Your Company Does Not Need More Dashboards. It Needs Better Decisions.

2026-07-13 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Many companies face a "decision problem" rather than a "dashboard problem," despite tracking numerous metrics across various departments. Existing data initiatives often fail because important decisions still rely on manual checks and inconsistent data, leading to multiple metric definitions and unused reports. The article advocates for modern data platforms to focus on enabling faster, better decisions with confidence, rather than just producing more charts. It proposes starting with the decision a data product should support, establishing clear ownership for every metric, building reusable data models for consistency and faster delivery, and using automation to reduce friction. Furthermore, it stresses making data quality visible through automated validation and freshness monitoring, and measuring adoption and business impact instead of just dashboard delivery. This approach aims to create a reliable link between operational data and business action.

Key takeaway

For Directors of AI/ML overseeing data strategy, recognize that simply increasing dashboards won't improve decision-making or AI reliability. You should prioritize designing data platforms around specific business decisions and establishing clear metric ownership. Focus on building reusable, governed data models and making data quality transparent. This approach ensures your AI systems are fed consistent, trusted data, leading to more confident, impactful business actions and avoiding the pitfalls of inconsistent information.

Key insights

Modern data platforms must prioritize confident business decisions by connecting raw data to action, not just producing more reports.

Principles

• Data initiatives must start with the decision they aim to support.
• Every important metric requires clear definition, source, calculation, and business owner.
• Build reusable, trusted data models for consistency and faster delivery.

Method

Design data products by first identifying the specific business decision they should enable, shifting focus from available data to desired outcomes.

In practice

• Design inventory dashboards to guide actions like transferring, discounting, or reordering stock.
• Implement automated validation tests and freshness monitoring for data quality visibility.
• Measure data product impact by tracking decisions supported and manual work eliminated.

Topics

• Data Strategy
• Decision Intelligence
• Data Governance
• Data Quality
• Analytics Engineering
• AI Data Reliability

Best for: Data Engineer, Analytics Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.