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Twenty Platform Ideas. One Framework. One Better Decision.
The problem was never having ideas. It was choosing the right one.
The team identified more than 20 opportunities to improve the platform. The challenge was not identifying ideas but deciding which ones should be prioritized. The Data team focused on efficiency and reducing manual effort; the Product team also weighed user needs, business value and development effort. Both were right, with no shared basis for deciding. A ranking framework across five areas replaced opinion with evidence: business and user value, client impact, effort relative to value, security and data quality, and functional quality and usability. Of three candidates, ThemeLens stood out, combining client value, market relevance and broader business potential, and the Product team built it. The feature that saves the most internal effort is not necessarily the feature that creates the most value.
As the platform evolved, we identified more than 20 opportunities to improve existing functionality and introduce new features. The challenge was not identifying ideas but deciding which ones should be prioritized.
Feature prioritization: Having ideas was easy. Choosing the right one was harder.
Taking a feature from an idea to production involved several practical challenges. Engineering resources were often committed to other priorities, some enhancements involved development or deployment costs, and the Data and Product teams sometimes had different views on priorities. While the Data team naturally focused on efficiency and reducing manual effort, Product team also considered broader user needs, business value, development effort, and other factors. Both were right. That was exactly the problem, two valid perspectives, no shared basis for deciding between them.
A feature prioritization framework: Prioritization works best when opinion gives way to evidence.
We therefore developed a ranking framework around five areas. This allowed us to look beyond internal efficiency and consider the value each feature could create for users.
- Business and user value
- Client impact
- Effort relative to value
- Security and data quality
- Functional quality and usability
Three features emerged as strong candidates.
The first, InsightAssist, was an LLM-based feature designed to respond to client queries using data available through the user interface. It had clear potential to reduce manual intervention and provide faster responses. However, the accuracy of responses was not yet consistently at the required level, highlighting that speed and automation cannot replace reliable information.
The second, BulkFix, addressed a recurring operational challenge. Dividend data intended for income share classes can sometimes be incorrectly loaded against accumulation share classes. Correcting large volumes of such records manually can be time-consuming, making bulk deletion a valuable efficiency improvement.
The third, ThemeLens, emerged from looking beyond our immediate operational requirements. Through our research, we identified growing interest in thematic areas such as Crypto and ESG funds. ThemeLens emerged from looking beyond immediate operational requirements — growing market interest in thematic areas like Crypto and ESG funds. Thematic filtering could provide more targeted insights than traditional sector or industry classifications, helping users focus on specific areas of interest.
| Feature | What it does | What the framework showed |
|---|---|---|
| InsightAssist | LLM-based responses to client queries using data available through the user interface | Automation potential but accuracy limitations |
| BulkFix | Bulk deletion of dividend data incorrectly loaded against accumulation share classes | Operational efficiency |
| ThemeLens | Thematic filtering, such as Crypto and ESG funds | Client value, market relevance and broader business potential: built by the Product team |
Fund data platform features: The strongest business case is where user value meets market demand.
When we applied the framework, ThemeLens stood out. InsightAssist had automation potential but accuracy limitations. BulkFix offered operational efficiency. ThemeLens combined client value, market relevance, and broader business potential and the Product team built it.
The feature that saves the most internal effort is not necessarily the feature that creates the most value.
Moving from “which feature should we build” to “which enhancement creates the greatest overall value” that shift in question was the real outcome of the exercise.
Opinion gives way to evidence.
Which enhancement creates the greatest overall value?
If your platform has more ideas than capacity, we can help you weigh them against user value, client impact and effort before anyone commits engineering time.
Talk to our teamFrequently asked questions
What criteria can a feature prioritization framework use?
This framework ranked features across five areas: business and user value, client impact, effort relative to value, security and data quality, and functional quality and usability. It allowed the team to look beyond internal efficiency and consider the value each feature could create for users.
Why do Data and Product teams disagree on feature priorities?
The Data team naturally focused on efficiency and reducing manual effort, while the Product team also considered broader user needs, business value, development effort, and other factors. Both were right. The problem was two valid perspectives with no shared basis for deciding between them.
Why did the LLM-based query feature not come out on top?
InsightAssist, an LLM-based feature designed to respond to client queries using data available through the user interface, had clear potential to reduce manual intervention and provide faster responses. However, the accuracy of responses was not yet consistently at the required level. Speed and automation cannot replace reliable information.
Why can thematic filtering be more useful than sector classifications?
Thematic filtering could provide more targeted insights than traditional sector or industry classifications, helping users focus on specific areas of interest. Research identified growing interest in thematic areas such as Crypto and ESG funds.
What is the main lesson for prioritizing platform features?
The feature that saves the most internal effort is not necessarily the feature that creates the most value. The real outcome was a shift in the question, from which feature should we build to which enhancement creates the greatest overall value.
