Did The Bible Society study on the “Quiet Revival” among young men reach the wrong conclusion? It could mask a significant “Quiet Withdrawal” of Gen Z women, who are leaving churches due to perceived inequality and systemic bias. Cherry-picking data can be an extension of cherry-picking Bible verses, and I suggest ways to move forward from a data perspective.
Focusing on Commvault, the recent developments in AI data security at the RSA Conference emphasise the importance of integrating Data Security Posture Management (DSPM) into AI systems. The “last mile” of governance, where sensitive data may be mishandled, is highlighted as a gap that organisations must address to ensure secure AI operations.
In 2024, the Bible Society falsely claimed a “Quiet Revival” in UK church attendance based on flawed data from YouGov. After a retraction, the Society acknowledged that their beliefs influenced acceptance of the inaccurate results. The incident underscores the importance of data diligence, internal validation, and skepticism toward overly favorable outcomes to prevent confirmation bias.
This week’s discussion focuses on the changing role of dashboards in the context of AI and decision velocity. While traditional dashboards often promote passive data observation, organizations must shift towards action-oriented decision-making. High decision velocity is essential for effective strategy, requiring real-time data and confidence in data utility for decision velocity leading to actions.
The Commvault SHIFT London event highlighted that data resiliency is essential for effective AI and BI strategies. We need to consider what we do with the data that AI produces, not just the data sources. Without a strong data foundation, businesses risk poor decisions and operational failures. The event advocated for integrating security and data management, deploying tools like Commvault Cloud Unity, and continuously testing recovery plans to ensure organisational resilience.
Welcome to this week’s Saturday Strategy. Grab a coffee, buckle in, and let’s talk about the most powerful, persistent, and occasionally perilous force in the
Power BI’s greatest value might not be the insights it delivers. Rather than masking the issues like an Instagram filter, Power BI may be your critical friend because of the problems it exposes. The wise thing to do is to use that signal to navigate your next step, rather than try to mask it as if it is an Insta filter.
Microsoft Power BI is facing increased phishing attacks that exploit its legitimate report subscription feature, allowing scammers to send malicious emails from trusted domains. Organisations need to recognise that Business Intelligence tools have functionality that could be used for good or bad. Governance means understanding the features, and using controls that recognise the fine line between business intelligence and cybersecurity.
Microsoft’s latest earnings call indicates a strategic shift towards prioritizing high-margin AI software over infrastructure. With 15 million Copilot users, the focus is on transforming enterprise value through active user engagement rather than mere license purchases. The emphasis on intelligence signals the future direction for organizations investing in AI.
The rise of artificial intelligence brings excitement, yet many organizations fall into the “AI Pilot Trap,” where initiatives fail to yield real value. Statistics show 95% of AI tools never reach production and 72% destroy value. To escape this trap, organizations should focus on governance, back-office ROI, and strategic partnerships.
Datasphere technologies are transforming data management for AI by transitioning from siloed structures to interconnected ecosystems that deliver real-time insights, ensure data quality, and democratize product development. These advancements enable organizations to efficiently leverage data, improve AI deployment speed, and enhance innovation, positioning them for competitive advantage in the evolving landscape.
In 2025, organizations are shifting their focus from sophisticated AI tools to enhancing data quality, recognizing that poor data costs them significantly. By investing in foundational data quality, companies can reduce operational costs, accelerate innovation, and mitigate risks. This strategic transition is vital for achieving reliable ROI in AI initiatives.