Real-world data analysis often requires joint reasoning over heterogeneous artifacts such as tables, charts, and textual reports. Training reliable multimodal large language model (MLLM) agents for this setting remains challenging, as large-scale aligned training data are scarce, and outcome-only rewards are too coarse to distinguish grounded cross-source analysis from spurious success. We introduce OmniDA, a training recipe that addresses these two challenges with a scalable multimodal data-synthesis pipeline and an Evidence-Aware Process Reward (EAPR) that supervises both evidence recovery and procedural validity. Through this pipeline, we construct OmniDA-11K, a high-quality training corpus for multimodal data analysis. When instantiated with Qwen3-VL-8B, OmniDA improves over the corresponding agentic baseline by 19.2 points on average across five challenging benchmarks and achieves the strongest overall performance among existing open-source data-analysis agents. These results highlight the value of supervising evidence recovery and analytical procedure beyond final-answer correctness.