Making Big Data Work: Reflections on Ivan Teh’s Vision
In a world where data is generated by nearly every action, decision, and interaction, the challenge for organizations is no longer simply collecting information—it is making sense of it. Dato’ Seri Ivan Teh, in his presentation “Making the Most of Big Data,” offers a powerful roadmap for how institutions can turn data into real competitive value.
From Data Collection to Insight
Ivan Teh argues that collecting vast volumes of data is just the first step. https://fskm.uitm.edu.my/scds2016/abstractivanteh.html The real value emerges when that data is cleansed, integrated, and transformed into actionable insights. Organizations today gather data from many sources—customer transactions, online engagement, social media, sensors, legacy databases—but without thoughtful processing, those raw data streams remain noise.
The goal, he suggests, is to deliver intelligence that supports both long‑term strategic planning and daily operational choices. In practice, this means building systems that can provide real‑time or near real‑time responses. Rather than waiting for weekly or monthly reports, decision makers should see evolving trends, anomalies, or opportunities as they emerge.
Tailored Strategies, Not Generic Models
One of the central lessons in Teh’s talk is that context matters. A Big Data strategy that works for a retail enterprise may not suit a healthcare provider or a transportation company. Each sector creates different kinds of data, has different regulatory constraints, and pursues different performance metrics.
Hence, organizations must:
Select the right data sources (not all data is equally useful)
Choose analytic approaches that match their domain (predictive models, anomaly detection, clustering, etc.)
Design how insights are delivered (dashboards, alerts, automated triggers)
Embed the analytics within workflow so it leads to action
In other words, success depends less on having “more data” and more on choosing and applying data wisely.
Advanced Tools Amplify Impact
Teh also discusses how tools like Artificial Intelligence, Deep Learning, Machine Learning, and Internet of Things (IoT) can raise the ceiling of what Big Data can do. These technologies push analytics from descriptive (what happened) to predictive (what may happen) and prescriptive (what we should do). They enable automated pattern detection, anomaly alerts, recommendation engines, and even autonomous decision logic.
But these tools require the right infrastructure, data pipelines, and skilled talent to operate—and that is often where organizations struggle.
Organizational Foundations & Culture
Technology alone does not guarantee success. Teh emphasizes that leadership, culture, and capabilities are equally critical. A few key elements:
Leadership buy‑in is essential to secure resources, dismantle silos, and prioritize data initiatives
Data governance and quality must ensure that analysis is trustworthy
Skilled people—data scientists, engineers, analysts—must be developed or recruited
A culture of experimentation allows learning from failure and iterating
Without these foundations, analytics risk being superficial or ignored.
Challenges & Ethical Concerns
Teh is realistic: there are hurdles. Issues like data privacy, security, integration complexity, bias in models, and organizational resistance are real. As analytics become more embedded, ethical questions arise: How should data be used responsibly? Who is accountable when decisions are automated? Governance and transparency are non‑negotiable.
Looking Ahead
In summary, Dato’ Seri Ivan Teh’s message is clear: Big Data is not about volume but about insight; not about flashy tools but about embedding analytics into every layer of an organization. For those ready to invest in strategy, capabilities, technology, and trust, the rewards are compelling—agility, precision, innovation, and competitive edge. In a rapidly changing world, the ability to “make the most of Big Data” will increasingly separate leaders from laggards.
