Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation -- Internal Processes Perspective

Data valuation and monetisation are emerging as central challenges in data-driven economies, yet no unified framework exists to measure or manage data value across organisational contexts. This paper presents a systematic literature review of metrics and key performance indicators (KPIs) relevant to data valuation and monetisation, focusing on the Internal Processes Perspective of the Balanced Scorecard (BSC). As part of a broader effort to explore all four BSC perspectives, we identify, categorise, and interrelate hundreds of metrics within a comprehensive taxonomy structured around three core clusters: Data Quality, Governance & Compliance, and Operational Efficiency. The taxonomy consolidates overlapping definitions, clarifies conceptual dependencies, and links technical, organisational, and regulatory indicators that underpin data value creation. By integrating these dimensions, it provides a foundation for the development of standardised and evidence-based valuation frameworks. Beyond its theoretical contribution, the taxonomy supports ongoing practical applications in decision-support systems and data valuation models, advancing the broader goal of establishing a coherent, dynamic approach to assessing and monetising data across industries.

💡 Research Summary

The paper addresses the pressing need for a unified framework to measure and manage data value in today’s data‑driven economies. While the macro‑level importance of data is widely acknowledged, organizations still lack consistent methods to translate data assets into quantifiable financial or operational impact. To fill this gap, the authors conduct a systematic literature review (SLR) of over 150 scholarly and industry sources, focusing specifically on metrics and key performance indicators (KPIs) that pertain to the Internal Processes perspective of the Balanced Scorecard (BSC).

The review identifies hundreds of disparate metrics, many of which overlap or are defined inconsistently across studies. To bring order to this landscape, the authors propose a three‑cluster taxonomy: (1) Data Quality, (2) Governance & Compliance, and (3) Operational Efficiency. Each cluster groups related attributes—such as accuracy, completeness, and timeliness for quality; security, privacy, regulatory adherence, and metadata management for governance; and pipeline latency, storage cost, system availability, and data reuse rates for efficiency.

Building on the BSC, the taxonomy is organized into a three‑level hierarchy that aligns with organizational decision‑making layers: (i) high‑level strategic KPIs for executives (e.g., data‑driven revenue growth, data investment ROI), (ii) mid‑level departmental KPIs for data engineering, governance, and analytics teams (e.g., percentage improvement in data quality, zero compliance violations), and (iii) low‑level operational metrics for specialists (e.g., data ingestion success rate, error‑log frequency). This structure enables a clear mapping from day‑to‑day operational performance to strategic objectives, ensuring that data‑related activities are monitored not in isolation but as contributors to overall value creation.

The paper also situates its taxonomy within existing data‑quality frameworks (e.g., Total Data Quality Management) and demonstrates how the proposed metrics can be integrated into decision‑support systems and data valuation models. By linking technical, organisational, and regulatory dimensions, the taxonomy offers a foundation for developing standardized, evidence‑based data valuation methodologies.

Contributions are twofold: (1) an extensive, up‑to‑date literature synthesis of data‑valuation and monetisation metrics from the internal processes viewpoint, and (2) a novel, BSC‑aligned taxonomy that categorises and hierarchises these metrics for practical use. The authors acknowledge limitations, notably the exclusive focus on the internal processes perspective, the absence of weighted scoring or prioritisation rules for the metrics, and the lack of empirical validation through real‑world case studies. They suggest future work should extend the taxonomy to the remaining BSC perspectives (Financial, Customer, Learning & Growth), explore inter‑metric dependencies, and test the framework in industry settings to refine weighting schemes and demonstrate tangible benefits.

Overall, the study provides a comprehensive, structured lens for organisations seeking to assess, communicate, and monetize the value of their data assets, laying groundwork for more rigorous and comparable data‑valuation practices across sectors.