Operating Model for a Federated Analytics Backlog
A P&C insurer has 14 business units, nine legacy data warehouses, and 40 analysts scattered across finance, claims, and underwriting. Analytics requests wait nine months; analysts spend 60% of their time finding and cleaning data. The board approved an $18 million, three-year data strategy and hired a CDO. The cloud migration will take 18 months. Business-unit heads want their own analysts; the CDO wants a central team. Regulatory reporting is already late in two states, and the CIO says a central build will slow delivery. The CEO asks for an operating model that improves delivery without a reorg.
1. Recommend an operating model and decision rights. Explain how it addresses the backlog and regulatory risk.
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Use a hub-and-spoke model without a full reorg. The hub owns the platform, data engineering standards, data catalog, access controls, model risk, and regulatory reporting data products. Spokes are embedded analysts in each business unit who own domain use cases and priorities.
Decision rights: the hub owns data definitions, quality rules, platform architecture, and access. Business units own use-case prioritization within their domain, subject to a portfolio council that allocates scarce engineering capacity. Regulatory reporting is treated as a shared data product, not a one-off extract, which directly addresses the late filings.
The model reduces the backlog by building reusable data products and common definitions instead of 14 separate extracts. It also gives business units local speed without allowing nine warehouses to multiply into more.
2. How would you prioritize the first 12 months of the $18 million program? Give criteria and a candidate portfolio.
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Score candidates on value, regulatory or financial risk reduction, feasibility, and reuse. Use a 70-20-10 split: 70% core data products and regulatory reporting, 20% high-value analytics, 10% exploration.
Candidate portfolio: first, a regulatory reporting data product for the two late states; second, a data catalog and quality remediation for the highest-use claims and policy data; third, a claims triage data set that reuses the same claim data; fourth, broker or underwriting scorecards using curated policy data. Defer standalone dashboards and one-off models that do not reuse the core.
This sequence shows early regulatory wins, builds reusable assets, and prevents the program from becoming a tooling migration.
3. Define three KPIs and one anti-metric to govern success.
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KPIs: time from data request to access, percentage of requests delivered within agreed SLA, and reuse ratio, meaning the share of data products used by more than one business unit. Also track analyst time spent cleaning data and regulatory filing timeliness.
Anti-metric: number of dashboards or reports produced. It is easy to increase while value stalls. Pair it with adoption and decision-use measures so the program is judged on reusable data and faster decisions, not output volume.