AI-Driven Data Governance & Predictive Intelligence for Marketing and Business Insights

We operate within a complex marketing/ data environment where multiple internal data sources, taxonomies, and reporting needs exist across initiatives. This creates fragmented, slow-to-unify datasets spanning user interactions, campaign performance, and sales; leading to long turnaround times and inconsistent insights that delay decisions. We require an AI-powered framework to: • Connect & pull data from authorized internal platforms; real-time where possible or via scheduled syncs (APIs, secure data shares, or approved insight platforms) • Unify, clean, deduplicate, and enrich CSV/ Excel and platform data to build standardized datasets • Auto-cluster, segment, or detect patterns to simplify filtering, targeting, and audience analysis • Run predictive analysis & trend detection that combines historical internal data with accessible and licensed external market/ public sources where appropriate • Generate recommendations (e.g., optimal campaign timing, content themes, audience clusters) • Ensure data governance and Indonesia data-residency compliance • Note: Full real-time processing may not always be feasible; the key objective is to automate and shorten data processing cycles while maintaining data integrity and compliance

Accelerate the processing, unification, and analysis of internal marketing and commercial datasets to reduce turnaround time by up to 75% and deliver more accurate, actionable insights that guide planning and business decisions

We use several approved tools for aggregation and visualization, but workflows mainly remain manual, siloed, and validation heavy. The goal is to integrate or replace these with an AI-driven, semi-automated workflow

We use several approved tools for aggregation and visualization, but workflows mainly remain manual, siloed, and validation heavy. The goal is to integrate or replace these with an AI-driven, semi-automated workflow

• Internal historical datasets for marketing interactions and sales • Structured files (CSV/ Excel) and platform exports • Sufficient data volume for model training and validation

• All data and compute must remain within Indonesia’s data-compliance boundaries • Integration expected with existing databases and business-intelligence tools (e.g., Looker Studio, Power BI, or internal dashboards) • Deployment model: preferably hybrid or on-premises (to align with compliance requirements)