INTELLIGENT TECHNOLOGY: DATA GOVERNANCE
Six steps to efficient, compliant and valuegenerating data governance
Cesar Ripari, Senior Director of Pre-Sales at Qlik for Latin America, outlines six essential steps for building efficient, compliant and valuegenerating governance frameworks
4. Define responsibilities: Assign owners and clarify roles so teams understand obligations.
5. Operationalise governance: Embed controls in daily workflows, systems and interactions for continuous enforcement.
6. Observe and act: Review, monitor and adjust processes to maintain integrity.
In today’ s information-driven economy, data is one of the most valuable assets an organisation can cultivate. Yet volume alone does not generate advantage. What truly differentiates leading companies is their ability to use data quickly and reliably. Speed enables real-time answers, supports rapid decisionmaking and elevates customer experiences. Trust ensures accuracy, security and consistency. The challenge is that these two forces often collide.
Organisations that prioritise speed above all else adopt integrations and fragmented solutions that offer shortterm gains but create inconsistency, risk and limited scalability. Those that focus solely on trust implement restrictive rules that slow innovation and reduce autonomy. Striking the right balance separates reactive management from modern, proactive, businessaligned strategy.
Data governance unifies processes, standards, policies and responsibilities to ensure quality, security, accessibility and traceability. When effective, it delivers the right data to the right people at the right moment with context that enables meaningful decisions.
Though governance may seem complex, six foundational steps enable scalable implementation:
1. Identify data and movement: Profile assets to understand structure and sensitivity.
2. Standardise information: Define and apply common formats and definitions to avoid misunderstandings.
3. Consolidate: Remove redundancies to build a single trusted version of truth.
These practices turn governance into a catalyst for innovation that evolves with new technologies, models and regulations. Success requires shared accountability supported by data literacy that helps teams interpret information within business context.
To achieve these outcomes, organisations must cultivate a culture that values transparency, stewardship and continuous improvement. Governance works best when woven into strategic planning, not treated as an afterthought or compliance requirement. Business leaders, data specialists and teams should collaborate to define priorities, assess risks and measure outcomes, ensuring governance delivers tangible value rather than bureaucratic overhead.
Equally important is empowering employees with self-service tools that allow them to explore, trust and use data confidently without compromising security or consistency.
As regulatory expectations evolve and competitive pressures intensify, establishing strong governance becomes a prerequisite for responsibly scaling advanced analytics and AI. Clean, well-governed data reduces model bias, improves explainability and accelerates development cycles. It also strengthens customer trust by demonstrating responsible data management practices.
By viewing governance not as a constraint but as a strategic enabler, companies position themselves to innovate with confidence, adapt and unlock new sources of long-term value across the enterprise. p
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