Case
From Unclear Data Entry to Decision-Ready KPIs
Project Management | Public Sector Organization
A Danish public-sector organization needed to establish valid and transparent KPIs to meet new regulatory requirements, but inconsistent data entry in a newly implemented IT system threatened the reliability of management reporting.
Business Learning structured the improvement project, clarified the underlying data architecture, and translated the challenges into decision-ready scenarios, including an assessment of an AI-supported solution.
The organization is a mid-sized Danish public-sector entity responsible for supporting government authorities in fulfilling obligations defined by Danish law.
Challenge
The company required clearer and more reliable KPIs across the organization. A new IT system had been implemented, but the quality and consistency of data entry varied significantly.
This created several risks:
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KPIs based on inconsistent or invalid data
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Reduced trust in management reporting
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Inefficient manual data processes and confusion
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Uncertainty about whether AI-based solutions should be introduced.
Management needed clarity before setting direction with a new ambition level and committing to further technology investments.
Business Learning was selected due to its combined strengths in project governance, KPI design, and ability to create clear recommendations for management to take decisions in complex matters.
Approach
The work started by clarifying the project scope, mandate, stakeholders, and decision authority to ensure a clear and focused start. Business Learning reviewed the existing data entry setup and identified where data quality issues occurred, including how data was entered, how it fed into KPIs, and where errors or risks affected data validity.
Based on this analysis, several solution options were defined and compared: continuing manual data entry with clearer guidelines, improving structured digital input, and introducing an AI-supported chatbot for verbal data entry. Each option was assessed based on usability, risks and compliance, technical feasibility, cost, and impact on KPI quality. This gave management a clear view of trade-offs before making a strategic technology decision.
Business Learning also supported the project manager with a focused kick-off, clear agendas, and simple planning tools to keep progress fast and well controlled, giving management a clear basis for decision-making.
Impact
The project enabled a fast and well-structured start to the improvement initiative, with a clear scope and the right stakeholders involved from the outset. The organization gained a shared, practical understanding of how data needed to be entered and structured to support reliable KPIs.
By clearly describing and comparing different data entry options, management could see the trade-offs between usability, risk, cost, and data quality, including the implications of introducing AI-based solutions. As a result, management gained confidence in the chosen direction and a solid basis for setting the KPI ambition level for the coming year.
On the operational level, the project resulted in:
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Clearly documented data architecture
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Defined roles and responsibilities in data entry
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Stronger stakeholder alignment
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Faster and more reliable project start-up.
The organization moved from ambiguity and fragmented data practices to structured clarity and informed decision-making.
This case may be relevant for you, if you:
- Must report KPIs to public authorities
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Struggle with data quality in new IT systems
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Need clarity before investing in AI solutions
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Want structured project start-ups with clear governance
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Require management-ready recommendations in complex environments.