What we have learned.
Practical writing from our engagements on data platforms, master data, analytics, and AI in production.
Modernizing Informatica PowerCenter: what to settle before you convert a mapping
Most PowerCenter programs start by converting mappings. Here is what to settle first, covering what really runs, where the logic lives, how to prove the new pipelines match, and how you will operate them.
ArticleBuilding an AI assistant that respects document permissions
Connect a model to SharePoint or Confluence and it can answer from files the person asking was never allowed to open. Here is how to carry permissions through to every answer.
GuideCheck the data before you fund the project
Data and AI projects are often approved on a slide and discover the data problems weeks into the build. A short readiness check on the real data behind one decision avoids that.
Point of viewDefine the metric once, before you build another dashboard
When every report calculates net sales its own way, meetings turn into reconciliation sessions. We think each metric should be defined once, with a named owner and tests, before anyone builds on it.
GuideProve your match rules before you configure an MDM platform
Most master data programs find out whether their matching works months in, after the platform is set up. Here is how to test match rules on a sample of your own records first.
GuideHow to test a GenAI assistant before every release
Prompts, models, and retrieval settings change all the time, and most teams judge quality by trying a few questions. A test set written by your experts, run on every change, catches problems before users do.
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