Episode Details

When two government agencies define "household" differently, one by mailing address and one by shared cooking facilities, no integration platform reconciles the numbers. This episode argues that roughly 70 percent of cross-agency integration failures trace to the semantic layer rather than the plumbing, and that the median time to reach a trusted cross-agency data agreement is 18 months.

You get a worked example from benefits coordination, where food assistance uses an economic household and Medicaid often uses the tax household, plus the trap of mapping household_id to household_id. You leave with a sequencing rule for governance spend, a named-owner test, and a three-entity definition exercise. References include MIT Sloan Management Review and dbt Labs.

Key takeaways

• Spend the first dollar on written definitions, not an integration platform, because mapping fields that mean different things creates agreement without substance.
• Treat any vendor promise of interoperability in a quarter as selling the connection, not the consensus; plan against an 18-month median for a real cross-agency agreement.
• Ship one agreed entity, such as household, case or resident, with both agencies signing the exact wording including edge cases, then publish it.
• Name a single person who can say no on each definition instead of a working group, since shared ownership means the definition drifts.
• Ask each partner agency to define your three most-shared entities independently in one sentence, and treat every disagreement as the actual project.

Chapters
0:00 Two agencies, two definitions of household
0:58 Why 70% of integration failures are semantic
2:03 Food assistance and Medicaid household mismatch
3:02 The 18-month median for data agreements
3:40 Shipping a signed definition, not a dashboard
4:18 Monday action: write definitions independently

Go deeper, free lessons

• Interoperability and shared standards across agencies
• Mapping the public data landscape: registries, admin records, and survey data
• Governing privacy and equity on legacy systems
• Writing consent and data-sharing agreements that survive an audit
• Building open data that citizens and journalists actually use

Full article and transcript: https://www.mba-training.com/blog/public-sector-interoperability-shared-data-infrastructure

MBA Training, mba-training.com