To get to the meat of things... here is my recipe for estimation success... It won't surprise you to see me say that the key elements of estimation are:
- Understand the task, i.e. what needs to be produced
- Comparison with similar tasks already completed
- Decomposition of tasks into smaller, more measurable activities
- Attention to detail (sufficient for our purposes)
The first (requirements) is obvious, but is very often not given enough attention to detail, resulting in an incomplete set of items to be produced. In a SAS context, this list might include technical objects such as Visual Analytics reports, stored processes, information maps, macros, DI Studio jobs, table metadata, library metadata, job schedules, security ACTs and ACEs, userIDs, data sets, views, and control files; on the business side, your list might include a user guide, training materials, a schedule of training delivery, a service model that specifies who supports which elements of your solution, a service catalog that tells users how to request support services, and a process guide that tells support teams how to fulfil support requests; and on the documentation side your list might include requirements, designs & specifications, and test cases.
Beyond identifying the products of your work, you'll need to identify what inputs are required in order to allow you to perform the task.
I'll offer further hints, tips and experience on requirements gathering in a subsequent article.
With regards to comparisons, we need to compare our planned task with similar tasks that have already been completed (and hence, we know how many people worked on them and how long they took). When doing this we need to be sure to look for differences between the tasks and make sure we take account of these by increasing or decreasing our estimate above or below the time it took for the actual task. In doing this, we're already starting to decompose the task because we're already looking for partial elements of the task that differ.
Decomposition is the real key, along with a solid approach to understanding what each of the sub-tasks does. As you decompose a unique task into more recognisable sub-tasks, you'll be able to more confidently estimate the effort/duration of the sub-tasks.
As we decompose the task into smaller tasks, we must be sure that we are clear which of the decomposed tasks is responsible for producing each of the deliverable items. We need to look out for intermediate items that are produced by one sub-task as an input to another sub-task; and pay the same attention to inputs - we must be certain that we understand the inputs and outputs of each sub-task.
I'll offer a deeper article on decomposition in a subsequent article.
You're probably thinking that requirements, comparisons, and decomposition are quite obvious. So they should be! We already established that all perform estimations every day of our life. All I've done is highlight the things that we do subconsciously. But there is one more key element: attention to detail. We must pay attention to understanding each sub-task in our composition. We must be sir to understand its inputs, its outputs, and how we're going to achieve the outputs from the inputs.
Having a clear understanding of the inputs, the outputs and the process is crucial, and it can often help to do the decomposition with a colleague. Much like with pair programming, two people can challenge each other's understanding of the task in hand and, in our context, make sure that the ins, outs and process of each sub-task are jointly understood.
I hope the foregoing has helped encourage you to estimate with more confidence, using your existing everyday skills. However, we should recognise that the change in context from supermarket & driving to software development means that we need a different bank of comparisons. We may need to build that bank with experience.
To learn more about estimating, talk to your more experienced colleagues and do some estimating with them. I'm not as great fan of training courses for estimation. I believe they're too generalistic. In my opinion, you're far better off learning from your colleagues in the context of the SAS environment and your enterprise. However, to the extent that some courses offer practical exercises, and those exercises offer experience, I can see some merit in courses.