Problem

Non-technical teams such as PM, R&D, and Regulatory had limited access to product data. They were unable to independently build queries or search through data without IT support. This caused delays, errors, duplicated work, and inconsistent analysis outcomes.

The need

To design a tool that supports intuitive data exploration without requiring SQL skills. It should allow users to save, validate, and share queries within a friendly interface that promotes autonomy and improves consistency.

My role

I was responsible for defining the problem and designing the Data Explorer application. Through user research, testing, and UX design, I created a solution that enables non-technical users to work effectively with data without technical barriers or BI team involvement.

1. Project goal

The organization needed a solution that would let non-technical users such as PMs, R&D, and Regulatory teams explore product data and build complex queries. These queries should be easy to save and share within the team, without requiring SQL knowledge or IT assistance.

2. Research and requirements definition

Research methods

• Five in-depth interviews with end users
• Observations of workflows using Excel and manual filtering
• Analysis of seventeen real-life query examples from spreadsheets and emails

Key user needs

• An intuitive interface for filtering data
• Ability to build complex logic using AND and OR
• Options to save and share queries
• Query history and version control
• Validation before running a query

Insights

• The absence of a shared repository led to repeated work
• Even experienced users preferred a click-based interface
• The lack of visual feedback during query building led to errors

3. Core features and user journey

3.1 Application start and data source selection

Users open the application from the main dashboard. They can either start exploring data from scratch or load it from a project or the shared query repository.

3.2 Default view (data table)

When the user opens the application, they see:

• a table with default results
• the ability to filter by columns such as Flavor Type, Status, or Project
• basic exploration tools available immediately

3.3 Manual query creation

The user clicks “Add Query” to build a filter. Example rules include:

• Solubility = Water
• Storage Conditions = Ambient
• Description contains “Ginger ALE”
• Flavor Label = Natural, WONF
• GMO Status = Not Detectable

The system:

• automatically suggests values from available lists
• validates empty or incorrect fields
• allows multiple conditions to be added
• shows the number of matching results in real time

3.5 Saving and publishing a query

The user can:

• save the query privately using Save New Query
• publish it to other system users using Publish Query

The system enforces title uniqueness and notifies the user if a query with the same title already exists.

→This is because the title serves as the identifier for the query, enabling easy search, distinction, and management. Duplicate titles could lead to confusion, accidental overwrites, or difficulty finding specific queries in the system.

4. Results and business value

Results

• Data retrieval time reduced from 30 minutes to 2 minutes. This was achieved by introducing a visual query builder that automates filtering and data search, along with the ability to save and re-run queries without manually setting parameters each time.
• Queries can be reused. This saves time, reduces errors, and ensures consistency — users can repeatedly run verified queries across time periods or with adjusted parameters.
• Easier preparation of comparison tests and product samples for clients.
• Consistent data use across departments — all teams rely on the same filters and logic.

Benefits

• Increased productivity in R&D and Regulatory teams
• Reduction in manual errors
• Elimination of duplicate efforts across teams
• Improved data quality control

5. Summary

The Data Explorer project is an example of a user-centered analytics tool built for non-technical audiences. The key to its success was a combination of deep user research, clear UX, transparent logic, and knowledge sharing.

In-depth research

During the discovery phase, we conducted interviews and observed users from different departments. This revealed real barriers to data access, particularly the difficulty of repeating analyses and the dependency on technical teams. As a result, we designed a tool that directly addressed user challenges, leading to stronger engagement and adoption.

Clear UX

The interface was designed for non-technical users and guides them step by step through the query-building process. Clear labels, a limited number of options, and plain language allow users to work with data independently. By simplifying complex operations, the app significantly expanded access to analytics within the organization.

Transparent query logic and filters

Every query is presented in a clear and understandable format. Users can see which filters are active and what data will be retrieved. This increases trust in the results and makes it easier to review and explain findings within teams. Transparency also supports auditing and verification.

Shared knowledge through the query repository

Users can save and share queries, building a shared knowledge base. This speeds up work, promotes best practices, and aligns analytical approaches across the organization. The repository becomes a trusted source of proven solutions — especially valuable for onboarding new team members and launching analytical initiatives.

Takeaways

• Even advanced users prefer visual tools over manually working in Excel or SQL
• Without a shared knowledge base, teams duplicate work and produce inconsistent analyses
• Non-technical users can explore data independently when guided by a step-by-step interface and supported by visual feedback
• Transparent queries and visible logic build trust in data and facilitate team discussions

Lessons learned

• When designing tools for non-technical teams, focus on a simple, predictable interface and plain language
• True user needs often emerge through observation, not just interviews
• A built-in query repository is more than an organizational feature — it’s the foundation of a strong data culture
• Validating user actions in real time and providing immediate feedback reduces errors and frustration
• Scalable solutions do not have to be complex