Election models built to 80% accuracy.
Cloud costs down 30%.

Electoral data spanned jurisdictions, but nobody could vouch for it.

The client ran electoral analytics across multiple jurisdictions for government and corporate clients, but the data feeding those projects had no consistent governance behind it. Without a framework to guarantee accuracy and reliability, every downstream model and campaign insight inherited that uncertainty.

On top of that, there was no predictive capability turning the data into decisions. Government clients needed forecasts they could act on, and campaigns needed analytics delivered on the timelines that electoral strategy and policy planning actually run on — not after the fact.

Three constraints were non-negotiable:

• No consistent data governance across multi-jurisdictional projects
• No predictive model translating data into electoral forecasts
• Analytics too slow to inform live campaign and policy decisions

Fix the data. Then build the models on top of it.

The first move was governance, not modelling. Data governance strategies were implemented to enforce integrity across every multi-jurisdictional project, so the numbers feeding into any downstream forecast could actually be trusted.

With that foundation in place, predictive models were designed and deployed to forecast election outcomes at 80% accuracy, generating real-time campaign insights rather than retrospective reports.

Cloud infrastructure was rebuilt on Azure and PostgreSQL to improve data processing efficiency, which is what made the cost reduction possible without giving up performance.

Trusted data. Accurate forecasts. Lower bills.

Governance came first, and the payoff followed: 98% data accuracy across every jurisdiction, election models forecasting outcomes at 80% accuracy, and a 30% cut in cloud costs — proof that getting the data right is what makes everything built on top of it worth trusting.