What data preparation an AI project really needs, and how to do it without overbuilding.
dgm is an independent osFoundry integration partner — not affiliated with osFoundry’s maker (OS LLC), and dgm has no completed client integrations yet.
Data preparation is where many AI projects quietly succeed or fail. The good news: you usually need good data for one use case, not perfect data everywhere. Here is how to approach it.
What preparation actually means
For most business AI, preparation means making the data your specific use case needs accessible, reasonably clean, and properly permissioned — not a years-long data-cleanup programme.
Doing it proportionately
Identify the data the use case needs, fix obvious quality issues, structure it for retrieval if needed, and confirm you are allowed to use it (PIPEDA, Law 25). Avoid over-engineering.
Keeping it controlled
Decide where the data lives and who can access it, especially for personal or confidential information. osFoundry’s managed cloud pins data to US, EU or Japan — it does not currently offer a Canadian managed region. For data that must stay in Canada, the honest path is self-hosting osFoundry (BYO Cloud) inside a Canadian cloud region such as AWS Canada (Montréal/Calgary), Azure (Toronto/Quebec City) or Google Cloud (Montréal), or running models locally on-device.
Where dgm fits
dgm is an independent integration partner that helps Canadian businesses adopt osFoundry — scoping a first use case, handling the build, and connecting AI to the systems you already run. dgm is independent of osFoundry’s maker (OS LLC) and has no completed client integrations yet, so everything described here is a service offered, not a past result. If you want to scope a practical first project, dgm can help you map it out.