requiring your management team to actually govern the project effectively. Many organizations believe a T&M strategy to be very risky, which it is when your IT governance strategy isn't very effective. An interesting variation, particularly in a situation where a service provider is doing the development, is an approach where a low rate is paid for their time which covers their basic costs, the cost of materials is paid out directly, and delivery bonuses are paid for working software. This spreads the risk between the customer/stakeholder and the service provider. The service provider has their costs covered but won't make a profit unless they consistently deliver quality software.
The point is that there are several strategies for funding agile software development projects, just like there are several strategies for funding traditional software development projects. My experience is that fixed-price funding strategies are incredibly poor practice which increases the risk of your software development projects dramatically. I recognize how hard it can be to change this desire on the part of our business stakeholders, but have also had success changing their minds. If you choose to perservere, which is a difficult decision to make, you can help your organization's decision makers to adopt more effective strategies. Like you they want to improve the effectiveness of your IT efforts.Further reading: (In recommended order)
- Something's Gotta Give: Argues for a flexibly approach to funding, schedule, and/or scope.
- Agile on a Fixed Budget: Describes in detail how to take a fixed-price approach on agile projects.
- The Dire Consequences of Fixed-Price IT Projects: Describes in detail the questionable behavior exhibited by IT teams when forced to take a fixed-price approach.
- Is Fixed-Price Software Development Unethical?: Questions the entire concept of fixed-price IT projects, overviewing some of the overwhelming evidence against this really poor practice.
- Reducing the Risk of Fixed-Price Projects: Describes viable strategies for addressing some of the problems resulting from the decision of fixed-price projects.
- Strategies for Funding Software Development Projects: Describes several variations on the strategies described above.
- Lies, Great Lies, and Software Development Project Plans: Summarizes some results from the July 2009 State of the IT Union survey which explored issues related to project funding (among many).
Modified by ScottAmbler
IBM Rational recently published an update to my Agility@Scale e-book, which can be downloaded free of charge. The e-book is a 21 page, 2.3 meg PDF (sorry about the size, guess the graphics did it) . It overviews the Agile Scaling Model (ASM) (which has since been replaced by the Software Development Context Framework (SDCF) ), Disciplined Agile Delivery (DAD), the scaling factors of agility at scale, and ends with some advice for becoming as agile as you can be. In short it's a light-weight coverage of some of the things I've been writing about in this blog the past couple of years. Could be a good thing to share with the decision makers in your organization if they're considering adoption agile strategies.
The Agile Scaling Model (ASM) is a contextual framework for effective adoption and tailoring of agile practices to meet the unique challenges faced by a system delivery team of any size.
The ASM distinguishes between three scaling
- Core agile development. Core agile methods, such as Scrum and Agile Modeling, are self governing, have a value-driven system development lifecycle (SDLC), and address a portion of the development lifecycle. These methods, and their practices, such as daily stand up meetings and requirements envisioning, are optimized for small, co-located teams developing fairly straightforward systems.
- Disciplined agile delivery. Disciplined agile delivery processes, which include Dynamic System Development Method (DSDM) and Open Unified Process (OpenUP), go further by covering the full software development lifecycle from project inception to transitioning the system into your production environment (or into the marketplace as the case may be). Disciplined agile delivery processes are self organizing within an appropriate governance framework and take both a risk and value driven approach to the lifecycle. Like the core agile development category, this category is also focused on small, co-located teams delivering fairly straightforward systems. To address the full delivery lifecycle you need to combine practices from several core methods, or adopt a method which has already done so.
- Agility at Scale. This category focuses on disciplined agile delivery where one or more scaling factors are applicable. The eight scaling factors are team size, geographical distribution, regulatory compliance, organizational complexity, technical complexity, organizational distribution, domain complexity, and enterprise discipline. All of these scaling factors are ranges, and not all of them will likely be applicable to any given project, so you need to be flexible when scaling agile approaches to meet the needs of your unique situation. To address these scaling factors you will need to tailor your disciplined agile delivery practices and in some situations adopt a handful of new practices to address the additional risks that you face at scale.
The first step in scaling agile approaches is to move from partial methods to a full-fledged, disciplined agile delivery process. Mainstream agile development processes and practices, of which there are many, have certainly garnered a lot of attention in recent years. They’ve motivated the IT community to pause and consider new ways of working, and many organizations have adopted and been successful with them. However, these mainstream strategies (such as Extreme Programming (XP) or Scrum, which the ASM refers to as core agile development strategies) are never sufficient on their own; as a result organizations must combine and tailor them to address the full delivery life cycle. When doing so the smarter organizations also bring a bit more discipline to the table, even more so than what is required by core agile processes themselves, to address governance and risk.
The second step to scaling agile is to recognize your degree of complexity. A lot of the mainstream agile advice is oriented towards small, co-located teams developing relatively straightforward systems. But once your team grows, or becomes distributed, or you find yourself working on a system that isn’t so straightforward, you find that the mainstream agile advice doesn’t work quite so well – at least not without sometimes significant modification. Each of the scaling factors introduces their own risks, and when addressed effectively can actually reduce project risk, and for your project team to succeed you will want to identify the scaling factors applicable to the situation that you face and act accordingly. Unfortunately, this is a lot easier said (OK, in this case blogged about) than done.
IBM Rational advocates disciplined agile delivery as the minimum that your organization should consider if it wants to succeed with agile techniques. You may not be there yet, still in the learning stages. But our experience is that you will quickly discover how one or more of the scaling factors is applicable, and as a result need to change the way you work.
I was recently in Bangalore speaking at the Rational Software Conference, which was really well done this year, and visiting customers. In addition to discussing how to scale agile software development approaches, particularly when the team is distributed geographically and organizationally, I was also asked about what I thought about a software factory approach to development. My instinctual reaction was negative, software factories can result in lower overall productivity as the result of over specialization of staff (I prefer generalizing specialists
), too many hand-offs between these specialists (I find close collaboration to be far more effective), and too much bureaucratic overhead to coordinate these activities. I initially chalked it up to these people still believing that software development was mostly a science, or perhaps an engineering domain, whereas my experiences had made me come to believe that software development is really more art than it is a science. Yet, the consistent belief in this strategy by very smart and experienced people started me thinking about my position.
Just let me begin by saying that this blog posting isn't meant to be yet another round in the age old, and relatively inane, "art vs. science" debate within the software development community. That debate is a symptom of versusitis
, a dread disease which particularly plagues the IT industry and which can any of us at any time. There is no known cure, although the combination of experience, open-mindedness, and critical thought are the best inoculation against versusitis that we have so far. In that vein, let me explore the issues as I see them and I will let you think for yourself.
On the one hand software development has aspects of being an art for several reasons. First, the problem definition is never precise, nor accurate, and even when we have detailed specifications the requirements invariably evolve
anyway. The lack of defined, firm requirements requires us to be flexible and to adjust to the situation that we find ourselves in. Second, teams typically find themselves in unique situations, necessitating a unique process and tool environment to reflect this (assuming that you want to be effective, otherwise there's nothing stopping you from having a "repeatable process" and consistent tool environment). Third, software is built by people for people, requiring that the development team have the ability to build a system with a user interface which meets the unique needs of their end users. One has only to look at the myriad UI designs out there to see that surely there is a bit of art going on. Fourth, if software development wasn't at least partially art then why hasn't anyone succeeded at building tools which take requirements as inputs and produce a viable solution that we can easily deploy? It's been over four decades now, so there's been sufficient time and resources available to build such tooling. Fifth, regardless of how much of a scientific/business facade we put over it, our success rate at producing up front detailed cost estimates and schedules speak for itself (see Funding Agile Projects
for links to articles).
On the other hand software development has aspects of being a science for several reasons. First, some aspects of software development have in fact been automated to a significant extent. Second, there is some mathematical basis to certain aspects of software development (although in the case of data-oriented activities the importance of relational theory
often gets blown way out of proportion and I have yet to see a situation where formal methods proved to be of practical value).
What does this have to do with Agility@Scale. As you know, one of the agile scaling factors
is Organizational Complexity, and cultural issues are the hardest to overcome. Whether your organization believes that software development is mostly an art or mostly a science is a cultural issue which will be a major driver in you choice of methods and practices. Organizations which believe that software development is more of a science will prefer strategies such as software factories, model-driven architecture (MDA),
and master data management (MDM)
. And there is ample evidence to support the claims that some organizations are succeeding at these strategies. Although you may not agree with these strategies, you need to respect the fact that many organizations are making them work in their environments. Similarly, organizations which believe that software development is more of an art will find that agile and lean strategies are a better fit, and once again there is ample evidence that organizations are succeeding with these approaches (there's also evidence that agile projects are more successful
than traditional projects, on average). Once again, you may not agree with these strategies but you need to respect the fact that other people are making them work in practice.
Trying to apply agile approaches within an organization that believes software development is mostly a science will find it difficult at best, and will likely need to embark on a multi-year program to shift their culture (likely an expensive endeavor which won't be worth the investment). Similarly, trying to apply a software factory strategy in an organization that believes that software development is mostly an art will also run aground. The bottom line is that one size does not fit all, that one strategy is
not right for all situations and that you need to understand the trade-offs of various strategies, methodologies, techniques, and practices and apply them appropriately given the situation that you face. In other words, it depends! If you are embarking on a software process initiative, and you don't have the broad experience required to effective choose between strategies (very few organizations do, although many believe otherwise), then you should consider Measured Capability Improvement Framework (MCIF)
to help increase your chance of success.
I recently wrote a detailed article about Large Agile Teams that was a detailed walkthrough of how to structure agile teams of various sizes. I suspect that this is the most comprehensive online discussion of this topic. The article addressed the following topics:
Organizing Agile Teams. The article starts with a summary of the results of some industry research that I've done regarding the size of agile teams, showing that agile techniques are in fact being successfully applied on a variety of team sizes. It then goes into detail describing the organization structure of agile teams at various sizes. The article starts with a discussion of small agile teams, covering the common rhetoric of how to organize such a team and then making observations about what actually happens in practice. It then walks through two approaches to organizing medium sized teams of 15 to 50 people - a structure for a single team and a structure for a team of teams. Finally, it walks through how to organize a large agile program of 50+ people, focusing a fair bit on the need for a leadership team to coordinate the overall activities within the program. This advice is similar to what is seen in the SAFe framework although proves to be a bit more flexible and pragmatic in practice.
Supporting Large Agile Teams. The leadership structure to support a large agile team is reasonably straightforward once you understand the issues that such a team faces. In this section the article overviews the need for three important sub teams within your overall leadership team: The Product Delivery Team, The Product Management Team, and The Architecture Team. It also describes the need for an optional Independent Testing/Integration Team, something misleadingly labeled an integration team in SAFe, that reflects some of the known agile testing and quality practices that I've been writing about for several years.
Organizing subteams. The article includes a detailed discussion for how to organize the work addressed by agile sub teams within a large agile program. These strategies include feature teams, component teams, and internal open source teams. As you would expect with the Disciplined Agile Delivery (DAD) framework, the article clearly summarizes the advantages and disadvantages of each approach on provides guidance for when (not) to apply each one. I suspect you'll find this portion of the article to be one of the most coherent discussions of the Feature vs. Component team debate.
Tailoring agile practices. The article provides a detailed overview of how the various DAD process goals are tailored to address the issues faced by large teams. This advice includes: Do a bit more up-front requirements exploration; Do a bit more up-front architectural modelling; Do a bit more initial planning; Adopt more sophisticated coordination activities; Adopt more sophisticated testing strategies; and Integrate regularly. My hope is that you find this part of the article very illuminating regarding how the DAD framework provides flexible and lightweight advice for tailoring your approach to address the context of the situation that you face.
Other Resources. The article ends with a collection of links to other resources on this topic.
I welcome any feedback that you may have about Large Agile Teams.
At Agile 2009 in August Sue McKinney, VP of Development Transformation with IBM Software Group, was interviewed by DZone's Nitin Bharti about IBM's experiences adopting agile techniques. There are over 25,000 developers within IBM Software Group alone. Follow the link to the interview
to view it online (there is also a text transcript posted there. There's some great insights into the realities of scaling agile in large teams, in distributed agile development, and in particular how to transform a large organization's development staff.
Modified by ScottAmbler
I was recently involved in an online discussion about how to calculate the benefits realized by software development teams. As with most online discussions it quickly devolved into camps and the conversation didn’t progress much after that. In this case there was what I would characterize as a traditional project camp and a much smaller agile/lean product camp. Although each camp had interesting points, the important thing for me in the conversation was the wide cultural and experience gap between the people involved in the conversation.
The following diagram summarizes the main viewpoints and the differences between them. The traditional project camp promoted a strategy where the potential return on investment (ROI) for a project would be calculated, a decision would be made to finance the project based (partly) on that ROI, the project would run, the solution delivered into production, and then at some point in the future the actual ROI would be calculated. Everyone was a bit vague on how the actual ROI would be calculated, but they agreed that it could be done although would be driven by the context of the situation. Of course several people pointed out that it rarely works that way. Even if the potential ROI was initially calculated it would likely be based on wishful thinking and it would be incredibly unlikely that the actual ROI would be calculated once the solution was in production. This is because few organizations are actually interested in investing the time to do so and some would even be afraid to do so. Hence the planned and actual versions of the traditional strategy in the diagram.
The agile/lean camp had a very different vision. Instead of investing in upfront ROI calculation, which would have required a fair bit of upfront requirements modelling and architectural modelling to get the information, the idea was that we should instead focus on a single feature or small change. If this change made sense to the stakeholders then it would be implemented, typically on the order of days or weeks instead of months, and put quickly into production. If your application is properly instrumented, which is becoming more and more common given the growing adoption of DevOps strategies, you can easily determine whether the addition of the new feature/change adds real value.
Cultural differences get in your way
The traditional project camp certainly believed in their process. In theory it sounded good, and I’m sure you could make it work, but in practice it was very fragile. The long feedback cycle, potentially months if not years, pretty much doomed the traditional approach to measuring benefits of software development to failure. The initial ROI guesstimate was often a work of fiction and rarely would it be compared to actuals. The cultural belief in bureaucracy motivated the traditional project camp to ignore the obvious challenges with their chosen approach.
The agile/lean camp also believed in their strategy. In theory it works very well, and more and more organizations are clearly pulling this off in practice, but it does require great discipline and investment in your environment. In particular, you need investment in modern development practices such as continuous integration (CI), continuous deployment (CD), and instrumented solutions (all important aspects of a disciplined agile DevOps strategy). These are very good things to do anyway, it just so happens that they have an interesting side effect of making it easy (and inexpensive) to measure the actual benefits of changes to your software-based solutions. The cultural belief in short feedback cycles, in taking a series of smaller steps instead of one large one, and in their ability to automate some potentially complex processes motivated the agile/lean camp to see the traditional camp as hopeless and part of the problem.
Several people in the traditional project camp struggled to understand the agile/lean approach, which is certainly understandable given how different that vision is compared with traditional software development environments. Sadly a few of the traditionalists chose to malign the agile/lean strategy instead of respectfully considering it. They missed an excellent opportunity to learn and potentially improve their game. Similarly the agilists started to tune out, dropping out of the conversation and forgoing the opportunity to help others see their point of view. In short, each camp suffered from cultural challenges that prevented them from coherently discussing how to measure the benefits of software development efforts.
How Should You Measure the Effectiveness of Software Development?
Your measurement strategy should meet the following criteria:
Measurements should be actioned. Both the traditional and agile/lean strategies described above meet this criteria in theory. However, because few organizations appear willing to calculate ROI after deployment as the traditional approach recommends, in practice the traditional strategy rarely meets this criteria. It is important to note that I used the word actioned, not actionable. Key difference.
There must be positive value. The total cost of taking a measure must be less than the total value of the improvement in decision making you gain. I think that the traditional strategy falls down dramatically here, which is likely why most organizations don’t actually follow it in practice. The agile/lean strategy can also fall down WRT this criterion but is much less likely to because the feedback cycle between creating the feature and measuring it is so short (and thus it is easier to identify the change in results to the actual change itself).
The measures must reflect the situation you face. There are many things you can measure that can give you insight into the ROI of your software development efforts. Changes in sales levels, how often given screen or function is invoked, savings incurred from a new way of working, improved timeliness of information (and thereby potentially better decision making), customer retention, customer return rate, and many others. What questions are important to you right now? What measures can help provide insight into those questions? It depends on the situation that you face, there are no hard and fast rules. For a better understanding of complexity factors faced by teams, see The Software Development Context Framework.
The measures should be difficult to game. Once again, traditional falls down here. ROI estimates are notoriously flakey because they require people to make guesses about costs, revenues, time frames, and other issues. The measurements coming out of your instrumented applications are very difficult to game because they’re being generated as the result of doing your day-to-day business.
The strategy must be compatible with your organization. Once again, this is a “it depends” type of situation. Can you imagine trying to convince an agile team to adopt the traditional strategy, or vice versa? Yes, you can choose to improve over time.
Not surprisingly, I put a lot more faith in the agile/lean approach to measuring value. Having said that, I do respect the traditional strategy as there are some situations where it may in fact work. Just not as many as traditional protagonists may believe.
Modified by ScottAmbler
In 2009 I wrote a white paper entitled The Agile Scaling Model (ASM): Adapting Agile Methods for Complex Environments for IBM Rational. Apparently it's been taken down, which I think is unfortunate as it contains some interesting ideas that your organization may be able to benefit from.
The original white paper addresses several key issues:
It provides and explains a definition for disciplined agile delivery. A more up to date discussion of DAD can be found on the Disciplined Agile Delivery site.
It describes criteria to determine is a team is agile. I've explored this issue via several surveys over the years since then. See the January 2013 How Agile Are You? results.
It describes the ASM, which distinguishes between core agile development techniques, disciplined agile delivery strategies, and agility at scale. The ASM was superceded in early 2013 by the Software Development Context Framework (SDCF). Perhaps this is why the ASM paper was taken down??
It overviews the eight scaling factors which a delivery team may face, scaling factors which motivate changes in the process that you will follow and the tools that you will adopt. The SDCF provides my recent thoughts regarding scaling factors. I have also run various IT Surveys over the years exploring how well organizations fare at scaling agile.
It describes the implications of the ASM. My blog posting Scaling Agile: Start with a Disciplined Foundation covers this very well.
It argues that you should strive to be as agile as you need to be, and that will be driven by the situation that you face.