Enterprise Data Management

1. Data Management & Analytics Services WHITEPAPER Enterprise Data Management Leaner, Agile Data Ecosystem in the Insurance industry

2. Enterprise Data Management - Leaner, Agile Data Ecosystem in the Insurance Industry Worldwide spending on enterprise data management initiatives continues unabated and is expected to accelerate in the future. The master data management (MDM) market size is expected to scale from USD 16.7billion in 2022 to almost USD 34.5billion by 2027 at a compound annual growth rate (CAGR) of 15.7%. The insurance industry, too, is undergoing a transition from being policy centric to being more customer-centric. The rising demand in the competitive market and the increasing regulatory pressure are pushing the sector through a stir and forcing it to drive innovation and governance whilst keeping the costs minimal. This is not easy when most of the data (customer, policy and claims) are housed separately in the outdated legacy systems. MDM in the Insurance Sector The insurance industry, like other sectors, is facing its own unique set of challenges arising due to data inconsistencies. The data swamp that we are witnessing is resulting in ineffective customer strategies, internal and external reporting issues, and higher costs of operations. Figure 1: Data Swamp in the Insurance Industry The data swamp in the insurance industry leads to multiple issues Customer acquisition Customer interactions Corporate Actions Inability to aggregate multiple data sources for effective marketing compaigns Inability to harness the full potential of all communication channels Disjointed Technology Infrastructure Lack of Enterprise Data Ecosystem The data Swamp Lack of Data Governance Changing Regulatory Landscape Customer retention Operational cost High data cost and inefficient business processes Ineffective customer segmentation for customised offerings Manual-intensive Data Processes Lack of Golden Source/Data Lake Client risk accessment Regulatory reporting Fraud detection Financial Reporting Incorrect categorisation of customer risk due to data reliability issues Risk of inadenquate & Inaccurate reporting Inability to use predictive models effectively due to bad data Consolidating data from various LOBs 2

3. Enterprise Data Management - Leaner, Agile Data Ecosystem in the Insurance Industry The insurance industry is reliant on accurate data for effective product design, risk mitigation, and investment decisions. Due to the siloed nature of the insurer’s application environment, MDM ensures consistency across platforms wherever accuracy is paramount. The rising demand for MDM in the insurance industry will be imperative to operations and can lead to gaining a major competitive advantage. MDM offers a data management framework that assists in defining the disciplines, processes, and technologies required to ensure the accuracy, integrity as well as consistency of data sources. Beyond operational data use, MDM is also growing in prominence to keep up with a proliferation of analytics and external demands. Ingredients of a Successful MDM Strategy The most critical ingredient for a successful MDM strategy and execution is the alignment of goals between the business, data, and technology teams. Shared accountability between stakeholders should be agreed upon, and long multi-year programs should be broken down into small outcome-oriented projects. By focusing on the quality and consistency of data in a single source of truth (SSOT) or master data source, insurance firms can extend their data validation across multiple departments and services without dramatically increasing the data management burden. Figure 2. MDM Components Synchronization Distribution Acquisition Master Data Repository Hierarchies Business Services Data Quality \DQ Control Master Data Services Data Consolidation Business Rules Access Control Data Intake Interface Metadata Master Reference Data Interoperability Application Master Data Models MDM Architecture Master Data Lifecycle Operations Management/Data Stewardship 3

4. Enterprise Data Management - Leaner, Agile Data Ecosystem in the Insurance Industry Key Components of Designing an MDM Strategy Defining the business outcome For the successful design and implementation, along with the adoption of any MDM strategy, defining the outcome and value is paramount for every business. The outcomes can be defined as operational or analytical in the following ways: • Operational master data management strives to enhance the data quality and consistency of the enterprise data that can be shared with multiple business applications. The single source of truth can then be employed through underwriting, claims or re-insurance processing. • Analytical master data management is more focused on improving decision-making by cleansing, matching, merging and aggregating critical enterprise data elements in advanced analytics and business intelligence functionality. Educating the stakeholders The most critical task is educating the stakeholders, without which the entire effort is ineffectual. A misaligned team, together with an unconvinced consumer group that lacks clarity regarding the goals and mission of the MDM strategy, will result in major challenges ahead. Defining data standards Setting precise data standards or frameworks is one of the most challenging tasks of MDM implementation. The data standard set by the organisation for their master data should be in accordance with all the data types in the company. The set standard should be adaptable to the data across different departments of the organisation. If not planned well, data standardisation can come across as a cumbersome process. Selecting the MDM software and master data model Selecting the right master data model makes a huge difference in execution and maintenance. When deciding upon the MDM software, businesses need to consider the components that meet the needs from the system’s perspective and are adaptive to the ever-changing business requirements. It is also extremely crucial to determine whether the solution is to be hosted on-prem or on cloud. Key steps involve - • Creating a data model • Defining business rules • Defining data validation controls • Specifying roles and security measures On-Prem vs Cloud Enterprise spending on the public cloud within addressable market segments is expected to overtake spending on traditional computing models in 2025. This rise in the adoption of distributed cloud holds the potential to accelerate the cloud shift, as it brings public cloud services into domains that were primarily non-cloud. Enterprises are transitioning to the cloud within the application software, infrastructure software and business process services, along with the system infrastructure markets. Source: Gartner 4

5. Enterprise Data Management - Leaner, Agile Data Ecosystem in the Insurance Industry Figure 3. Sizing cloud shift worldwide, 2019–2025 Total Revenue Growth in Revenue 138 1,000 140 917 124 868 838 110 805 775 779 744 Total Revenue($B) 710 705 94 655 80 544 66 500 70 451 370 304 33 33 31 31 30 5 0 0 2019- 2020 2020- 2021 2021- 2022 2022- 2023 2023- 2024 2024- 2025 2023 2022 2025 2024 2021 2020 2019 Traditional Cloud Cloud Traditional Source: Gartner Data integration Integrating MDM into other data applications can be a laborious task. Data transfer from one application to another can lead to errors. Furthermore, a few fields may transfer seamlessly during the integration process whilst others may not. The key measures include: • Defining data integration policies • Managing integration with internal, external as well as cloud-based applications Establishing suitable data governance Despite the introduction of definite models and standards, the MDM implementation process is considered complicated. Whilst strong guidelines and rules can assist in addressing the complexity of the master data, data governance is considered a vital element without which organisations cannot gain a clear overview of the data operations. Data governance should not be perceived as a one-time data cleansing exercise. With a structured data governance process, organisations can identify, measure, capture and rectify data quality issues in the source system. Data stewardship Establishing data stewardship is crucial for businesses to maintain the quality of their data. A bad data set will not only hamper the consolidation of master data but also create long-term data management problems. In the absence of efficient data stewardship, the established MDM implementation framework is likely to suffer. The key actions to be taken include: • Organising data stewardship tasks based on roles • Managing tasks connected to the master data • Authoring master data 5

6. Enterprise Data Management - Leaner, Agile Data Ecosystem in the Insurance Industry Business Impact with MDM Enhanced customer responsiveness Reports claim that insurance firms spend roughly 44% of their sales budget on customer acquisition. Simultaneously, only 18% of this budget is spent on customer retention. In the insurance sector, consumer retention is a difficult task. Customer data integration (CDI) goes in accordance with MDM. CDI enables organisations to improve the customer experience (CX) and employ data-driven insights to reinvent product-service value propositions. With CDI, insurance firms can better understand their customers to deliver more tailored and personalised services. CDI stimulates the creation of a new product-service portfolio whilst improving personalisation and customer engagement for customer satisfaction and retention. Regulatory compliance Insurance firms should be aware of not only their data protection requirements but also CDI. With regulations like PCI- DSS, HIPAA and GDPR mandating more stringent data governance policies, enterprises are moving towards integrating MDM frameworks. MDM is enabling insurance firms to understand what data they could store and exactly where it should be stored. Advanced analytics, AI/ML and cognitive learnings Integrating advanced analytics like artificial intelligence (AI) is assisting insurance firms to up-sell/cross-sell more efficiently and effectively. By determining customer channel preferences, firms can communicate, service, market as well as sell accordingly. Conclusion Whilst it is easy to consider master data management as a technological issue, a purely technological solution without corresponding modifications to business processes and controls will likely fail to deliver satisfactory results. For insurance, master and reference data are at the centre of every mission-critical activity. Transactional systems require consistent information. Analytics need accurate dimensions and hierarchies. Compliance efforts need trustworthy data. In effect, MDM makes it possible for insurance companies to transcend the error-laden, redundant or silted information and makes one correct version of the ‘truth.’ Increased revenue Reduced Expenses Customer acquisition, retention & LTV Improved efficiency & productivity Competitive advantage So, why not adopt the MDM Insurance Services strategy and move to the next level? Insurance leaders, the ball is in your court now! 6

7. Enterprise Data Management - Leaner, Agile Data Ecosystem in the Insurance Industry About the Author RAJESH RAMAN • Vice President - Data Modernization Rajesh has nearly 2 decades of experience in the Banking and Capital Markets space and has extensive experience in managing strategic data modernization engagements. He has worked with Barclays, Bank of America, Merrill Lynch in the Private Banking, Commercial Banking and Asset Management. SRINIVASAN VENKATESAN • Associate Vice President - Data Modernization Srinivasan has more than 8 years of experience in data management. He has extensive experience in setting up and implementing MDM and Data Governance projects. Disclaimer This document makes descriptive reference to trademarks that may be owned by others. The use of such trademarks herein is not an assertion of ownership of such trademarks by SG Analytics (SGA) and is not intended to represent or get commercially benefited from it or imply the existence of an association between SGA and the lawful owners of such trademarks. Information regarding third-party products, services, and organizations was obtained from publicly available sources, and SGA cannot confirm the accuracy or reliability of such sources or information. Its inclusion does not imply an endorsement by or of any third party. Copyright © 2022 SG Analytics Pvt. Ltd. www.sganalytics.com GET IN TOUCH Pune | Hyderabad | Bengaluru | London | Zurich | New York | Seattle | San Francisco | Austin | Toronto 7