The Signal

Enterprises are sitting on more data than ever, yet boardroom decisions remain frustratingly disconnected from it. According to McKinsey’s research on analytics in banking, only 7% of surveyed organizations had achieved full integration of key analytics use cases — and just 15% of leaders reported making decisions with heavy reliance on analytics. The gap is not a data volume problem. It is a data product problem.

Why This Time Is Different

The industry has responded with a structural answer: the rise of formally owned, governed data products. Gartner’s CDAO Agenda Survey 2024 found that 50% of organizations have already deployed data products, with another 29% actively planning to do so. And per Gartner’s Hype Cycle for Data Management 2024, data products have reached the peak of inflated expectations — which means organizations that build disciplined operating models now will be the ones reaping plateau-of-productivity gains in 2–3 years.

The Real Problem Being Solved

The root failure is the absence of a governing contract between the pipeline and the consumer. Data engineers optimize for flow reliability; business leaders optimize for dashboard availability; and no one owns the meaning of what sits between them — what the metric measures, who approved it, and whether it actually answers the question it claims to answer. Gartner’s 2024 D&A Trends report identifies this accountability gap as a primary reason organizations fail to realize expected value from their data and AI investments, even after significant spend.

The Leader’s Playbook

The organizations closing this gap fastest share one structural decision: they separate data platform ownership from data product ownership. Platform teams own the pipe; product owners own the meaning, the consumer experience, and the business accountability of what flows through it. Alation’s operating model guide, grounded in Gartner research, identifies clear human ownership — not team or system ownership — as the non-negotiable foundation of any scalable data product. The practical starting point: identify your top 20 board-reported KPIs, assign a named owner to each, and make that person accountable for both accuracy and consumption.

Signal vs. Noise

Ignore the debate about which data catalog to standardize on. Tool selection matters far less than the ownership model decision above it. Organizations that solve accountability first and select tooling second consistently outperform those that do it in reverse — and avoid the expensive rework that follows a governance program built on software with no human at the center.