Splunk tells you what happened. Datadog tells you something is broken. Snowflake holds your data. None of them tell you what to do. An Enterprise AI Data Fabric connects to every system you already own and runs a reasoning layer on top, converting dashboards into decisions, alerts into root cause hypotheses, and data warehouses into strategic intelligence engines.
The modern enterprise runs on 900 SaaS applications on average (Productiv, 2024). Each generates data. Most of that data reaches a warehouse or a monitoring platform. But the gap between data and decision remains a human problem: a team of analysts, SRE engineers, procurement leads, and finance managers who manually stitch signals together to answer questions like "why is churn accelerating," "which contract is costing us the most," or "what caused last night's incident."
Every major cloud vendor has told you the answer is more dashboards, more alerts, more queries. Splunk gives you a search bar. Datadog gives you a graph. Snowflake gives you a query interface. None of them close the gap from data to decision. That gap is where $50 billion in enterprise productivity losses live annually, according to IDC's 2024 Knowledge Worker Productivity study. It is also where most AI investments stall: point tools that add another dashboard to a stack that already has too many.
The Enterprise AI Data Fabric is a different architectural bet. Rather than adding a new data product, it places a reasoning layer above the data products you already own. An agentic orchestration layer routes questions to the right source systems, formulates and executes queries, triangulates signals across silos, and returns structured decisions with evidence and recommended actions. The human role shifts from data retrieval to decision validation. That is the shift that justifies calling it enterprise transformation rather than a tool purchase.
All processing occurs within customer VPC. No training on customer data. Connectors are read-only by default.
The Fabric is not a product with fixed features. It is an orchestration capability that routes any business question to the right data source and returns a decision-ready brief. Representative questions across buyer personas:
| Capability | Splunk / Datadog | Snowflake + BI Tool | AI Data Fabric |
|---|---|---|---|
| Incident root cause | Alert + search bar. Human investigates. | Not applicable. | Ranked hypothesis with evidence in 4 min. |
| Churn risk signal | Not applicable. | Dashboard refresh cycle; analyst interprets. | Daily ranked list with ARR and recommended action. |
| Contract anomaly | Not applicable. | Requires manual procurement query. | Continuous audit across all active MSAs. |
| Earnings intelligence | Not applicable. | Not applicable. | Investment-grade brief in under 5 minutes. |
| Cross-silo reasoning | Requires custom integration per pair. | All data must already be in Snowflake. | Native multi-source triangulation by design. |
| Output format | Dashboard / alert. | Chart / table. | Structured brief: findings, evidence, actions. |
| System | Category | What the Agent Queries | Status |
|---|---|---|---|
| Datadog | Observability | Metrics, APM traces, logs, monitors, deployment events | LIVE |
| Splunk | Log analytics | SPL search, alerts, event correlation, field extractions | LIVE |
| PagerDuty | Incident mgmt | Alert history, escalation policies, incident timelines | LIVE |
| Snowflake | Data warehouse | SQL query layer across any schema; write-protected read role | LIVE |
| Salesforce | CRM | Accounts, opportunities, activity history, health scores | LIVE |
| SAP S/4HANA | ERP | Contract master data, PO history, vendor rate cards | LIVE |
| Bloomberg | Market data | Earnings transcripts, analyst consensus, price feeds | LIVE |
| Databricks | Lakehouse | Delta Lake tables, MLflow model registry, notebooks | CUSTOM |
| ServiceNow | ITSM | Ticket history, CMDB, change management records | CUSTOM |
| Workday | HCM | Employee profiles, org structure, skill records | CUSTOM |
| Use Case | Annual Value Basis | Conservative Return |
|---|---|---|
| Incident RCA (10 P1s/month) | $5,600/min downtime (Gartner); 45 min MTTR reduction | $2.5M avoided downtime |
| Churn intelligence | $3.2M ARR at risk flagged; 30% save rate | $960K ARR retained |
| Contract audit ($500M spend) | 8% savings rate on addressable spend (McKinsey) | $4.0M annual savings |
| Earnings intelligence | Analyst team productivity; 2 FTE reallocated | $340K FTE equivalent |
| Combined (first 2 use cases) | $6.5M+ Year 1 |
ROI figures are illustrative, based on published research benchmarks from Gartner, McKinsey, and IDC. Actual returns depend on incident volume, ARR base, contract portfolio size, and implementation scope. A scoping call quantifies the specific return for your organization.
The right starting point is one anchor use case: the one with the highest dollar value and the clearest data access path. 15 minutes tells us which one that is.
Schedule a 15-minute scoping call →