Enterprise Decision Intelligence Platform for a Fortune 500 Beverage Company

A decision intelligence platform brings data, analytics, business context and governed workflows together to help people make better decisions. Unlike a dashboard that only describes what happened, it helps teams understand why performance changed, test possible actions and evaluate outcomes.

DreamzTech built an enterprise platform that combines commercial performance analytics with structured experimentation. It supports historical decomposition, interactive dashboards, hypothesis design, statistical analysis, recurring tests, executive reporting and a centralized repository of learnings.

Business intelligence usually focuses on reports, dashboards and historical trends. Decision intelligence organizes data and analysis around a specific decision, the factors that influence it, the action taken and the resulting outcome. This creates a repeatable path from insight to action.

An enterprise experimentation platform provides a governed way to design, schedule, monitor and evaluate tests across teams and markets. It standardizes hypotheses, test methods, metrics, statistical calculations, permissions, reporting and the reuse of previous learnings.

The documented solution supports A/B tests, randomized controlled trials and observational study designs. Teams can define blocking factors such as region or customer segment and use sample-size calculations to plan statistically credible tests.

The platform can attribute changes in volume, net revenue, market share and return on investment to drivers such as pricing, marketing, promotions and distribution. It can also incorporate non-commercial factors such as economic conditions and weather.

Its statistical engine calculates measures such as uplift, average treatment effect, minimum detectable effect, confidence levels and p-values. Teams can compare test and control groups, examine counterfactual results and drill into daily or user-level metrics where permitted.

Interactive decomposition and efficiency views connect commercial inputs, including marketing and promotion spend, with business outcomes. Decision-makers can compare contribution by channel, brand, region and period before adjusting investment.

Yes. Authorized users can filter results by geography, brand, business domain and time period; compare period-over-period momentum; and use rolling windows to separate recurring patterns from unusual events.

Role-based access, reusable templates, experiment tagging, prioritization, scheduled milestones and a centralized learning repository help teams apply consistent methods and retain institutional knowledge across business units.

Yes. Teams can configure weekly, monthly or milestone-triggered tests, reuse approved parameters and receive notifications when a test is due or completed. This reduces setup effort and improves consistency.

The supplied project records report a 70 percent reduction in experiment setup time, a 50 percent reduction in analysis time, a 60 percent reduction in report-generation time and a 40 percent reduction in manual reporting workflows. All figures should be validated and approved before public release.

The documented design includes role-based permissions and fine-grained data-access policies. Any public security or regulatory statement should reflect the deployed environment and be confirmed through the client’s legal, security and privacy review.

Yes. A custom platform can integrate with approved data sources, onboarding pipelines, planning tools and reporting processes. The integration design should define data ownership, refresh frequency, validation, lineage, access controls and failure handling.

Yes. DreamzTech can design a custom solution around an organization’s decisions, data environment, experimentation methods, KPIs, governance model, reporting requirements and security controls. A practical starting point is one high-value decision workflow with measurable outcomes.