Starting an IT project without proper estimation is equivalent to setting off on a long journey without a map or a fuel plan. You move forward, yes, but reaching your destination on time, or at all, will be pure luck.
Similarly, accurately estimating the cost of software development is not simply a matter of guesswork, but rather a disciplined practice based on the use of all available clear data, analysis, and previous experience. It is not whether we create a definitively known future, but rather we are able to reduce the uncertainty to a point where we can proceed and create a plan for our work and timelines.
What is a project estimation?
A project estimate is your plan on how long it will take to do the work, and what the cost is in relation to time, effort, and resources. It is like the roadmap for the project manager and his team, which directs them from the first task all the way to delivery of the project.
Project estimation methods
Estimating your projects does not have to be actually a shot in the dark. There are some great ones, very configurable methods. Approaches that can obtain stable results and be adapted to diverse kinds of projects.
When we talk about methods of estimating, be mindful that one method alone will not give you a complete assessment but using multiple of them will often uncover blind spots, and allow better decision making.
1. Bottom-up assessment
The concept is simple. You don’t try to estimate the entire cost of a feature or a task as a whole if you can’t estimate all its components individually with any accuracy. No, you break the work and required resources into pieces. Each piece is evaluated on its own, and then the totals are combined to build a more accurate overall estimate.
The bottom-up assessment is conducted by generalization. As we progress, we move towards aggregating to a total. From the particular to the general.
It works because it is reliable. And it gets power from the fact that it tells us the truth. Its accuracy depends on how detailed the costs are estimated at the lowest levels of decomposition. As a statistical matter, the final estimate gets sharper and more accurate the more granular the data.
2. Top-down assessment
This will have less accuracy than the prior method and is the opposite of the bottom-up approach. Here, teams often use a Work Breakdown Structure (WBS) to deconstruct the costs. It forms a hierarchy by using the total project (higher level) and breaking it into pieces (a lower level) to split the project into manageable pieces.
However, the WBS is being reversed when compared to the last method. And in general, it is appropriate if one does not have a reasonable, comprehensive WBS, if you do not have enough organizational experience as to the type and quantity of resources and materials necessary to get the project done.
It is a fairly quick method that is justified in the early stages of a project. When the project is being evaluated for viability. And the decision on whether to spend resources on more detailed planning and evaluation is unclear.
3. Valuation by analogy
The head notes, estimates, and cost data for a similar work previously performed in other projects are the only support for the method. Since the estimation is based on the forecasts of past experiences, statistical data, and experts’ opinions matter the most to justify the final assessment. The most promising aspect of this approach is its valid projection. The reason is that it provides insights not only about the planned cost of the work being analyzed, but also about its actual cost. And it’s a difference between the planned estimate and the actual cost in particular that can add value to the project manager’s consideration.
4. Parametric assessment
This method is based entirely on statistical dependencies and mathematical models. To figure out the value, it uses a statistical relationship between the process cost and certain data (parameters) extracted from an analysis of historical data. Think of it like trying to calculate how much you’d spend to build a house. So, without going back to the drawing board, you can easily see what a new house might cost, based on an average cost per square metre from past projects.
The systematic nature of parametric modeling provides accurate and well-founded estimates, especially in combination with analogous estimation.
5. Expert assessment
The classic and multipurpose method when a certain expert from the relevant industry acts as an interviewee. When experts evaluate, they use intuition, instincts, and, most importantly, experience. But of course, there is some subjectivity in this, as you are relying on judgment and not on objective data.
The value of accurate estimation
Because it tells everyone what’s possible, what’s anticipated, and where the risks are, realistic estimation provides the basis of any IT project.
Everything and all at once. This is what a startup wanted when they came to us with the idea of a platform that could connect city buyers and local farmers directly. Real-time logistics tracking, AI pricing, and, believe it or not, predictive analysis. It sounded like a pretty great idea, but was the world ready for something this complex?
We started with a discovery phase, conducting real market research, interviews with potential users, a cost breakdown, and testing key assumptions. It turned out that people didn’t care about AI or fancy features, especially in this field of marketplaces. Despite the fact that the results were equally derived from both younger and older generations, still, the majority just wanted a simple, easy way to buy fresh produce.
Through careful estimation and validation, we were able to show that adding all the “extras” would have inflated the budget by 70% without delivering more value. This insight came directly from breaking down project costs, timelines, and user impact during the discovery phase. So, we suggested that the client abandon the development of such a platform and instead, advised launching a lead MVP with basic marketplace features and only the core logistics module.
The discovery phase saved more than $120.000 they would have spent on features users didn’t really need. Stories like this prove why estimation isn’t optional. Without it, projects risk overspending on features users don’t care about or missing market opportunities altogether.
Final thoughts
Estimation isn’t about predicting the future. But about reducing uncertainty to make smarter decisions. With a solid estimate, teams avoid the traps of unrealistic budgets and missed deadlines. The project is set on the right track, saving time, money, and effort from day one.
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