A data modernization initiative attempted to consolidate a fragmented network of legacy reports by relying solely on automated system usage logs. By failing to engage the business users who relied on the data, the IT-led program was positioned to deliver a suite of new dashboards that completely missed actual operational needs.
Case Study
One dashboard replaced hundreds of disconnected reports.
When a global footwear and apparel company’s data foundation modernization outpaced the dashboards built on top of it, we shifted a stalled reporting program from an isolated IT rollout into an initiative engineered around user demand.
Engagement at-a-glance
Industry:
Retail
Organization size:
70,000+ employees globally
Services:
Product Management
Digital Transformation
Data, AI, & Automation
Digital Transformation
Data, AI, & Automation
The Challenge
The dashboards functioned perfectly, yet the end users who required them were never consulted.
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Development prioritized by automated logs instead of stakeholder engagement
The team rebuilding the reporting tools identified the most frequently accessed legacy dashboards to dictate the development pipeline. By relying exclusively on raw usage volume, developers overlooked direct user input, failing to analyze what the business actually required from the modernized system.
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Preliminary demonstrations exposed critical gaps in operational alignment
When the initial rebuilt dashboards were presented to field stakeholders for final approval, the pushback was immediate. The tools did not mirror actual team workflows, and stakeholders had no visibility into the requirements driving development. A standard review step quickly turned into an outright rejection of work that users had no part in shaping.
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A centralized maintenance model incapable of scaling to global demand
Even if every dashboard could be successfully updated to technical specifications, relying on a small centralized team to maintain hundreds of unique reports across multiple regions and business units was an unsustainable model that could never keep pace with the organization's daily data needs.
Our Approach
We paused the rollout to rebuild the program around what stakeholders actually needed.
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1
Investigated the data architecture before assessing what stakeholders needed.
Built technical fluency in the newly deployed data foundation prior to assessing user needs. Understanding the system capabilities and limitations relative to the legacy environment allowed our team to lead highly credible alignment conversations with the core development engineers.
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2
Deployed the initial dashboard pipeline to surface the alignment gap
Delivered the first wave of in-progress dashboards to business users for evaluation according to the legacy project plan. The resulting feedback provided the unmistakable evidence needed to demonstrate the underlying gap between IT assumptions and business requirements.
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3
Paused active delivery to gather requirements directly from the field
Suspended ongoing dashboard development to facilitate intensive requirement gathering sessions with affected business units. These sessions exposed a critical, cross-functional demand for a single platform health interface that the original technical scoping had completely missed.
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4
Developed and deployed a consolidated platform health dashboard
Recognizing that a centralized engineering team could not scale to support hundreds of legacy reports, we began transitioning the modernized data infrastructure into an accessible self-service platform. This enabled stakeholders to build custom reports and extract insights directly without requiring specialized coding skills.
Value Delivered
Re-engineering data reporting into a user-centric operational asset.
Delivered a comprehensive platform health view optimized for business users
The new system provides leadership with a unified, at-a-glance visualization of operational health across the full customer journey, complete with historical period comparisons and earning strong initial validation during user acceptance testing.
Automated manual workflows to reclaim significant operational time
Prior to this implementation, one business unit dedicated three full business days per month to manual scorecard compilation, while another spent up to six hours weekly on identical tasks. The new architecture updates daily, completely eliminating those manual processes.
Mitigated legacy data gaps prior to business disruption
By mapping frequent stakeholder queries against the new architecture, we identified missing parameters, such as granular user click data, well before they impacted operations, securing a high-priority fix on the core data team's development roadmap.
Established a scalable self-service analytics framework
Shifted the program's long-term trajectory away from a continuous, centralized dashboard rebuild model and toward an autonomous self-service platform, allowing individual business units to generate custom reporting independently.
Results that reflect the work behind them.
3 days
Spent monthly by one team manually compiling platform health data into a scorecard before automating the process.
5
Retail business areas impacted by the transformation initiative
1
Consolidated tool deployed to replace a fragmented network of legacy reporting structures
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