Building or Buying: The Agentic Analytics Dilemma
Companies face a critical decision between building or buying agentic analytics solutions, as initial DIY successes often mask long-term challenges. While custom systems may work in early stages, they frequently fail to scale reliably across teams and use cases. The key issue is not whether a solution can be built, but whether it can be maintained, standardized, and trusted over time.
- ▪DIY agentic analytics setups often appear successful in early demos but struggle with scalability and consistency.
- ▪Ambiguous business logic, such as undefined metrics like 'active customer,' leads to unreliable and inconsistent AI-generated answers.
- ▪Answer quality depends more on structured context than on model performance, making governance essential for reliability.
- ▪Two sources of noise—natural language variation and unstructured metric definitions—compound inaccuracies in agentic systems.
- ▪Maintainability becomes a major bottleneck when metric definitions change or new data sources and teams are introduced.
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Databao Agentic platform with modular AI tools and a governed semantic layer for any data stack All AI Data Data Science Building or Buying: The Agentic Analytics Dilemma Claire Amaouche Every company, when evaluating new tools, technologies, or infrastructure, eventually runs into the same question: “Should we build this ourselves or buy a ready-made solution?” The default answer is often: “We can do it ourselves.” And technically, that’s true. But the real question isn’t whether it’s possible. It’s how fast and how efficiently you can get there.
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