Internal Data Validation Tool

Designing an operational platform to improve data accuracy and efficiency.

Overview

ClearGlass Analytics delivers transparent cost and performance data across the UK and European asset management market. A critical part of this process involves validating large volumes of data submitted by asset managers before it becomes available to clients.

The internal data team was responsible for reviewing and verifying this information, yet the workflow relied heavily on fragmented external tools. This created operational inefficiencies and increased the risk of inconsistencies in the validation process.

To address this, we designed a dedicated internal validation tool integrated into the platform, enabling the team to manage submissions more efficiently while maintaining the high level of data accuracy expected by clients.

My Role

Responsibilities included:

• Conducting user research with internal teams

• Mapping workflows and operational processes with the product manager assigned on that project

• Designing wireframes and interactive prototypes

• Running usability testing sessions with the team

• Collaborating with product managers and engineers to deliver the MVP

• Supporting post-launch iteration and optimisation
  
  

The Problem

Before the project:

• The data validation workflow relied on spreadsheets and disconnected tools

• Processes were manual, fragmented, and time-consuming

• Reviewing submissions required switching between multiple systems

• Operational complexity and time increased during annual reporting cycles
  
  

Because ClearGlass positions itself as a trusted data provider, maintaining accuracy and efficiency was critical. The existing workflow placed significant pressure on the internal team and was not scalable as the company grew.

Key Constraints

This project was shaped by several unique constraints:

Industry cycles
Asset management reporting follows strict annual cycles. Missing these windows would delay improvements by an entire year.

Data accuracy requirements
Even small inconsistencies in submitted data would impact client reporting and benchmarking.

Complex workflows
The validation process involved multiple review stages and required clear visibility of submission status.

Specialised users
The internal data team had deep domain knowledge but limited access to purpose-built tools.

Research & Discovery

To quickly align stakeholders and explore solutions, we conducted a series of cross-functional workshops.

Participants included:

• Product and design team members

• Engineering representatives

• Internal data specialists (primary users)

• Customer-facing teams
  
  

Key research activities

We used several product discovery methods:

• Proto-personas to capture internal user needs

• Journey mapping to visualise the existing validation workflow

• Detailed flow charts to make sure all the aspects of the work and all tasks are covered

• Collaborative workshops to gather domain insights
  
  

This phase helped uncover the most critical friction points in the current process.

Key insights

1. Validation tasks were spread across too many tools.

2. Users needed clear visibility of submission progress.

3. Repetitive manual checks slowed down the workflow.

4. The process became particularly difficult during peak reporting periods.

Ideation

During the ideation phase, cross-functional participants generated solution concepts through structured sketching sessions.

We translated these ideas into a storyboard describing the ideal validation workflow, outlining:

• Submission review stages

• Data verification interactions

• Status tracking

• Collaboration points between team members

• Different types of access among the users

This storyboard served as the blueprint for the prototype.

Design

I translated the storyboard into interactive wireframes and a clickable prototype, focusing on:

• Clear information hierarchy

• Reducing cognitive load during data review

• Improving workflow transparency

• Enabling faster navigation between submissions
  
  

The prototype simulated realistic validation scenarios so our internal users could test the proposed workflow.

Testing & Iteration

The prototype was tested with members of the internal data team performing their real tasks.

Because of the complex nature of the validation process, several iterations were required to refine:

• data visibility

• validation interactions

• workflow clarity

This iterative testing allowed us to address usability issues before development.

MVP Launch

Once validated, the feature was developed and released as an MVP within the internal platform.

The MVP focused on enabling the team to:

• review submissions within one environment

• track validation progress

• reduce reliance on external tools

• pick up the work after other team members

• communicate with watch other inside the tool and leave the comments

Continuous Improvement

After launch, we monitored usage and gathered feedback from the internal team. Iterative improvements were implemented to further optimise the workflow.

By the following annual reporting cycle, the tool had significantly accelerated the validation process by 50%, enabling the team to handle submissions more efficiently while maintaining high standards of accuracy.

Impact

The internal validation tool delivered several key outcomes:

• Reduced reliance on fragmented external tools from 3 to 1

• Improved workflow visibility and transparency
  
  

• Increased operational efficiency during reporting cycles by 50%
  
  

Supported ClearGlass’ commitment to accurate and reliable investment data