Why It’s Critical That Data Strategy Elevates, Enables, and Strengthens Creative

For more than a decade, Sisi Zhang has played a pivotal role in expanding Razorfish’s data and analytics capabilities, overseeing a team of more than 150 data professionals. She spearheaded Razorfish’s increased investments in data automation and engineering skills to keep a competitive edge against rising industry needs. Sisi was promoted to chief data and analytics officer in 2024 to drive forward business impact through more robust data-driven offerings, implementing standardisation processes, elevating data instrumentation capabilities to simplify client needs, and empowering her team’s niche and rich expertise.

LBB> What’s the number one question that clients are coming to you with when it comes to how they can better use data to enhance the creativity of their content and experiences?

Sisi> There are two primary challenges we see when it comes to better using data to enhance content and experiences for our clients. The first is understanding what data to use and what is most applicable, and the second is finding the right balance of quantitative, data-informed insights and qualitative, more intuitive approaches based on experience. Since the quality of insights that can inform creative experiences depends on the data inputs, the second is somewhat a subset of the first. We typically encourage clients to start by understanding and augmenting data they might want or need to use. From there, it’s easier to determine how much priority to give to certain insights.

LBB> How can you make sure that data is elevating creative rather than forming a wind tunnel effect and knocking all the interesting or unique edges off that make something distinctive?

Sisi> It’s critical that data strategies aren’t seen as inspiration or idea-killers, but, as you said, something that elevates, enables, and strengthens creative. Having the right data inputs as a starting point is key. This can help determine a testing roadmap to incorporate those interesting or unique elements that drive distinct experiences for consumers. The better the data and the more robust the test plan, the better we can lean into those more interesting elements.

LBB> More brands are working to create their own first party data practice - how can a brand figure out whether that’s something that is relevant or important for their business?

Sisi> For most brands, creating a first party data practice isn’t just relevant, it’s crucial – especially when we think of external factors that influence how a consumer moves through a purchase decision. Having more relevant first party data helps inform how to drive stickiness and resonance for those consumers so brands can stand out.

LBB> We talk about data driving creativity, but what are your thoughts about approaching the use of data in a creative way?

Sisi> This is a great question, and we believe approaching the use of data in a creative way is important for us to help brands elevate overall experiences. This could be something as simple as thinking through a data connection from two data sets in a different way, visualising data insights beyond using standard charts and graphs, or also incorporating creative feedback along the product development process to ensure that the output is as relevant as possible. But this isa two-way relationship, and recognising that will only strengthen the overall impact of each pillar.

LBB> 'Lies, damned lies, and statistics' - how can brands and creative make sure that they’re really seeing what they think they’re seeing (or want to see) in the data, or that they’re not misusing data?

Sisi> Brands need to make sure they know exactly what data is available, and how to use and enhance it to validate hypotheses. That may require appending other data as needed, or additional steps. But this is an ongoing process with continuous fine-tuning – it’s not a process of setting and forgetting.

LBB> What are your thoughts about trust in data - to what extent is uncertainty and a lack of trust in data (or data sources) an issue and what are your thoughts on that?

Sisi> Trust is critical because it makes it easier to use data to provide the right and relevant insights. Often, there can a lot of data that might be “noisy” or not as usable, so being able to understand what the data is meant to do, how it was collected, if consumers were able to opt in and make their preferences known, or how relevant it is, are the first steps in determining the right data to use. But without trust or confidence in the data, it loses its value and impact to drive meaningful outcomes.

LBB> With so many different regulatory systems in different markets regarding data and privacy around the world - as well as different cultural views about privacy - what’s the key to creating a joined up data strategy at a global level that’s also adaptable to local nuances?

Sisi> With many of our clients, we adopt a twofold approach with a global strategy, but a local market application. The key is to ensure consistency and reliability of data, standardisation of taxonomy and data collection processes where possible, adherence to local market regulations, and the ability to join data sets for specific applications only as needed for local markets (for example, not transferring data when it’s not allowed or not critical). This ensures that a global framework can be standardised as much as possible for local markets, while local markets are also able to adjust as needed to accommodate specific market nuances.

LBB> What does a responsible data practice look like?

Sisi> A responsible data practice needs to start with privacy-first data collection on a first party data ecosystem. From there, it can be built out – with access and permission governance that gets standardised across the organisation. These are fundamental, but essential foundational steps to putting a responsible, yet scalable data practice in place. Then, it can be customised depending on the specific circumstances and objectives within that organisation.

LBB> In your view, what’s the biggest misconception people have around the use of data in marketing?

Sisi> The biggest misconception is that more data or more recent data is better. Sometimes, less is more and prevents the models from 'overthinking' to reveal an insight that isn’t really there. Having historical data from beyond a traditional attribution window can also provide interesting insights into the consumer journey, and shouldn’t always be dismissed as not relevant or not applicable. When we are too rigid, we may miss out on meaningful discoveries.