There has been a surge of excitement around data fabrics and pipelines this past week — and for all the right reasons. At Databahn, we’ve been guided by a few core beliefs since day one:
1. Independent pipelines are the future: Data may be the new oil, but the value comes from a neutral, producer- and consumer-agnostic pipeline. Our “data Switzerland” delivers the right data, in the right format, to the various consumers and AI models.
2.Context is everything: Analysts and SREs shouldn’t be stitching data, definitely not during crisis or mid-incident. Context-first pipelines reduce friction and accelerate outcomes. Also, AI models depends on quality data: If you don’t shift left — normalize and enrich upfront — you’ll pay for it in compute and token costs. Clean, enriched data is the foundation of every effective agent.
3. Decentralized data stores are the direction things are heading. Your business may rely on a data pond or data lake of its choice, IT may select another, and cybersecurity may operate with an entirely different flavor.
Trying to co-locate all of this data into a single solution often leads to long, tedious projects and the hidden egress costs can add up over time.
The best strategies we see involve customers sending only critical data through independent pipelines for real-time detections, while leaving the rest in its system of origin. Some also leverage cold-storage effectively. Later, using a federated AI-powered search enables virtual-co-location of data and delivering the benefits of centralization without the overhead.
4.The future is multi-model, not mono-model: No single model will win every task. You need freedom to route workloads to the best model for cost, latency, capability, or governance — and to swap models as better ones emerge. Own your prompts, guardrails, and evals; keep them portable across providers.
5. AI will be the strongest consumer of your data: Models have everything except your institutional context. Independent, AI-ready pipelines — and the ability to choose and change models — are how you bridge that gap.
Net-net, our guiding principle is clear, architect for control. Control of your data and control of your model choices. Use open formats and frameworks, OCSF, OTEL.. Avoid vendor lock-in, design for BYO/replaceable models, stay agile, and prepare for continuous AI upgrades. The organizations that do this won’t just keep pace with current challenges but also future proof the AI wave.
At Databahn, we’re building for exactly that future. #dataneutrality
