You're an insurance executive who needs to leverage big data. What should you know first?

As an insurance executive looking to leverage big data, it's crucial to first understand the specific business objectives and key performance indicators (KPIs) you aim to improve through data analytics. Identify areas such as risk assessment, customer segmentation, fraud detection, and operational efficiency where big data can make a significant impact. Additionally, ensure you have a clear strategy for data collection, storage, processing, and analysis while prioritizing data security and compliance with regulatory standards. Finally, foster a data-driven culture within your organization to effectively implement insights gained from big data analytics into decision-making processes.

An insurance executive is a business or marketting person. He/She has a mere knowledge of big data processing or managing. But as a business person he knows the areas and processes to be implemented for organizations grouth. He has to identify the data scope that these data can implact the business areas. Considering the scope define the goal. Share the scope and goal with the analyst and ask for analysis of the data. And finally use the analytical report with organizations authority which will accelerate the decision making of those business areas.

- Defining objectives - Identify whether there are the right people in the right positions; - Be aware that every data project involves culture (seeking or strengthening it); - An excellent strategy and resilience (because a "punch" can come suddenly); Thus, publicising to the company and especially to the Board what the Big Data project is, what the COI x ROI ratio is, how long it will take, what the quickwins are and who is responsible, can remove barriers, avoid unnecessary demands and prevent "urgent" requests that could jeopardise the progress of the project/strategy.

Es importante tener claros nuestros objetivos organizacionales con el fin de poder dar el enfoque necesario y así cumplir con las expectativas que vamos a tener con Big Data y no dar falsas esperanzas con el esfuerzo a realizar.

Em alguns casos que já vivenciei, o investimentos em estrutura de big data, clusters e ferramentas foi crucial para estratégia de relacionamento com clientes, pois através dessas solu??es foi possível melhorar significativamente indicadores time to market, relacionamento e campanhas por exemplo. Mas isso só foi possível quando os executivos obtiveram conhecimento e definiram objetivos claros sobre retorno de valor para investimento nesse tipo de solu??o.

Implement an automated data cleaning tool that scans for discrepancies, duplicates, and incomplete entries across your database. For instance, a mid-sized insurer used such a tool to enhance the quality of its customer data, resulting in an increase in accuracy for targeted marketing campaigns. This underscores the importance of having clean, reliable data as the foundation for any big data project, ensuring compliance and enhancing decision-making accuracy.

Dados de alta qualidade s?o a espinha dorsal de qualquer análise de Big Data bem-sucedida. Portanto, é crucial avaliar a qualidade dos dados à sua disposi??o. Isso envolve verificar a precis?o, a completude e a relevancia dos dados que você planeja analisar. Dados imprecisos ou incompletos podem levar a conclus?es err?neas e afetar negativamente as decis?es de negócios. Por isso, antes de prosseguir, garanta que seus dados sejam confiáveis e representativos.

Data sourcing from golden source is another important piece of cake here. Every type of data gets generated or transformed in the trusted systems per type of data . So it’s important to get the cdo acknowledge the data sourcing from the trusted or golden or the current best system . Hence the data quality we can check is if the data is consistent complete and accurate what we receive , but when the data is received with poor quality should be raised to the trusted sources where are sourcing from . Hence it becomes the trusted sources systems responsibility to fix it or fix the business process to get the good quality data into the trusted systems .

Example: Partner with a cloud-based analytics platform, to enable real-time data analysis and scalability, and allow the company to adapt quickly to market changes and customer needs.

Cuando queremos tener éxito en los trabajos de Big Data es importante que nuestros socios tengan la experiencia necesaria en el manejo de las herramientas que debemos implementar tomando en cuenta que ningún proyecto es igual a otro pueden existir muchas similitudes pero todas las organizaciones funcionan diferente por lo que es importan el conocimiento de las herramientas necesarias para la implementación

Escolhe bons parceiros de dados para construir a plataforma de dados assim como buros de créditos para ter mais assertividade na qualidade de saída dos modelos

Yes selecting tools should be able to have high capability to show the Auto lineage as it’s hard for us to maintain or capture every transformation we are doing if we are working in big scale. It should be capable of something like data profiling hence it helps the data analysts instead they spend hours and days to understand the data quality . It should be able to run the big data faster cheaper with less maintenance like low code no code etc., ex Hadoop is taking more than 20 hours to run our huge transaction data .

Example: Utilized A/B testing to refine customer segmentation for your health insurance products. By analyzing big data on customer behavior and demographics, you could identify unique segments that respond differently to marketing strategies, and so increase your conversion rates. This iterative approach to using big data can not only improve marketing effectiveness but also enhance customer understanding and service personalization.

A explora??o do Big Data é um processo de aprendizado contínuo. N?o tenha medo de experimentar diferentes abordagens e técnicas de análise. Algumas estratégias podem n?o trazer os resultados esperados inicialmente, mas cada experimento oferece valiosas li??es que podem ser aplicadas em futuras iniciativas. Mantenha uma mentalidade aberta e esteja pronto para ajustar suas estratégias conforme necessário.

Si la organización tiene conciencia de que con Big Data podemos obtener ventajas competitivas, es importante que todos se vean comprometidos en los productos a obtener y educarnos para sacarle el provecho máximo a los proyectos que vamos a ejecutar.

Finalmente, é fundamental educar e engajar sua equipe e stakeholders sobre os benefícios e desafios do Big Data. A transforma??o digital bem-sucedida requer n?o apenas a ado??o de novas tecnologias, mas também uma mudan?a cultural. Educar sua equipe sobre as potencialidades do Big Data e engajá-los no processo de análise pode impulsionar a inova??o e garantir o alinhamento dos objetivos de negócios com as estratégias de dados.

Em conclus?o, o aproveitamento do Big Data no setor de seguros oferece oportunidades sem precedentes para inova??o e otimiza??o. Seguindo esses passos, os executivos de seguros podem posicionar suas empresas na vanguarda da transforma??o digital, garantindo que estejam bem equipados para enfrentar os desafios e aproveitar as oportunidades que o futuro reserva.