The path to data maturity in the insurance industry | The Financial Express

The path to data maturity in the insurance industry

As with so many other industries, the digital revolution has had a profoundly disruptive effect on the insurance industry.

The path to data maturity in the insurance industry
The role of insurance companies in the modern world has rapidly expanded in recent years.

By Saptarshi Sengupta, Senior Director of Product Marketing, Denodo

The role of insurance companies in the modern world has rapidly expanded in recent years. Insurers now take on a vastly greater scope of responsibility, covering an enormous array of verticals. This growth, however, has been accompanied by its own set of challenges. The shift to digital and the growing importance of analytical inputs have only exacerbated these issues, driven by increased competition and heightened customer expectations. Today, these are the insurance industry’s most frequently encountered challenges.

Perils Facing the Insurance Industry

As with so many other industries, the digital revolution has had a profoundly disruptive effect on the insurance industry. The greatest evidence of this is seen in the increased importance of advanced analytics. Through the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML), many key processes in the traditional insurance process can now be fully automated. In fact, common activities such as policy issuance, renewals, and updates, no longer require any human intervention at all. Analytics can aid in more intensive aspects of the process, such as damage assessment, increasing the efficiency and effectiveness of insurance agents. As these processes continue to evolve, agents will be able to take on even more complex roles.

Many insurance companies have recognised the massive advantages that advanced analytics have to offer and are working to integrate them into existing systems. These efforts are taking place alongside a wider systemic push for modernisation – from physical to digital, in-house systems to cloud-based systems, and labour-intensive to automated processes. But this push for greater flexibility also presents companies with multiple hurdles. Seamlessly integrating cloud and onsite data into a common data pool is challenging, especially due to the need for real-time access to multiple data sources. This can include text, voice, streaming data, and a variety of other sources. Collating all these sources for the purpose of analysis can result in bottlenecks, negating any advantage gained through analytics.

These infrastructural challenges are further compounded by a number of legislative barriers affecting the industry, which further exacerbate attempts at modernisation. Governmental oversight has resulted in regulations including the Health Insurance Portability and Accountability Act (HIPAA), the Gramm–Leach–Bliley Act, and the NAIC Insurance Data Security Model Law in the United States. Similar legislation has arisen in the EU, such as the General Data Protection Regulation (GDPR). While designed for the greater good, such regulations hinder the effective implementation of new data-focused processes. Due to the need for compliance and verification for every data module, any attempt to gather or access data can become a cumbersome process, stretched out over weeks. As a result, organisations looking to modernise their data infrastructures are often confronted with multiple time-consuming, expensive legal procedures.

The Shift to a Modern Data Infrastructure

Faced with these challenges, insurance companies need a path that enables them to modernise through advanced analytics, and a logical data fabric presents them with just such an opportunity. In its simplest terms, a logical data fabric is an architecture that uses data virtualization to integrate and help govern data across both on-premises and cloud deployments. The creation of this paradigm was in direct response to the difficulties of traditional data warehousing and the reliance on physically replicating data. Such approaches were time and resource-intensive, demanding frequent maintenance and upkeep.

The extract, transform and load (ETL) processes linked to data warehousing pose a further problem through their inability to cater to the data demands of various groups simultaneously. With such processes, insurers are often left grappling with a system that is resistant to change and unable to meet compliance regulations. Logical data fabric presents a solution by integrating data across users and platforms. Logical data fabric’s ability to process metadata makes it able to provide smarter data recommendations. According to Gartner, this results in data management needs dropping by approximately 70 percent. By using data virtualization to source data while leaving the original data untouched, physical replication is avoided and system resources are kept free.

Data virtualization differs from traditional ETL processes by serving as a connective data-access layer across a company, connecting individual data siloes with data consumers across different departments. Rather than containing copies of the original data, it directly accesses the requested information, while enabling stakeholders to implement global data governance protocols from a single point of control. This single-point access also empowers organisations to roll out updates and changes seamlessly, with no disruption to the network at large.

Ultimately, logical data fabric provides companies with two key advantages: The first is the adaptability offered by its adaptive architecture, enabling changes to be easily made to a company’s data infrastructure. Second, by working within a company’s existing infrastructure, it negates the need for an expensive, disruptive overhaul.

Insurance companies that implement logical data fabric are well on their way to modernising their data infrastructure and reaping the full benefits of this digital shift. Their data will be available through a modern, agile interface, enabling them to follow compliance norms and unlock the full potential of data to drive future decisions for the optimal results.

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First published on: 22-11-2022 at 09:16:08 pm
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