Cultural Nuances in Data Visualization
This is the continuation of the transcript of DM Radio’s program titled “What You See Is What You ‘Get’ – How Data Visualization Conveys Insight.”
Wayne Eckerson: Although, you know, visualization is a nuanced, cultural type of thing. For example, Guess – an apparel design company that makes jeans and other things like that – put out a dashboard that converted some tables to charts and graphics. They thought, “Well, this will be good. Users will like this much more than the tables.”
What they found was that, no, users did not find the charts much more usable. In fact, there was more information on the tables than in the charts. Whereas in the charts they had to click around a couple of times to get all the information they needed; in the tables it was just all there.
So you have to be careful that when you convert something to visual format, you’re delivering more information not less information than the numeric representation. Despite the density, it’s still needs to be easy to view and observe. That really is a challenge.
In the case of Guess, they tried the dashboard a second time. This time, they decide to kind of acknowledge the culture of the company – their audience being fashion designers and merchandise buyers. They had to really understand what the fashions were.
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The second dashboard, instead of viewing things by line and bar charts, actually put pictures up of the products in a way that also helped to rank them from one to ten. The particular page they sent to me to look at really just showed women’s skirts on models.
So you are just looking at the skirt and at first blush it doesn’t look anything like a dashboard. It looks like a men’s magazine. But the users found this dashboard to be incredibly helpful in providing a heck of a lot more information than the first iteration of the dashboard or tables of numbers.
They could actually see the product and understand how these different variations of skirts ranked or what were the penetration and sales rate. It was only a few easy, quick steps to get all the information they needed.
And, they jest it up--you know—it’s kind of decorative too. They kind of broke a lot of the rules that you commonly associate with visualization, such as keeping it clean. But in this case, the decoration works very effectively.
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Jim Ericson: We profiled them ourselves and these apps were built for fashion buyers. What a logical association to make between an image -- it was a skirt and ranking of how it goes with customers for a buyer.
I’ll talk about a role based tool to basically provide that sort of visual image compared to what they are looking at out in the field on their iPad. So you know it is a cultural thing -- the way they did it on top of a conventional MicroStrategy platform. It really fit with their audience.
How a Fashion Design Firm Combines Visualization, AI, and Analytics
Modern fashion design firms are blending the art of design with the science of data. By integrating advanced analytics, artificial intelligence (AI), and compelling data visualizations, these companies are transforming how they design collections, manage production, forecast trends, and engage customers. Here’s how a forward-thinking fashion design firm is combining visualization, AI, and analytics to stay ahead of the curve.
1. Turning Raw Data into Design Intelligence
The fashion world moves fast. Trends change overnight, and consumer preferences shift unpredictably. To adapt, this firm has built an analytics pipeline that draws data from multiple sources—social media sentiment, e-commerce activity, supply chain status, runway show feedback, and even fabric sensor data.
With this raw data, the firm applies AI-powered tools to identify emerging patterns. For example, natural language processing (NLP) models analyze customer reviews and fashion blogs to spot recurring keywords around color preferences, fabric textures, and fit. Computer vision algorithms also scan millions of images from Instagram and TikTok to detect trending silhouettes or popular accessory combinations.
All of this data is processed and visualized using interactive dashboards, allowing designers and merchandisers to make faster, smarter decisions about upcoming lines. Instead of relying purely on intuition, they now have quantifiable insights that support creative direction.
2. Predicting Trends with Machine Learning
Traditionally, trend forecasting was a mix of experience, instinct, and observation. Today, the firm uses machine learning models trained on years of historical sales, weather data, and global fashion cycles to predict what styles and items are likely to become popular in future seasons.
These models consider regional differences, economic indicators, and even social events to adjust predictions. For example, if the model detects an uptick in eco-conscious buying behavior, it might recommend incorporating more sustainable fabrics and highlighting those choices in marketing campaigns.
The result is greater confidence in seasonal planning. Designers can proactively prototype styles aligned with predicted demand, while procurement teams can reduce excess inventory and streamline the sourcing process.
3. Enhancing Design Collaboration with Visualization Tools
Visualization isn’t just for data dashboards—it also fuels the design process itself. The firm uses collaborative 3D modeling software and augmented reality (AR) interfaces to visualize and iterate on clothing designs in real time.
Designers upload sketches, CAD files, and textile scans into a shared workspace where AI tools simulate how garments will look and move on different body types. This eliminates the need for physical samples in early stages, accelerating the review cycle and reducing waste.
Stakeholders—from creative directors to marketing leads—can interact with the models via immersive visualizations, making suggestions on color palettes, drape, and embellishments. This visual feedback loop ensures everyone is aligned before a piece goes into production.
4. Real-Time Retail Analytics for Smart Decisions
Once products hit the market, the firm doesn’t stop analyzing. StyleBI dashboards give real-time views into retail performance across channels—brick-and-mortar, online, and wholesale partners. Interactive visualizations track key metrics such as sell-through rates, return reasons, regional demand, and influencer-driven spikes in sales.
AI tools monitor anomalies and trigger alerts if, for example, a popular item starts underperforming in a key market. The marketing team can then adjust campaigns, while logistics can reroute stock to higher-performing areas.
These insights feed back into the design process. For instance, if oversized jackets underperform but cropped versions soar in sales, future collections adjust accordingly—creating a continuous learning loop powered by analytics.
5. Customer Personalization Powered by AI
The firm also uses AI to enhance the customer experience. Machine learning models analyze individual shopping behaviors and preferences to deliver hyper-personalized recommendations on the company’s e-commerce site.
Customers are shown product combinations based on their browsing history, purchase patterns, and even the weather in their location. Visualization tools help stylists create virtual “lookbooks” that adapt dynamically for each user, increasing engagement and boosting conversion rates.
Beyond the web, the firm uses AI chatbots and voice assistants to help customers choose sizes, explore collections, and even schedule fittings—creating a seamless, data-enhanced brand experience.
6. Sustainability and Supply Chain Optimization
Fashion’s environmental footprint is under scrutiny, and this firm is using data and visualization to become more sustainable. StyleBI dashboards track supplier performance, fabric utilization, and shipping emissions. AI models suggest alternative sourcing strategies or optimized routes to reduce carbon impact.
Visual supply chain maps allow production teams to monitor delays and disruptions in real time. When issues arise—like raw material shortages or shipping delays—the system offers predictive rerouting and inventory reallocation strategies.
This proactive, analytics-driven approach ensures the brand can maintain ethical sourcing commitments while delivering on-time collections.
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