Introducing the Insurance Data & Analytics Maturity Model


Introducing the Insurance Data and Analytics Maturity ModelData is an arms race. Competitive pressures, internal demands, and an ever-growing pool of data are driving insurers to assess their data and analytics capabilities. Traditional approaches to data management are running up against the needs of advanced analytics, artificial intelligence (AI), and machine learning. Data privacy concerns and regulations place additional hurdles on data resource management.

Aite-Novarica Group’s new report, Establishing and Sustaining Data Mastery, introduces the Insurance Data & Analytics Maturity Model (iDAMM).

Summary of the Insurance Data & Analytics Maturity Model

This maturity model enables insurers to review where they currently are in their data and analytics journey, define a target state, and plan investments and initiatives to bridge the gaps between them. iDAMM allows insurers to assess their data organization and capabilities across seven dimensions and 21 subdimensions, including leadership and organization, data governance, and architecture and technology management.

The model uses three stages of maturity: Traditional, Evolving, and Transforming. Insurers are likely to have different levels of maturity across different model elements. Moving into a more mature stage is a function of organizational and technological capability, not duration. Like transformations in other parts of the insurance carrier, data and analytics innovation requires enabling technology, organizational change, and executive sponsorship.

Symptoms of Low Data Maturity

Most insurers are falling short of their data objectives despite increasing their focus and investments in this domain.

Organizations that suffer from data immaturity experience symptoms such as data access challenges, low business intelligence and analytics productivity, and spending significant effort to reconcile reports. These organizations may also be noncompliant with data privacy regulations due to ignorance of applicable regulations, incomplete data governance, or gaps in data access controls. Data-immature organizations also suffer from consistent data quality issues and fail to receive the full value of data science efforts and third-party data insights.

The Importance of Culture

Sustaining data mastery requires the insurer to establish and maintain a data culture. Data cultures are those in which data is fully democratized, in which data literacy is high, and that allow all tactical and strategic decision-making to be largely data-driven (but still informed by intuition). Further, all users value data highly and act as corporate data stewards in maintaining high data quality and protection.

Building a data culture is challenging. Insurers that have achieved data mastery will have a culture grounded in data and analytics that can survive leadership changes and shifts in business focus. This means that everyone cares about data quality, as everyone understands innately the value of information and insights. Strategic and tactical decisions are heavily influenced by signals in the data, though intuition developed through industry experience also plays a role.

For more information on data mastery and the iDAMM, access the full report here.