Leadership brief · one page

Building Trustworthy Data Products

Most organizations finish a data migration and then meet the harder problem: the people who understand the data are not the people who can build with it. This training teaches the first group to do the work themselves, with an assistant for the syntax and their own judgment for the rest.

Self-paced · 6 modules, 22 lessons · about 7 hours

Chapter 5 in the Meridian sequence · 9 live so far — the sequence follows one fictional utility through the same modernization, so the examples build on each other, and this one picks up the story from Cloud Modernization Patterns and Modern DevSecOps Foundations. Each training stands on its own; the order is the recommended path, not a prerequisite. The story continues in Grounded Answers From Documents.

What this training covers

Six modules in the order the work actually happens: deciding what answer is needed and what it means before anyone writes a query; profiling what the platform team handed you, including what the catalog never recorded; building a table that returns the same result every time it runs; proving it reconciles before anyone sees a number; serving it to the consumer who asked, in the form that consumer can use; and owning it once people depend on it. Every module teaches the work and the AI-assisted way to do it as one workflow rather than two, and every module ends by finding what the agent got wrong.

Why it matters

A governed data estate that produces no answers is a cost center with good posture. The bottleneck is rarely the platform. It is that the people who know what the data means are analysts, planners, and domain experts rather than engineers — and the people who can build are not always the ones who can tell a right answer from a plausible one. AI-assisted work closes the first half of that gap immediately. The second half is judgment, and judgment is what this training is about: a number that looks reasonable and is wrong costs more than no number at all, because somebody acts on it.

What changes in practice

Tags name what each shift affects most: calendar time, cost, contract risk, or an audit finding avoided.

1. 1

   The answer is defined and owned before the first query runs

   Calendar time

   Why it matters: rework is the largest hidden cost in analytics, and a written acceptance test is what stops a three-week build from producing a number nobody will accept · Module 1

2. 2

   One metric definition, one named owner

   Cost

   Why it matters: three teams stop maintaining three versions of the same number, and the disagreement gets settled once instead of in every meeting that uses it · Module 1

3. 3

   Classification decides which joins are allowed, before the join runs

   Audit finding avoided

   Why it matters: combining two permitted sources can produce a more sensitive result than either one, and deciding that up front is cheaper than an assessor finding it · Module 2

4. 4

   Every data product reruns and returns the same answer

   Cost

   Why it matters: a failed load gets retried instead of reconstructed by hand, and a backfill stops being an event that requires the person who built it · Module 3

5. 5

   Nothing publishes until it reconciles against an authoritative source

   Contract risk

   Why it matters: a wrong number that reached a customer, a regulator, or a board deck is the expensive kind, and reconciliation is the cheapest place in the pipeline to catch it · Module 4

6. 6

   Delivery lands where the consumer already looks, under a stated contract

   Cost

   Why it matters: the dashboard nobody opens stops being the default, and a change to a depended-on number gets announced with notice and a parallel period instead of discovered when a report breaks · Module 5

7. 7

   Every published data product has an owner, a service level, and a retirement path

   Audit finding avoided

   Why it matters: orphaned data products are what an auditor finds and what nobody can safely switch off, so they accumulate until someone is paid to inventory them · Module 6

Where the effort goes

Most of it lands in the two places that feel like overhead and are not: defining the metric before building it, and proving the result before publishing it. The build itself is the short part once an agent is doing the typing. What this does not require is a data engineering team standing up beside your analysts. It requires that the person doing the work knows what to check, and has an acceptance test written down before the first query runs.

How you'll know it worked

• A named consumer and a written acceptance test exist before any data product is built — no exceptions
• Failed loads are retried rather than rebuilt by hand, because the same inputs produce the same answer
• Zero published data products without an owner, a service level, and a retirement path