In Consideration of the Role of Data

“People don’t know what they want until you show it to them. That’s why I never rely on market research. Our task is to read things that are not yet on the page.” — Steve Jobs

I will come back to this comment from Steve Jobs, but I want to take an idea for a short walkabout that leads back to this concept as it relates to the role of data.

Let's start with the notion of data

Last week I had a conversation with my new friend over at Huge, the brilliant Emily Wengert. Her new role over at Huge, is as the Managing Director, Global ECD, Experiential. (Follow her - I believe you will like what she is doing.)

One of the discussion points we had, was around the subject of 'data'. I came to the conversation having just listened to her interview with José Manuel Simián.

In the interview with José, Emily said:

"My palette has become data, which is really different than... than maybe before when it was boxes and arrows, maybe when I first started, and then it became kind of gradients and fonts, and different things, and the materialism of design. But now I think its data, and so data is my new pixel. I'm really thinking about, and I have to know and understand that. And so, a lot of what we do is (I'm going to come back to it), is - story telling to each other. What do we want this data to be doing for people, and how many stories we can tell, and then turning that into systems of logic, and systems and guardrails, and thinking about it in terms of where does intelligence belong in this experience. Which is another really important question. Which is that, AI shouldn't be everywhere. It doesn't need to be in all steps, or the kind of AI you use, isn't always generative." - Emily Wengert

So this is the balancing act. While data is immensely valuable for providing insights and guiding strategies, it’s not always straightforward. There’s an art to interpreting data beyond its face value. As I shared of Emily's comment regarding her own personal transformation in her approach to design, emphasizing data’s pivotal role in crafting user experiences. Her insights offer a compelling starting point for rethinking how we integrate data into our creative and strategic processes, but I believe that data that is accurately gauged is important, but it must be constantly measured to show accuracy.

My thoughts around data, is that it tells us what is (up to a point), not what it could be. It’s often retrospective, analyzing past behaviors and trends. Innovation, on the other hand, often requires looking beyond current trends and anticipating what users will want before they know it themselves.

How has over-reliance on data hindered innovation in the past?

My first thought is of Swatch Watches from when I was in middle school - and how as a TREND, this was a permanent run for the rubber-band brand was set for life, but the trend died as fast as it came. Same for Fidget Spinners, Cabbage Patch Dolls and Tickle Me Elmo.

Here are a few relevant market study examples:

1 • Kodak’s Missed Digital Opportunity:

• Situation: Kodak was a dominant player in the film photography industry with extensive data showing strong sales of film and film cameras.
• Over-reliance on Data: Trusting their current market data, Kodak was hesitant to invest in digital photography, fearing it would cannibalize their film business.
• Result: Competitors embraced digital technology, leading to Kodak’s decline and eventual bankruptcy filing in 2012. Their reliance on existing data blinded them to the disruptive potential of digital innovation.

2 • Blockbuster Ignoring Streaming Trends:

• Situation: Blockbuster had data indicating high customer satisfaction with their in-store rental model.
• Over-reliance on Data: Confident in their data, they dismissed the emerging trend of online streaming and mail-order rentals.
• Result: Netflix and other companies capitalized on digital delivery methods, rendering Blockbuster’s model obsolete and leading to its downfall.

3 • New Coke’s Market Research Failure:

• Situation: Coca-Cola introduced “New Coke” in 1985 after taste tests and surveys showed consumers preferred a sweeter formula. I remember this personally and shared recently about the cola wars.
• Over-reliance on Data: The company relied heavily on quantitative data from taste tests, neglecting the emotional attachment customers had to the original formula.
• Result: The public backlash was swift, forcing Coca-Cola to bring back the original formula as “Coca-Cola Classic.” The data didn’t capture the brand loyalty and emotional connection consumers felt.

4 • Nokia’s Smartphone Hesitation:

• Situation: Nokia dominated the mobile phone market with strong sales data supporting their existing product lines.
• Over-reliance on Data: Confident in their market position, Nokia was slow to adopt touchscreen technology and advanced operating systems.
• Result: They lost significant market share to Apple and Android devices. Their reliance on current sales data prevented them from foreseeing the smartphone revolution.

5 • BlackBerry’s Decline:

• Situation: BlackBerry’s data showed that consumers valued physical keyboards and secure email capabilities.
• Over-reliance on Data: They continued to focus on these features, ignoring the growing demand for apps and multimedia capabilities.
• Result: BlackBerry couldn’t keep up with competitors who were innovating based on emerging consumer desires not yet reflected in existing data.

6 • Swatch Watches and Fad Products:

• Situation: Swatch capitalized on the trend for affordable, fashion-forward watches, with data showing strong sales during the peak of their popularity.
• Over-reliance on Data: Assuming the trend would continue indefinitely, they may have overproduced certain styles or failed to innovate beyond the fad.
• Result: As consumer interests shifted, sales declined. Similar patterns occurred with fidget spinners, Cabbage Patch Kids, and Tickle Me Elmo, where companies relied on current sales data without planning for the product lifecycle’s decline.

7 • Sears’ E-commerce Neglect:

• Situation: Sears had data indicating consistent performance through their catalogs and physical stores.
• Over-reliance on Data: They underestimated the impact of online shopping, as their data didn’t show an immediate threat from e-commerce.
• Result: Competitors like Amazon innovated rapidly, leaving Sears struggling to catch up in the digital marketplace.

8 • The Music Industry’s Digital Resistance:

• Situation: Record labels had data showing strong CD sales and underestimated the impact of digital music.
• Over-reliance on Data: They focused on fighting piracy rather than innovating new distribution models.
• Result: Companies like Apple with iTunes and streaming services like Spotify transformed music consumption, leaving traditional record labels behind.

Key Insights:

• Data Reflects the Past, Not Necessarily the Future: Companies relying solely on existing data may miss emerging trends that data hasn’t captured yet.
• Emotional and Intuitive Factors Matter: Data often fails to account for emotional attachments and unarticulated consumer desires.
• Innovation Requires Vision Beyond Data: True innovation often comes from anticipating needs and desires that consumers aren’t yet expressing or even aware of.

Data as the new Pixel?

Emily's approach, treating data as a “new pixel,” suggests a sophisticated and nuanced use of data, integrating it deeply into the design and decision-making process but not letting it dictate every step. Her focus on storytelling and systems of logic to guide the application of data in experience design is particularly insightful. It emphasizes using data to enhance human experiences rather than letting it dominate them. The trick - is the balance.

The idea that “AI shouldn’t be everywhere” and that its use needs to be thoughtful and selective is another critical point. This approach prevents over-reliance on generative AI systems and ensures that human creativity and insight remain at the forefront of branding and marketing strategies.

In the recent ages experienced of the Web 1.0 and 2.0, every measurable was used to gauge the decision making process and evaluate the effectiveness of activities based on vanity metrics (views, likes, comments, shares) rather than actual sales and user engagement with products and services. The thing with having measurable insight (ie - data) is it gives the marketer a sense that they have insight on the blind spots that John Wanamaker spoke about:

“Half the money I spend on advertising is wasted; the trouble is I don't know which half.” - John Wanamaker, American marketer

Insight from data though, is past looking.

The challenge then, let me state it again, is to strike the right balance: leveraging data to inform decisions without stifling innovation and creativity. This means continuously questioning and validating the data, ensuring it serves the vision rather than confining it.

This brings us back to Steve Jobs’ insightful observation: ‘People don’t know what they want until you show it to them…’ Like Jobs, we must recognize that while data illuminates the path already traveled, innovation requires us to venture into uncharted territory. It’s about reading ‘things that are not yet on the page,’ about anticipating needs and desires that data alone cannot reveal.

In a world dominated with data, let’s remember 2 key things: numbers tell only part of the story and the human element—our creativity, intuition, and vision—is what transforms data into meaningful innovation. It’s about seeing not just what is, but what could be. Use data as a compass, not a map—as a tool to inform our journey, not dictate it.

It’s time to transcend the numbers and embrace the art of possibility. Let’s challenge ourselves to envision and create what doesn’t yet exist, meeting needs people haven’t even realized they have. As we leverage data in our strategies, let it serve as a foundation upon which we build—not a box that confines us. The most remarkable advancements emerge when we dare to imagine the unseen and bring it to life. After all, the most groundbreaking innovations arise not from data alone, but from a profound understanding of the human experience.

Brian Sykes