Data-Driven Design: A Creative's Analytics Journey

The Curious Mind and the Rise of Data in Product Design

Have you ever, during your childhood, pondered the number of chairs needed to ascend to the heavens?

Perhaps not. This was a frequent line of inquiry for me.

From a young age, I consistently posed questions beginning with “how many” or “how much.” Some inquiries were practical – for example, the exchange rate between US dollars and Vietnamese Dong. Others were decidedly more whimsical, such as attempting to quantify the height of the sky in terms of chair stacks.

Remarkably, I’ve continued this habit of inquisitive statistical exploration into my twenties, managing to do so without incurring significant animosity.

It has proven to be a beneficial trait in the field of product development.

Early Experiences with Data Inquiry

My initial role as a product designer was within a dynamic, albeit small, fintech startup. The engineering team there also possessed data extraction capabilities.

I regularly engaged them with questions concerning feature performance and user behavior, such as, “What was the total number of exports generated by the recently released feature?” and “How many administrators configured at least one rule on this specific page?”

I possessed a strong interest in quantitative analysis, but lacked a clear understanding of how to begin.

The Growing Demand for Data Literacy

I quickly realized I wasn’t alone in this situation.

Even then, a noticeable demand for fundamental data literacy was emerging within the technology sector, and this demand continues to intensify annually.

Terms like “data-driven,” “data-informed,” and “data-powered” are now commonplace in product briefs across virtually all tech companies.

However, questions remained: Where does this data originate? Who is authorized to access it? How can one initiate independent data exploration? And crucially, how can this data be effectively utilized to enhance daily design practices once access is gained?

Key Questions for Designers

• What is the source of the data used in product decisions?
• Who within the organization has access to relevant data sets?
• How can designers begin to independently analyze data?
• How can data insights be integrated into the design process?

Unlocking Data Access: Overcoming the Obstacles

Kickstarter operates under the principle that “curiosity is our compass.” This drive for knowledge and information, however, often clashes with the established processes of larger, more rigid organizations – even if they don’t acknowledge it – as it can disrupt established workflows.

A curious mindset prompts investigation and verification before proceeding, which is crucial for determining the value of any undertaking. Formulating numerous questions – what, how, why, who, and how many – is vital for informed decision-making.

The answers to these inquiries, or at least partial answers, likely reside within your organization’s existing business intelligence (BI) tool. However, accessing this information often necessitates a formal data request and a detailed justification for its need.

While safeguarding personally identifiable information (PII) and sensitive data through established protocols is undeniably important, excessive hurdles can be counterproductive.

When obtaining information transforms into a struggle to gain approval from a “data governance council,” it introduces inefficiency and bureaucracy. Furthermore, some organizations actively discourage data discovery due to concerns about transparency regarding their financial status.

This hierarchical approach fosters distrust and hinders collaborative efforts. Ultimately, it can stifle data self-discovery, a fundamental skill for anyone aiming to develop and maintain a successful product.

What contributes to the steep learning curve associated with traditional data analysis for those without specialized training? One hypothesis is that tools like Tableau, Qlik, and SAP products can appear overwhelmingly complex.

These are undeniably powerful analytical platforms that have proven beneficial for business leaders, but they demand significant time and effort to master. They aren’t ideally suited for individuals like myself – inquisitive but inexperienced data explorers.

Their user experience (UX) is often intricate, and their user interfaces (UIs) are cluttered – significant concerns for any product designer. Moreover, data analysis is frequently perceived as requiring coding skills, a prospect that discourages a substantial portion of the creative workforce.

The Challenges of Traditional Tools

• Complexity: Traditional BI tools often have a steep learning curve.
• Intimidating Interface: The UX and UI can be overwhelming for beginners.
• Coding Requirement: The perception that coding is necessary creates a barrier to entry.

Self-Service Data Analysis

The increasing popularity of platforms such as Squarespace, Webflow, and Airtable demonstrates the current trend towards no-code tools. This shift is also impacting the analytics sector, with new solutions emerging that empower individuals without specialized expertise to engage in data exploration.

Platforms like Looker and Metabase feature user-friendly graphical user interfaces (GUIs). These interfaces enable users to execute impactful queries with relative ease, often without requiring any knowledge of SQL.

I've adopted a workflow where I initially attempt to construct a Looker query before requesting assistance from the Insights team. Frequently, I find that the process of building the query itself clarifies the question I'm trying to answer. Dimensional limitations often become apparent only when actively working with the data, rather than simply formulating the inquiry.

Relying exclusively on a GUI does present certain limitations. A significant challenge for those without a programming background is the necessity to develop a strong understanding of the underlying data architecture of the product.

Familiarizing yourself with JavaScript data structures, particularly the JSON object as detailed in Mozilla’s web documentation, is highly recommended. A grasp of fundamental data structure concepts will enhance your comprehension of the database and, crucially, facilitate navigation within it. Be patient; this learning process can be lengthy, especially if the database lacks clear organization.

In my experience, analysts can greatly assist non-analyst users by establishing and maintaining consistent, intuitive naming conventions for data objects. As your proficiency with the data tool grows and you seek to broaden the scope of your inquiries, coding will become necessary. Fortunately, most contemporary analytics tools provide custom syntax and comprehensive documentation. With practice, your coding abilities will improve.

Design Informed by Data: Insights and Lessons Learned

How can the ability to interpret data be applied to enhance design choices? My initial approach to validating any design concept involves a thorough analysis of current user behavior. While direct user interviews are valuable, combining them with data analysis provides a broader and more comprehensive understanding.

Exploring initial usage data has enabled me to move beyond superficial design elements and develop well-rounded, practical experiences. Addressing fundamental usability issues is more impactful than minor interface adjustments. Analyzing existing user behavior early in the process reveals how product features are utilized, identifies patterns among different user groups, and predicts potential adoption rates for new functionality.

In my past experience, I’ve encountered common pitfalls when designing with data, such as accepting quantitative data without critical evaluation and prioritizing metric improvements over user needs. The misuse of averages and hasty generalizations have also been areas for improvement.

A primary error often made by those utilizing statistics is focusing excessively on proving assumptions rather than seeking genuine improvement. It’s crucial to remember the human element behind the data and acknowledge potential biases. If you lack formal training in data analysis, regular consultation with a qualified analyst is recommended to avoid misinterpretations and learn from potential errors.

For those in leadership positions, investing in organization-wide data literacy can significantly benefit your product development process. Empowering employees to independently ask and answer key product questions reduces reliance on lengthy insight-gathering procedures.

If your organization hasn’t yet adopted a contemporary data exploration tool, it’s worth considering. Furthermore, offering introductory analytics workshops to your team can foster a greater interest in data-driven decision-making, even in areas not traditionally associated with analytics.

Here are some key benefits of data-driven design:

• Improved user experience
• More effective feature adoption
• Reduced development waste
• Stronger product-market fit

Ultimately, leveraging data effectively allows for the creation of products that are not only aesthetically pleasing but also genuinely useful and valuable to users. Data analysis should be viewed as a collaborative process, combining analytical rigor with a deep understanding of human behavior.