Consistency supports scalability and promotes both business objectives and stakeholder confidence. Ensuring data consistency is essential for rapid expansion of organisations ... Introduce a unified governance framework: Use a centralized governance model to maintain consistent standards across systems, reducing the risk of mismatched data policies. Standardize integration processes: Implement clear protocols for onboarding new data sources and systems to ensure seamless integration without creating data silos. Continuous validation and monitoring of data flow: Regular audits and automated monitoring can quickly uncover discrepancies, ensuring reliable and consistent data across all platforms.

I believe it's super important to set up effective processes for data management and governance as soon as you can. You can create some basic policies and practices quite quickly, and they'll really pay off in the long run! Once you have those in place, you can gradually improve your data management framework bit by bit. Think of it like managing a technical backlog: as your product evolves rapidly, getting things to market quickly becomes essential. However, if you focus solely on speed, you might sacrifice the quality of development and architecture. Eventually, you could end up with a massive backlog and a lot of technical debt, making it take even longer to fix things later on. It’s all about finding that balance!

Enabling 'DG By Design' if not already in place . Catching up will be a challenging act with expansion. legacy vs inflight vs future categorization of data sourced/stored/consumed across would be a good starting point along with literacy and accountability.

Key Considerations in order to cater to rapid expansion - 1. Data Management: Centralize and standardize data collection, storage, and retrieval. 2. Integration: Seamlessly connect disparate systems, applications, and data sources. 3. Data Governance: Establish policies, procedures, and controls for data management and security. 4. Data Quality: Ensure accuracy, completeness, and consistency of data. 5. Data Harmonization: Ensure consistent data definitions, formats, and taxonomy across all systems, departments, and geographies.