Empower Innovation in Insurance with MongoDB and Informatica

For insurance companies, determining the right technology investments can be difficult, especially in today's climate where technology options are abundant but their future is uncertain. As is the case with many large insurers, there is a need to consolidate complex and overlapping technology portfolios. At the same time, insurers want to make strategic, future-proof investments to maximize their IT expenditures.

What does the future hold, however? Enter scenario planning. Using the art of scenario planning, we can find some constants in a sea of uncertain variables, and we can more wisely steer the organization when it comes to technology choices. Consider the following scenarios:

• Regulatory disruption: A sudden regulatory change forces re-evaluation of an entire market or offering.

• Market disruption: Vendor and industry alliances and partnerships create disruption and opportunity.

• Tech disruption: A new CTO directs a shift in the organization's cloud and AI investments, aligning with a revised business strategy.

What if you knew that one of these three scenarios was going to play itself out in your company but weren’t sure which one? How would you invest now to prepare for one of the three?

At the same time that insurers are grappling with technology choices, they’re also facing clashing priorities:

• Running the enterprise: supporting business imperatives and maintaining health and security of systems.

• Innovating with AI: maintaining a competitive position by investing in AI technologies.

• Optimizing spend: minimizing technology sprawl, technical debt, and maximizing business outcomes.

Data modernization

What is the common thread among all these plausible future scenarios? How can insurers apply scenario planning principles while bringing diverging forces into alignment? There is one constant in each scenario, and that’s the organization’s data—if it’s hard to work with, any future scenario will be burdened by this fact.

One of the most critical strategic investments an organization can make is to ensure data is easy to work with. Today, we refer to this as data modernization, which involves removing the friction that manifests itself in data processing, ensuring data is current, secure, and adaptable. For developers, who are closest to the data, this means enabling them with a seamless and fully integrated developer data platform along with a flexible data model.

In the past, data models and databases would remain unchanged for long periods. Today, this approach is outdated. Consolidation creates a data model problem, resulting in a portfolio with relational, hierarchical, and file-based data models—or, worst of all, a combination of all three. Add to this the increased complexity that comes with relational models, including supertype-subtype conditional joins and numerous data objects, and you can see how organizations wind up with a patchwork of data models and overly complicated data architecture.

A document database, like MongoDB Atlas, stores data in documents and is often referred to as a non-relational (or NoSQL) database. The document model offers a variety of advantages and specifically excels in data consolidation and agility:

• Serves as the superset of all other data model types (relational, hierarchical, file-based, etc.)

• Consolidates data assets into elegant single-views, capable of accommodating any data structure, format, or source

• Supports agile development, allowing for quick incorporation of new and existing data

• Eliminates the lengthy change cycles associated with rigid, single-schema relational approaches

• Makes data easier to work with, promoting faster application development

By adopting the document model, insurers can streamline their data operations, making their technology investments more efficient and future-proof.

The challenges of making data easier to work with include data quality. One significant hurdle insurers continue to face is the lack of a unified view of customers, products, and suppliers across various applications and regions. Data is often scattered across multiple systems and sources, leading to discrepancies and fragmented information. Even with centralized data, inconsistencies may persist, hindering the creation of a single, reliable record. For insurers to drive better reporting, analytics, and AI, there's a need for a shared data source that is accurate, complete, and up-to-date. Centralized data is not enough; it must be managed, reconciled, standardized, cleansed, and enriched to maintain its integrity for decision-making. Mastering data management across countless applications and sources is complex and time-consuming. Success in master data management (MDM) requires business commitment and a suite of tools for data profiling, quality, and integration. Aligning these tools with business use cases is essential to extract the full value from MDM solutions, although the process can be lengthy.

Informatica’s MDM solution and MongoDB

Informatica’s MDM solution has been developed to answer the key questions organizations face when working with their customer data:

• “How do I get a 360-degree view of my customer, partner and & supplier data?”

• “How do I make sure that my data is of the highest quality?”

The Informatica MDM platform helps ensure that organizations around the world can confidently use their data and make business decisions based on it. Informatica’s entire MDM solution is built on MongoDB Atlas, including its AI engine, Claire.

Informatica MDM solves the following challenges:

• Consolidates data from overlapping and conflicting data sources.

• Identifies data quality issues and cleanses data.

• Provides governance and traceability of data to ensure transparency and trust.

Insurance companies typically have several claim systems that they’ve amassed over the years through acquisitions, with each one containing customer data. The ability to relate that data together and ensure it’s of the highest quality enables insurers to overcome data challenges.

MDM capabilities are essential for insurers who want to make informed decisions based on accurate and complete data. Below are some of the different use cases for MDM:

• Modernize legacy systems and processes (e.g. claims or underwriting) by effectively collecting, storing, organizing, and maintaining critical data

• Improve data security and improve fraud detection and prevention

• Effective customer data management for omni-channel engagement and cross- or up-sell

• Data management for compliance, avoiding or predicting in advance any possible regulatory issues

Taking the next step

For insurance companies navigating the complexities of modern technology and data management, MDM combined with powerful tools like MongoDB and Informatica provide a strategic advantage. As insurers face an uncertain future with potential regulatory, market, and technological disruptions, investing in a robust data infrastructure becomes essential. MDM ensures that insurers can consolidate and cleanse their data, enabling accurate, trustworthy insights for decision-making.

By embracing data modernization and the flexibility of document databases like MongoDB, insurers can future-proof their operations, streamline their technology portfolios, and remain agile in an ever-changing landscape. Informatica’s MDM solution, underpinned by MongoDB Atlas, offers the tools needed to master data across disparate systems, ensuring high-quality, integrated data that drives better reporting, analytics, and AI capabilities.