As the Apps Global CTO for Microsoft Services, Lenny Fenster sees the need for estimation in many shapes and sizes throughout the world. In his twenty years at Microsoft, Lenny has also seen many different attempts to improve how Microsoft Services estimates time and effort for software development projects. Not all of them have hit the mark. In this presentation for the QSM 2018 Virtual Conference, Lenny talks about the evolution his team is driving in Microsoft Services to improve the maturity, consistency, and defensibility of software estimation for some of the largest and most complex software projects in the world. He talks specifically about the intentional separation of scope and estimation and the use of SLIM as a key ingredient in the success they are now having. Estimates are now done much quicker, reducing the time to run an estimate from days to just 4.5 hours.  

Lenny was gracious enough to answer questions throughout his presentation about the estimation process at Microsoft Services. This sparked great participation from our audience, who asked a number of questions worth resharing. Here are the highlights:

Q: Did you experience any resistance among the architects in changing the way they did estimation to a new approach?­

In many agile environments, the budget, team size, and schedule are fixed based on an organization’s predetermined targets for sprints or iterations. This leads many project managers to question if software estimation is even necessary. The problem is, without a reliable size estimate, the amount of functionality promised within the time and money constraints could be difficult to achieve and could cause the product delivery to be short on features, or late and over budget.

This is where scope-level estimation tools come into play. They can help evaluate whether targets are reasonable and, even if the schedule and budget are both set in stone, they can help figure out how much work can be delivered. This type of analysis helps set customer expectations and provides data driven leverage for negotiations.

The best estimation tools leverage empirically-based models, industry analytics, and historical data. They can even be used before iteration level planning takes place. They ensure that the overall goals are reasonable before detailed plans are developed. 

In the three views below, we see an estimate generated from a “Time Boxed” method. This is where the product manager was able to input the predetermined time, a productivity measure (PI), and a team size, to see how many story points could be completed within the set constraints. The analysis also includes a “sanity check” of the estimate, comparing it to an agile industry trend from the QSM Industry Database and their own agile historical data.

The application of contingency buffer, more commonly known as “padding” or “management reserve” is the final step in any project estimation process.  The most common practice is for the estimator to use an intuitive multiplier which is added to base estimate.  Unfortunately, everyone has a different multiplier which is shaped by their own personal bias about risk and it is hidden in their head.  This creates a fundamental problem with transparency and consistency within most organizations.

Fortunately, there's a better way.  One solution is to define and configure agreed upon standards that are matched to specific business risk situations.  These should be collaboratively agreed to by all the stake holders in the organization.  Then they can be codified into a configuration that can be selected at the time when contingencies are typically applied to an estimate.  This helps solve the consistency issue.

To attack the transparency issue, you can use a technique of overlays to visualize the contingency in comparison to the base estimate. 

Scaled Agile (SAFe) is a methodology that applies Agile concepts to large complex environments.  QSM recently worked with an organization that had implemented SAFe to develop an estimation methodology specifically tailored to it.  This article discusses how it was implemented.

Software estimation typically addresses three concerns: staffing, cost/effort, and schedule.  In the SAFe environment, however, development is done in program increments (PI) that in this case were three months in duration with two-week sprints throughout.  Staffing was set at a predetermined level and varied very little during the PI.  Thus, the three variable elements that are normally estimated (staff, cost/effort, and schedule) had already been determined in advance.  So, our job was done, right?  Wrong!  What remained to be determined was capacity: the amount to be accomplished in a single PI.  And that was a very sore “pain point” for the organization. 

I was recently reading an article by Moira Alexander titled “Why Planning Is the Most Critical Step in Project Management” and I was stuck by her observation that one of the primary reasons that projects fail is because they commit to unrealistic expectations.  In my 35 years of experience, I believe this is the number one reason projects fail.  Yet it is competence that few organizations or product owners ever get good at.

 Today there are good simulation tools that make it simple to establish realistic project boundaries.  The results can be used effectively to communicate and negotiate expectations with clients.  

For example, imagine that you are a product owner planning out your next release.  Your team of 10 people has been working on a 5-month release cadence.   A backlog refinement has shown that there are approximately 100 story points to be completed in this release.  The project plan is shown in the figure below.

However, there are some uncertainties and we need to deal with them in a realistic way.  Since the schedule and the team size are fixed, the only area that can give is the functionality.   Simulations are a great way to quantify uncertainty.  In our case, we are confident in our team’s productivity and labor cost, but we are somewhat more uncertain about the new capabilities in this release.   It is easy to adjust the uncertainty settings and run a business simulation.  The uncertainty slider bars are in the image below. 

Now that the baseline estimate has been created, and stakeholders feel their inputs and concerns have been addressed, we as purveyors of the estimate have done our job.  In the world of IT project measurement, many organizations will deservedly feel accomplished that they have armed their development staff with an empirically based roadmap from which to navigate the next x number of months toward delivering a product.  Now let the construction and testing begin!  But wait, there’s more!

It’s always wise to have a sound estimate, but for added assurance of hitting the budget, schedule, staffing and risk targets, organizations have the option of tracking the project mid-flight. Just as estimating is conflated with planning, tracking can be equally confused with other one-dimensional monitoring of projects underway.  So many things can change from the time an estimate is created to the time the first iterations are built.  It’s likely that our estimate assumptions will change after some time has passed into the construction process, unless we have reacted to inevitable unforeseen forces.  For example, requirement changes, staff turnover, management demanding the project x weeks/months earlier, but still expecting all the original functionality.  These are all very real events that are thrown at the PM after the project is underway.  We at QSM have provided a solution for this since the mid-80’s in SLIM-Control, a module in our SLIM Suite.

1. Capture some historical data on your projects and keep it simple. The more data, the better, but you can get a good start to your estimation program with just a few projects and a small amount of data from each of those projects. Focus on the core metrics: size, duration, reliability, productivity, and effort. 
2. Estimate at the release level before detailed planning takes place. This will enable you to tailor your detailed plan to goals that are reasonable. Many analysts spend hours laying out detailed plans for projects that end up over budget and late because they don’t figure out the big picture first. 
3. Use an empirically-based model that enables you to manage uncertainty. When making big decisions, it’s important to see the 90% chance compared to the 50%. 
4. Sanity-check your estimates with industry analytics. It’s always good to see typical cost and duration trends from projects that are similar to yours. 

Today more than ever we have access to large amounts of information. You've probably heard the term "big data," which in essence is having access to large amounts of data and examining the trends in that data. But many executives want to know how they can leverage this information to solve business problems, like lowering IT costs. One way is to use the data to do a better job of estimating IT projects.

Better estimating helps avoid signing up to schedules and budgets that are unrealistic; it helps avoid overstaffing a project or a portfolio of projects; and it helps calculate how much work can be completed within project constraints. In addition, it improves communication internally across the enterprise and externally between the vendor and the client. You can apply estimation to in-house projects and you can use it to generate better proposals or to do a better job of evaluating proposals.  It can also help you negotiate more effectively.

To do a better job of estimating, you need to make good decisions regarding which metrics to leverage. You might have thousands of data points, but it's important to streamline the focus to the core release level metrics: cost, duration, effort, reliability, and productivity.  Next, you need to find a centralized place to organize and store the data so you can analyze it. There are tools out there that can help you. In the view below, you can see a portfolio of projects stored in a centralized place with the ability to manage the access and security.

In any development methodology, we throw around the word “estimate” freely not really understanding how it’s interpreted by many.  In many cases, an estimate, regardless of its content and process by which it was created, is received implicitly as a pin point number with the accuracy of multiple decimal points.  This presents a problem for all parties involved.

I recently had a discussion with a gentleman who told me that prior to using our SLIM tool, the estimates in his organization were arrived at by casual hallway conversations, often started with, “how much do you think this will cost, and how long will it take?”  A typical response is, “hmmm, I’d say about 6 months and $500K.”  That innocent musing then becomes the information upon which business decisions are based, leadership bonuses may be won or lost, and the credibility of the dev team is on the line.

I’d strongly recommend adhering to some definitions when talking estimates.  These definitions will help mitigate potential misunderstandings around the agreement of what makes an estimate: