The strategic value of data for insurance companies
María Jesus Pérez Fuentes
We participated in the Data Driven Day, where the focus was on the data-driven transformation taking place in relation to insurance, energy, and industry.
From a competitive advantage to a corporate obligation
Fundamental pillars of a good data governance model
- Data Management Processes: to allow definition of an organization’s internal workflows.
- A Common Language: to be applied to data within an organization; for example, by producing a glossary of terms shared by all departments.
- Efficient organization: through creation of new teams, data offices, etc., to encourage companies to implement data culture by generating value and continual improvement.
- Data quality: it must be ensured that decisions will not be based on low-quality data, and it is therefore important to define standards, parameters, and metrics that will help safeguard and monitor data quality within an organization.
- Technology: to allow construction of architectures for reference data, along with technological solutions, information models, and infrastructure criteria that will enhance the technical implementation of data.
- Security: this must be embedded within an organization’s culture and applied to all data management processes, to ensure that data is always used correctly.
Maribel Solanas, Chief Data Officer at MAPFRE, explained how the process of change is being implemented gradually at the company, with the existing internal experience and knowledge being understood as fundamental, and with assessment of the contributions they can make to the current situation.
Internal talent is key, and this is why we emphasize specific training, while also recognizing the need to supplement it with external training when necessary. The dynamics of teamwork are being transformed to incorporate aspects that are more traditional as well as those that are more disruptive, combining the strengths of rigorous business knowledge with new approaches, always with the aim of advancing the company’s strategic objectives.
“One of the main challenges in transforming insurance companies into data-driven companies is understanding the complementary nature of everything we do. This means combining the experience, skills, and knowledge we already have within our company with the capabilities and disciplines derived from new ways of working, which are much more collaborative and flexible”, she explained.
To make the benefits of improved data governance within a company tangible, it is essential to establish some measurement principles that can reveal the advantages of working without silos, so that the various processes can reinforce each other.
MOI allows new case studies and pilot projects to run in parallel with other medium-term and long-term initiatives, because combining these two facets is what allows tangible results to be achieved. This approach also maximizes the value of investments made in developing capabilities, prioritization, and implementing other initiatives, by taking into account their beneficial impact on the company.
The use of data science by insurance companies has become more dynamic in the post-pandemic context, and it is therefore important to have a privacy function to construct a trustworthy data model with a customer-centered perspective. Elements such as privacy by design and privacy engineering (k-anonymity and differential privacy) must be seen as fundamental aspects of the transformation into a data-driven company.
“The major challenge we face is ensuring that the large volumes of external and internal data we manage are collected in a transparent manner, and processed in compliance with ethical guidelines that take into account the rights and liberties of all stakeholders”, explained María Luisa Fernández, a lawyer at MAPFRE España with expertise in privacy and data protection.
These fields can be seen as benchmarks for the changes that are transforming insurance companies, where one of the main goals is to create more business through the continual data-based learning derived from interactions with customers.
Diego Bodas, Manager of Advanced Analytics at MAPFRE España, described new types of customers “who demand absolute immediacy. This represents a challenge for the insurance industry, because the complexity of the business has traditionally restricted our ability to respond so quickly”.
Streamlining customer service and eliminating perceptions of bureaucratic internal processes is one of MAPFRE’s main objectives, and it is also one where significant progress has been made in recent years, for example, in relation to digital verification procedures.