We will continuously measure our progress against five data success factors.
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Data success factors |
Proof points |
Potential measures |
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Data-informed culture |
Increased awareness and adoption of data assets and services Growth in ‘What’s in it for me’ use cases Successful delivery of data and analytics projects Proactive data champions network |
Growth in the number of use cases identified, funded and implemented Data accessibility and usage Percentage of ASIC staff using data and analytics tools and methods Realisation of data and analytics use cases, and project benefits |
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Efficient and effective data governance |
Aligned business and data performance measures Data decision rights and responsibilities clearly defined Clearly documented data controls, rules and governance Compliance with data governance frameworks Participation in data decision-making forums |
Number of data policy or governance-related exceptions, variations or non-conformances Data service catalogue and service levels defined Percentage of data policies documented and actively enforced Alignment of data and business performance measures Effectiveness of data decision-making forums Chief Data and Analytics Office service level performance |
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Robust data and analytics capability |
Acquisition and retention of new data capabilities and skills Fit-for-purpose skills and capabilities as required to meet business needs Active engagement of staff in data training and development programs |
Uplift in high-priority data capability maturity levels Reduction in vacancy duration and employee turnover for key data positions Data and analytics training offerings and participation Percentage of team members with talent development plans |
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Trusted, secure and valued data sets |
Increased quality of data sources, including detailed metadata and documentation Growth in availability and usage of existing and new data sources Reduced manual effort absorbed in data preparation activities Compliance with ASIC security and regulatory obligations |
Business colleague satisfaction rating (episodic and transactional) Percentage of data sets proactively registered, classified, audited and quality-managed Uptake and utilisation of Chief Data and Analytics Office services Increased volume and reuse of common data sets Reduction in effort spent on data sourcing and preparation |
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Fit-for-purpose data and analytics tools |
Increased availability and utilisation of data and analytics tools Sturdy and flexible technology foundations to support new data use cases Efficient and effective utilisation of budget allocations |
Data and analytics projects delivered to scope, on time and at budget Performance tracking against data and analytics strategy and roadmap Increased reuse of common data assets and tools Total data and analytics funding against industry benchmarks |