There's a question that comes up early in almost every TBM or FinOps conversation.
Not about methodology. Not about frameworks. About data.
"Where does it actually come from?"
And then, almost immediately after: "can we trust it?"
This is where most cost transparency efforts slow down before they've started. The data that tells the full story of IT spend and consumption isn't in one system. It's spread across cloud billing exports, ERP ledgers, HR platforms, SaaS tools, asset databases, ITSM tools, and a handful of others. Each has its own format, its own cadence, its own method of access.
But getting the data flowing is only half the problem. The other half is what arrives.
Enterprise source systems were not designed with TBM or FinOps in mind. They were designed to run payroll, process invoices, manage incidents, and track licenses. What that means in practice is that cost center codes don't always match between HR and finance. Cloud tags are inconsistent, missing, or wrong. SaaS user counts include contractors who left six months ago. Asset records lag behind what's actually deployed.
When you pull that data into an analysis, you don't just inherit the numbers. You inherit all of this.
The standard response is to fix the source. And eventually, that's right. But in the meantime, cost analysis still needs to happen. Decisions still need to be made. Waiting for perfect data is rarely an option.
Yarken is built to handle data as it actually arrives — with the gaps, the inconsistencies, and the mismatches that are inevitable when pulling across a real enterprise landscape. That means applying normalization rules, flagging quality issues where they exist, and making the state of the data visible rather than burying it inside a number that looks cleaner than it is.
The integration surface for TBM and FinOps is wide because the data sources are wide. Yarken is built to ingest from the categories that actually hold IT cost and consumption data:
Yarken supports multiple ingestion pathways to match different enterprise environments: API-based connections for real-time sync, file-based ingestion via SFTP for legacy systems, cloud bucket ingestion for large-scale exports, local agents for secure on-premises data extraction, and query federation for direct warehouse access.
The goal is to meet systems where they are — not to require change to how data is currently stored or exported.
The path from a raw data source to trusted, analysis-ready data follows a consistent set of steps inside Yarken — regardless of which source type you're connecting.
You start by selecting a source type from the integration library — cloud, ERP, HR, SaaS, ITSM, or any of the other categories Yarken supports. From there, you create a pipeline: choose your ingestion method (API, SFTP, cloud bucket, local agent), configure the connection credentials, and set a schedule.
Once the pipeline is active, ingestion runs automatically. Yarken handles incremental and full loads, tracks run history, and surfaces any failures — so you always know whether the data in the platform is current.
The final step is where raw data becomes trusted data. Upload rules run at ingestion time, before data enters the Yarken data model. They handle three jobs:
These rules are configured through the interface. No code required, and no dependency on engineering to make changes when business structures shift.
Getting data in is the foundation. But integration also runs the other way.
When analysis surfaces a finding — a cost anomaly, a budget threshold crossed, an optimization opportunity — that finding needs to reach the person who can act on it, in the context where they work. Yarken supports outbound integrations that connect insights to action: creating tickets in tools like Jira or ServiceNow, sending alerts to Slack or Teams, and routing notifications based on the org structure already held in the platform.
The insight and the action don't have to live in separate places.
TBM asks: what does technology cost, and what value does it deliver? FinOps asks: where is cloud spend going, and is it optimized?
Neither question can be answered well from a single data source. The full picture requires financial actuals alongside consumption records. It requires HR data to allocate to the right cost centers. It requires application and service context to connect infrastructure to the products it supports.
When those sources are disconnected — or when the data quality issues inside them go unaddressed — analysis is partial. Decisions are made on incomplete information. And reconciliation becomes the work, instead of the insight.
Integration isn't a feature.
It's the starting point for everything that follows.
Next up: How Yarken structures data so that insights lead naturally to action.