AI Data Quality & Cleanliness in Indonesia

Our programme provides a unified governance framework covering UU PDP, GR 71/2019, KOMDIGI's Circular Letter No. 9/2023, and sector-specific regulations like OJK's banking AI governance. We help organisations build a single governance structure that satisfies multiple regulatory requirements.

Our training includes a PDP Agency readiness assessment and preparation roadmap. Key actions include establishing data protection impact assessments, building incident response procedures, and ensuring your AI governance framework is mature before enforcement begins.

We recommend a layered approach: UU PDP compliance as the foundation, KOMDIGI's ethical AI principles as the guiding framework, sector-specific requirements (OJK, health, etc.) as overlays, and international best practices (ISO 42001) as aspirational targets. All adapted for Indonesian business culture.

AI is only as good as the data it works with. Poor data quality leads to unreliable outputs, flawed decisions, and wasted investment. Our programme teaches your team practical techniques to audit your current data, identify quality gaps, and build cleanup processes that bring your data to AI-ready standards so your AI initiatives deliver trustworthy results.

The timeline depends on your starting point, but our programme gives you a structured approach that delivers quick wins within weeks and sustainable improvement over months. You will learn to prioritise the data that matters most for your AI goals, establish ongoing quality practices, and create a realistic roadmap so data quality becomes a continuous strength rather than a one-time project.

That is exactly the situation this programme is designed to address. Most organisations have data spread across CRMs, spreadsheets, email, and legacy systems with inconsistencies and gaps throughout. We teach practical techniques to audit, consolidate, and clean your data in a structured way that makes it useful for AI applications. You do not need perfect data to start. You need a clear process for improving data quality over time, and that is what this programme delivers.

Your team receives a data quality audit template that can be applied to any system or dataset in your organisation, a standardisation rulebook for cleaning common data issues like duplicate records, inconsistent formatting, and missing fields, a data governance policy draft outlining ownership and maintenance responsibilities, and a prioritisation matrix for deciding which datasets to clean first based on their AI readiness impact. We also provide step-by-step guides for using free and low-cost data cleansing tools that do not require programming skills. These deliverables are designed so your team can run independent data quality sprints on a quarterly basis, continuously improving your data foundation without needing to bring in external consultants every time a new AI initiative requires clean input data.