Data Strategy
A robust data strategy is the foundation of any successful AI implementation.
Key focus
Ensuring high-quality, relevant data to fuel AI models and drive insights.
It’s about ensuring that your organisation has the right data, in the right format, at the right time, to fuel your AI models and drive valuable insights.
This involves creating a comprehensive inventory of all your data assets, including their quality, relevance and accessibility.
Clear data governance policies and procedures, aligned with the broader AI Governance and Security framework, are essential to ensure data quality, privacy and security throughout the AI lifecycle.
This includes ethical data collection practices, ensuring alignment with your organisation’s Ethics and Compliance work, and robust mechanisms for cleaning, transforming and securing data.
Checklist
Five things to get right.
- 01Develop a comprehensive data inventory, documenting all data assets
- 02Establish clear data governance policies and procedures for data management
- 03Implement efficient and ethical data collection processes
- 04Develop robust mechanisms for cleaning, transforming and enriching data
- 05Ensure secure and controlled access to data for authorised personnel
Maturity
What elevates it, and what diminishes it.
Elevates your maturity
- Data-centric culture
- High data literacy
- Centralised data platform
- Proactive data quality
- Robust data security
Diminishes your maturity
- Data silos
- Ethical lapses
- Lack of ownership
- Poor data quality
- Insufficient security
How mature is your Data Strategy?
We help you assess where your organisation stands in Data Strategy and the other 13 areas, and what to strengthen next for sustainable, measurable results.