AI outcomes are set upstream, before a single model runs. Each day turns on a handful of critical deliveries: the 8 a.m. sales close that feeds revenue forecasts, the customer-event stream driving engagement models, and the prior day’s SLA roll-up used in compliance reports.
When those datasets arrive on time, in the format systems expect, and without silent gaps, the rest of the business moves with confidence. When they slip or drift, confidence thins and everything waits.
This is a reliability problem. You need data that can be put on a calendar, accounted for in a budget, and defended when questioned.
Teams need one simple definition of “good”: data that shows up when promised and behaves consistently under pressure.
In this edition, I introduce a single reliability signal that makes performance visible across product, finance, and operations, so when inputs hold, AI holds.
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One number for data reliability
Use a single, plain metric to signal whether today’s AI-driven outputs and executive reports can run on time.
Call it Data Reliability Yield (DRY). It is the percentage of scheduled deliveries for a named data product (table, file drop, or stream window) that arrived by the promised timestamp and passed a minimum quality gate in the period.
Think of it like manufacturing yield: usable over total.
Quality is explicit: required keys present, schema unchanged or versioned, rule checks green (e.g., no negative prices, event counts within expected bands).
DRY turns “is the data good enough to use today?” into one number everyone understands.
Set a minimum DRY for each dataset based on business tolerance.
For a daily Finance forecast, set 95%—in a 20-day month that allows one miss. For truly revenue-critical cuts, use 99% (near zero misses). For supporting feeds, 90% is acceptable (about two misses a month).
If a dataset’s DRY is at or above its minimum, work proceeds as planned.
If it falls below, treat it as a stop: Finance holds the forecast until the next clean run, Product keeps the feature flag off, and model jobs serve the last trusted snapshot or skip the run until data passes checks.
Do this next
- Put each product’s DRY on the same Power BI/ops dashboard that teams open every morning.
- Assign a data product owner who owns the SLOs, incidents, and the DRY number.
- Publish the two promises in plain language: delivery time and quality checks, in your catalog and dashboard, and enforce the floor as policy.
How reliability moves revenue
A top U.S. consumer bank rebuilt its marketing data pipeline around clear SLAs: freshness, completeness, and accuracy with end-to-end observability.
The result was SLA breaches down 96%, millions recovered in marketing revenue, and $10M in potential fines avoided thanks to better lineage and quality controls.
Reliability stopped being an abstract goal and became a measurable driver of cash flow and risk reduction.
The logic travels well to mid-market teams. When a dataset feeds revenue decisions, treat it like production capacity. In manufacturing, CIOs run sensor feeds with tight targets like ≤1–2 minutes lag and 99.9% completeness, because missing a temperature read can halt a line.
Data downtime is treated like an assembly-line breakdown. Your revenue datasets deserve the same standard.
Do this next
- Tie DRY to money. Label each data product with the business it powers (e.g., “Campaign send list—monthly bookings impact”). If DRY dips below the floor, the related campaign/feature pauses automatically.
- Adopt the bank pattern. Instrument the pipeline, set SLAs for time + quality, and publish DRY where marketing/product leaders look first. Expect breach counts to fall fast if someone owns the number.
- Escalate like ops. Treat a missed delivery for a revenue dataset as a P1: roll back to the last trusted snapshot, page the owner, and log a post-incident summary in the catalog.
Make this real with Microsoft
Use Microsoft Fabric to give each business domain (Sales, Finance, Support) have accountable owners and delegated controls. Register the data products there and publish two promises for each: when it lands and what checks it must pass.
Use Microsoft Purview so policy travels with the data. Apply sensitivity labels, turn on DLP, and keep an audit of who accessed what and when. Those controls follow the data into Power BI and any agent that consumes it.
Put DRY and the SLOs on a shared Power BI/ops view that leaders open every morning. If a product is below its floor at 08:00, dependent jobs don’t run, and reports/features wait. Serve the last trusted snapshot until a clean run lands, and page the owner.
IFS consolidated on Fabric and standardized ownership and reliability across domains. Data access jumped from ~20% to >85%, costs fell, and insight cycles sped up, visibility plus ownership changed how fast the business could move.
Do this next
- Stand up two Fabric domains for your highest-stakes lines of business and assign domain admins.
- Turn on Purview labels, DLP, and audit for Fabric items; make the Purview hub visible to data owners.
- Add a DRY check to your morning run: below the floor, auto-pause dependents and notify the owner; above it, ship.
Data reliability is an operating commitment. Give each critical dataset a planning rate, how many hours or dollars of downstream work you’re willing to schedule per point of reliability above its floor.
A sales cut at 98% reliability earns more capacity than one at 92%; planning reflects that difference. Over a quarter, this converts reliability into faster cycle time and cleaner forecasts because teams commit only what the inputs can support.
For teams standardizing on Microsoft Fabric, let us walk you through a practical blueprint.
