The Importance of Using BI in the Insurance Industry for Decision-Making
This article is the result of a collaboration with Ariel Rojas Vasquez, Software Developer at Flux IT. Enjoy the read!
In an increasingly complex and competitive environment, the insurance industry faces the need to make faster and better-informed decisions. This is where business intelligence (BI) becomes an essential ally and tool. This article explores the importance of BI in the insurance industry and how it can transform decision-making.
The Role of BI in Insurance
The insurance industry generates a significant amount of data in its daily operations. What sets it apart from other industries is its use of advanced statistical techniques in areas such as pricing, calculating the number of expected accidents, and quantifying potential claims, among others. However, the key lies in how this data is used. A BI-driven approach is essential for organizing the data life cycle, covering operational, tactical, and strategic aspects. This provides a reference framework that facilitates the creation of a data model aligned with the organization’s needs. This approach not only enables more informed decision-making but also unlocks a range of additional benefits. For example, one of the main advantages of BI is its ability to deliver insights that support data-driven decisions. Furthermore, BI helps identify operational inefficiencies, uncovering bottlenecks and improvement opportunities. Once these issues are addressed, processes are enhanced and the customer’s experience improved. To conclude, the ability to analyze customer data allows insurers to segment their markets more effectively, thus developing customized products and services that boost retention and open new cross-selling opportunities.
Implementation Challenges
Despite its benefits, implementing BI comes with its challenges. During the adoption process, the following obstacles often arise:
1. Data Integration: The presence of diverse data sources poses a challenge for any project, since this often leads to the use of various access tools, thus making it difficult to standardize a single method for data extraction.
2. Data Quality: The accuracy and usefulness of data analysis depend on the quality of the data used. Incomplete, inaccurate, or outdated data can lead to undesirable outcomes in decision-making. It is therefore essential to implement processes that ensure data quality, including data processing and cleansing.
3. Organizational Culture: This type of approach brings about a cultural shift within organizations, steering them towards a data-driven focus. Such a change can sometimes encounter resistance. Therefore, it is essential to foster a mindset that values informed decision-making and teamwork.
4. Data Governance: Implementing policies, processes, and structures to ensure effective and responsible data management is essential. This becomes particularly important for organizations that rely heavily on data for decision-making.
A Process
With a Flux IT client, we have been working on a customer service tool where the implementation of BI is critical to evaluating the company’s performance in customer care, aiming to deliver a more agile and efficient service. The goal is to implement BI processes that enable descriptive and predictive analysis of SLA (Service Level Agreement) performance, allowing us to identify areas for improvement and anticipate potential breaches.
1. Descriptive Analysis: Understanding the Current SLA
The first step was to perform a descriptive analysis on the tool’s existing historical data. Here, we focused on identifying and evaluating the statistically significant variables in the SLA calculation. The goal is to understand data features and patterns, while using segmentation criteria to establish a robust model for SLA estimation.
Identification of Key Variables
During this phase, a variety of data is analyzed, ranging from response times to the nature of incidents. Some of the key variables identified include:
● Incident classification: Different classifications can have varying resolution times.
● Time and day of the week: Incident traffic patterns can fluctuate depending on the time and day.
Pattern Establishment
Through statistical and visualization techniques, patterns can be identified which allow us to establish a preliminary model for SLA estimation. This not only provides a deeper understanding of current response times but also generates an output to be used in the next phase.
2. Predictive Analysis — Looking Ahead…
The second step involves applying machine learning models that can predict the SLA for new tickets based on current conditions. This phase revolutionizes the traditional management approach by enabling more accurate estimates through model learning.
External Data Integration
A key aspect for predictive analysis is the ability to enrich models with external data. By incorporating these factors, a more comprehensive and anticipatory understanding of expected behavior can be achieved.
3. Resource Allocation Modeling — Meeting the SLA
Finally, the third step involves developing a model that allows for the optimal distribution of staff across different days and hours, ensuring that the established SLA is met. This model provides guidance on how to appropriately assign personnel based on projected incident volumes.
Visualization and Planning Tools
Using visualization and analysis tools, we can graphically represent resource needs at different times, making planning and staff allocation easier. This not only improves operational efficiency but also enhances customer satisfaction by ensuring optimal response times.
Conclusion
Integrating business intelligence into incident management provides the opportunity to optimize processes and enhance the overall experience. Through descriptive analysis and a predictive approach, we can anticipate and effectively manage incidents, thus ensuring compliance with the established SLAs. With these steps, we not only improve internal efficiency but also enable organizations to respond proactively to changing needs. Investing in BI is not just a technical enhancement, but a long-term strategy that fosters a culture of data-driven decision-making.
