The real cost of a fragmented data and AI stack
The licences are the visible cost. The hand-offs between tools are the expensive one.
Eight tools, seven borders
A typical organization assembles its data and AI capability one purchase at a time: something for integration, something for analytics, a machine learning platform, an AI platform, automation, governance, an application builder and monitoring for all of it.
Each tool is defensible on its own. The cost appears at the borders between them, because every border is a place where work is exported, re-imported, re-secured and re-explained.
Where the money actually goes
Fragmentation rarely shows up as a line item. It shows up as time.
- Integration work that connects tools to each other instead of to the business.
- Definitions that drift, because a metric is calculated in three places.
- Lineage that stops at each product's edge, so tracing a number means interviewing people.
- Permissions configured separately in every tool, and audited separately too.
- Projects that stall between a working model and a working process.
Consolidate where it creates value
The answer is not to replace everything at once. Large estates do not move that way, and they should not have to.
A more realistic approach is a connected operating layer: use the technology you already have, connect it, and consolidate only the workflows where one environment is clearly better than several. Start with a single use case that is useful on its own, and let the next one build on it.
A simple test
Pick one decision your organization makes every week. Count the tools the data passes through before the decision, and the tools involved in acting on it afterwards. If the number surprises you, that path is a good first candidate.
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