Big vs Small Data

What Do We Mean by Big Data?

Yes, literally it’s large volumes of information collected from multiple channels. But Big Data is more than scale – it’s a mindset and methodology.

• Data-centric approach.
• Powered by specialist platforms, software, and expertise.
• Driven by the belief that once data pipelines are built and integrated, hidden insights and correlations will emerge.

And Small Data?

Small Data isn’t just “less of it.” It’s a customer-centric approach.

• Focused on purposively identified groups of individuals.
• Extracts detail that can be extrapolated into broader market trends.
• Puts the human at the centre, not just the numbers.

Where They Overlap

Both Big and Small Data share common ground in:

• Analysing attitudinal and behavioural trends.
• Interpreting what those trends mean for the business.
• Implementing insights via visualisation, storytelling, and commercial activation.

The foundations of good analysis – distillation, interpretation, communication – predate Big Data, and still apply to both.

Where They Differ

Big Data is defined by the four Vs: volume, variety, velocity, veracity.

• Scale brings complexity.
• Large datasets reveal correlations that smaller studies can’t.
• Modern tools make it possible to identify and test these relationships in real time.

Small Data, by contrast, thrives on depth over breadth – digging into human experiences to explain the “why” behind behaviour.

Pitfalls to Watch Out For

Big Data isn’t always easy:

• Scale can be daunting – and sometimes paralysing.
• Processing is time-intensive and resource-heavy.
• Benefits may take time to materialise, creating pressure when investment is high.

For both Big and Small Data, one rule holds true: data without action is meaningless. Simply collecting it achieves nothing; its value lies in what organisations do with it.

Not a Binary Choice

This isn’t David vs. Goliath. The strongest insight strategies combine Big and Small Data.

• Big Data offers breadth, scale, and predictive power.
• Small Data brings human context, nuance, and emotional drivers.
• Together, they provide a complete, 360º understanding of customers.

Modern technologies – AI, machine learning, DMPs, advanced visualisation – make this integration easier than ever. For example:

• Social CRM draws on Small Data (attitudes, segments, influencers).
• Combine it with Big Data (web analytics, transactions).
• The result: a powerful engagement and planning tool that fuses the human and the statistical.

The Takeaway

It’s not about “big vs. small.” It’s about knowing when to use each, and how to combine them. Get that balance right, and the payoff is exponential – richer insight, smarter strategy, and stronger business outcomes.

‍