Synthetic data is machine generated data with the aim to replicate real-world patterns. It enables organisations to innovate faster, while bringing rich consumer insights into every marketing decision. This blog explores what it is, why it matters and what value we believe it can bring to organisations.

Let’s start here: what is synthetic data and why is it relevant today?

You may be hearing the term “synthetic data” a lot at the moment, especially if you’re working in research, insights or AI. It’s one of those phrases that sounds futuristic, slightly mysterious and a little scary all at once.

So, what is it? At its heart it’s surprisingly simple: artificially generated data that mirrors the statistical patterns and structures of real-world information, without being directly collected from real people, transactions or events. It’s not copied data; it’s newly created data that behaves like the original and allows us to model reality.

And that’s exactly why it’s so relevant today. As organisations race to build smarter AI systems and faster insight engines, the need for high-quality, diverse data delivered rapidly and at scale has never been greater. At the same time, access to robust datasets is becoming more complex and concerns around bias and representation are under increasing scrutiny.

Synthetic data helps to navigate these pressures. Generated through statistical modelling, generative AI or simulation environments, it recreates real-world scenarios digitally. As organisations work towards AI-driven KPI’s, it offers a scalable way to fuel innovation and bring stronger consumer insights into more marketing decisions.

Lost in translation?

In the world of synthetic data and AI, many terms are currently in use. As the industry and best practice continue to evolve, it is crucial to stay grounded in clear definitions.

You’ll often hear terms like augmented data and fully synthetic data. While closely related, they refer to slightly different approaches.

Augmented data adds to an existing study or project by creating controlled variations. It expands datasets by adding diversity or filling gaps, making them more robust.

Fully synthetic data, by contrast, is where a completely new data set is generated, comparable to setting up an entirely new study. This is the territory of virtual AI audiences where we’re trying to make an AI representation of people’s behaviours, thoughts and feelings. These can take the form of AI personas, which represent an aggregated group or archetype in the same way personas are used in marketing. Or they can be digital twins, which are one-to-one representations of real people and their AI counterparts.

Where is the value in synthetic data for organisations?

Synthetic data is often framed around efficiency, such as reducing costs, speeding up AI training and enabling faster experimentation. While this is true, its deeper value lies in democratising insights and embedding a consumer perspective across decisions.

By generating scalable datasets that reflect real-world patterns, teams across marketing, innovation and strategy can explore questions, test ideas and model outcomes. They are no longer limited by access to traditional research. It also helps address gaps or biases in existing datasets, making AI and analytics more representative and reliable.

The impact of synthetic data is about making consumer obsession universal by including a customer perspective in every marketing decision:

1. Human understanding: Create hypotheses, deepen existing research and bring segments to life.
2. Co-creation: Generate and refine ideas, validate messaging and value propositions and iterate expressions.
3. What if’s: Simulate market scenarios, test decisions before making them and anticipate future outcomes.

Ultimately, synthetic data helps organisations put consumer understanding at the heart of every decision, making insight more accessible, actionable and impactful.

Our lens: keeping the human at the heart of AI

Our approach to AI and synthetic data starts with something simple but often overlooked: real people. Their lived experiences, emotions and context are what make insights meaningful in the first place. AI doesn’t replace that foundation; it builds on it. The most powerful applications of AI begin with humanity, not algorithms. While AI helps us move faster and work smarter, it doesn’t replace human imagination, judgement or creativity. People remain firmly in the lead, shaping, questioning and guiding every outcome.

Trust sits at the heart of how AI is applied. Its use is guided by a deliberate commitment to responsibility, transparency and empathy. The limitations of AI – bias, hallucinations and occasional errors – are acknowledged and managed, with human-led insight kept firmly at the centre.

As the synthetic data space continues to evolve, the real opportunity lies in blending deep human understanding with the capabilities of AI. This balance enables faster progress, stronger collaboration and helps brands move with both authenticity and distinction.