SAPinsider Las Vegas: Why Data Strategy Must Start With Trust:

Why It Matters

Without trusted data, AI amplifies errors, jeopardizing decision‑making and eroding executive support for digital transformation. Establishing data trust directly influences ROI on analytics investments and ERP modernization efforts.

Key Takeaways

• Data trust precedes AI success.
• Align data strategy with business outcomes.
• Executive sponsorship critical for analytics adoption.
• Use one‑slide canvas to prioritize quick wins.
• Continuous governance builds data confidence.

Pulse Analysis

Data trust has become the linchpin of successful AI and analytics initiatives, especially as enterprises grapple with fragmented definitions across ERP, CRM, and cloud platforms. When profit, customer, or inventory metrics carry multiple meanings, machine‑learning models generate misleading insights, prompting business users to double‑check dashboards rather than rely on them. This erosion of confidence not only slows adoption but also inflates costs as organizations duplicate effort to validate data, underscoring the need for a disciplined governance framework that standardizes semantics before any advanced analytics are deployed.

Hilgefort’s seven‑element blueprint places executive sponsorship and clear business outcomes at the forefront, shifting the conversation from technology hype to measurable value. By defining a vision tied to revenue growth or cost reduction, leaders can secure funding and align cross‑functional teams, while governance policies assign ownership of data quality and policy enforcement. Integrating these elements with a current‑state assessment and capability roadmap ensures that data movement, performance expectations, and use‑case prioritization are grounded in real‑world ROI, turning data into a strategic asset rather than a compliance checkbox.

The practical one‑slide canvas Hilgefort championed forces organizations to condense their strategy into vision, assessment, capabilities, prioritized use cases, and an execution timeline. This concise format accelerates decision‑making, enabling 30‑60‑90‑day pilots that deliver quick wins and demonstrate tangible benefits, which is crucial for maintaining budgetary support. For ERP‑centric firms, embedding data governance into S/4HANA migrations or cloud ERP projects safeguards master data integrity, ensuring that downstream analytics and AI layers operate on reliable inputs. Continuous quarterly reviews further refine the approach, keeping the data strategy agile and aligned with evolving business goals.