I’ve been a programmer and IT enthusiast for 30 years (since the zx spectrum) and concentrated on AI (neural nets & genetic algorithms) at University. My principle skills are concentrated on Enterprise and Solution Architecture and managing effective developer teams.
I enjoy the mix between technical and business aspects; how technology enables and how that (hopefully) improves profit/EBITDA & reduces cost-per-transaction, the impact upon staff and how to remediate go-live and handover, and risk identification and mitigation. My guiding principle is “Occams Razor” that simplicity is almost always the best option by reducing complexity, time to build, organisational stress and longer term costs.
Data led Software Architectures
In recent years the emphasis has been on "emerging" architectures where the design is iterative and occurs alongside typical agile sprints. My view is that this has led to larger development costs (and more fragile code due to increased technical debt) for a number of projects. Not just from poorly performing architectures requiring extra changes or more hardware allocated to reach its potential, but also to prevent over-engineering.
Instead a more "data led" approach is required so the correct architecture based upon the performance criteria (and easier to manage into the future) can be realised.
Volumetrics
Although unfashionable the term Volumetrics is a useful one in software architecture and development.
Volumetrics seeks to capture the low, expected and "stretch" (i.e. wouldn't it be amazing if we hit those numbers!) values for the key criteria of system to be developed.
The problem is that there are no magic set of Volumetrics we can mechanically apply and determine for different developments. This is because development requirements vary widely, even in a single industry, based upon the client's requirements.
How to create Volumetrics
In the case where you are lucky enough to be building a new version of a platform/tool/app then you're already in an enviable position of knowing a lot of data about it's current use. Most of which is amenable to the new development.
If you are green-field developing then, if possible, begin with the business model/reasoning behind an Return on Investment (ROI) planning. In this is usually the prospective benefits of the system and how it is to be achieved. How many users are expecting to use the system? In what period? How are user's paying for the service? etc.
As a rough guide one can start thinking around volumetrics by examining the utilisation of the system. Is it user based? Is it used by another computer system? Once this is known then we know our primary actor that affects the system.
From there we can start to work through the utilisation model for those actors. For example if we have a website learning management platform then how many times a year will the typical user login? How many courses will they take over a year? Are there any "hot spots" where a lot of users will use the system concurrently?
Using that data we can start to establish a simple excel data set: the volumetric type (is it allied to people or a system etc), the volumetric, the period of measure (second, minute..week.. year?), the minimum number, the expected number, the stretch number.
We can then refine the data to be more meaningful for tech teams by reducing the period of time against each and understanding the weighted averages for the expected and stretch.
For example, if we are building a learning management tool
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