Elise Morris Says Data Doesn't Remove Risks, But Creates Better Ones

Elise Morris is head of intelligence at Digitas Australia and is a senior data and intelligence leader with more than 17 years’ experience driving data-led transformation for major consumer brands. She was named in B&T’s 'Best of the Best Top 10 Data Scientists' in 2025.

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?

Elise> In a world that feels noisier, faster, and less forgiving than ever, our clients are searching for more compelling ways to connect with their customers that cut through the noise. A challenge I hear repeatedly is: “We’re drowning in data. So why does it feel like we’re not learning anything new?”

Most organisations have no shortage of metrics, but very few are designed to surface genuinely transformative insights. Too often, data is structured to confirm what we already know, not to challenge how we think. That’s the real gap.

The most valuable analysts behave like detectives. They’re not just answering the question they’ve been asked; they’re interrogating the problem beneath it and looking for inconsistencies, tensions, and signals hidden between datasets.

The richest insights rarely come from customer data alone. They emerge when you connect multiple perspectives, behavioural data, cultural signals, economic pressure points, and shifting social norms. These forces shape how people think, feel, and decide long before they ever interact with a brand.

The magic happens when analytics isn’t a post-rationalisation layer, but a live creative ingredient, and when data teams sit in the brainstorm and help shape the tension, the audience truth, and the opportunity from the start.

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?

Elise> I always ask: are we using data to find permission or to find possibility? Permission-seeking data use (the numbers say this is safe) tends to produce safe work. Possibility-seeking (the data shows there’s a real gap here, nobody is filling) is where creative teams can actually run. The best creative partnerships I’ve seen use data to sharpen the question, not prescribe the answer.

LBB> Can you share with us any examples of projects you’ve worked on where the data really helped boost the creative output in a really exciting way?

Elise> ALDIfy (ALDI’s digital hub for shoppers seeking recipe inspiration) is one project I’m particularly proud of. This started with a small but powerful tension in the data: people obsess over food trends online, yet a huge proportion say choosing what to cook is genuinely stressful.

Anyone who loves cooking knows the feeling of having inspiration everywhere, yet facing decision paralysis at dinnertime. The data simply gave language to something very human.

Instead of just reporting on the insight, we built the idea around it. By continuously analysing emerging food signals across social platforms, we transformed fast-moving trends into practical, shoppable inspiration. Recipe ideas that felt intimidating suddenly feel doable.

Creatively, what excited the team was that the data wasn’t static. It shaped the experience on the platform in real time. Trend signals influenced what content surfaced on the platform, how it was framed, and how easily someone could move from inspiration to action.

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?

Elise> I’d start with three questions. One, do you have repeat interactions where a value exchange makes sense? Two, are you currently dependent on intermediated targeting/measurement you don’t fully control?

Three, do you have use cases that would materially improve with better identity and consented signals (personalisation, CRM, loyalty, service)?

First-party data only matters if it’s tied to action; an insight is only as useful as your ability to act on it. First-party data isn’t a checkbox; it’s a capability that supports marketing maturity.

If you’re investing in CRM, CDP, personalisation, or measurement resilience, you need a first-party foundation, built on clear consent and transparency, because it’s built on trust and value exchange. The brands that win don’t collect everything; they use progressive profiling to earn the right data over time.

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

Elise> I think the phrase “data-driven creativity” is slightly misleading and sometimes dangerous. When data drives, creativity often follows the safest possible path. When data provokes, creativity gets interesting.

Using data creatively means treating it like raw material, something imperfect, human, and open to interpretation. Patterns matter, but so do the anomalies. The outliers are often where the most culturally relevant ideas live.

The goal isn’t to remove risk. It’s to take better risks. Data should give you the confidence to leap, not the excuse to play it safe.

LBB> "Lies, damned lies, and statistics" -- how can brands and creatives 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?

Elise> “Lies, damned lies, and statistics” still applies, especially when there’s pressure to prove something. Most misuse isn’t malicious. It’s rushed. Or defensive.

We rely on three safeguards: Define the question before you open the dashboard. Triangulate sources; one dataset is rarely enough.

Actively invite dissent. Someone in the room should be trying to break the interpretation.
Data doesn’t remove bias. It can amplify it if you’re not careful.
Curiosity and intellectual humility are underrated analytical skills, and some of the most important ones.

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?

Elise> Trust operates in two directions. Consumers need to trust brands with their data, which means transparency, consent, and a clear value exchange.

But teams also need to trust the data enough to act on it.

Ironically, the biggest barrier I see isn’t poor data, it’s waiting for perfect data. In reality, most meaningful decisions are made with incomplete information. What matters is understanding the limits, the trade-offs, and the risks.

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?

Elise> Global consistency comes from shared principles, not identical execution. We recommend a global backbone (consent standards, taxonomy, governance, and measurement logic) with modular local layers for regulatory and cultural nuance. Think ‘one operating system, many interfaces’.

LBB> What does a responsible data practice look like?

Elise> At its core, it’s about three things: consent, clarity, and usefulness. You collect only data you can clearly explain, protect what people entrust to you, and use it to genuinely improve their experience, not just to make targeting more efficient.

The brands that treat responsibility as a strategic asset (not a legal constraint) are the ones that earn long-term trust and loyalty.

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

Elise> That more data equals better decisions. The volume of data available has never been higher, and I’ve never seen more confusion about what to do with it. The constraint is almost never the data itself; it’s the ability to ask the right questions and act on the answers.

LBB> In terms of live issues in the field, what are the debates or developments that we should be paying attention to right now?

Elise> The tension I keep coming back to is between data democratisation and AI adoption, and why getting that balance wrong is a real risk for creative businesses. There’s enormous pressure to open data up, to let everyone interrogate it.

AI tools are accelerating that pressure because suddenly, anyone can ask a complex question of a dataset without knowing SQL or statistics. But democratisation without governance doesn’t produce better decisions; it produces confident wrong ones.

In creative industries, especially, where gut instinct and data are already in an uneasy relationship, the last thing you want is AI handing people beautifully formatted answers built on inconsistent definitions or poorly understood data.