Business now is all-powered by data at every stage: business people must first quantify risk, set a reasonable price for their products, and, most importantly, offer protection to their customers. Traditionally, insurers relied upon data drawn from common sources such as demographic data, traffic, weather patterns, and environmental data to segment their customer base and offer the appropriate products to suitable people. Now, with data coming into databases from every possible direction, right from social media and the Internet of Things, employees have to manage and draw insights to offer suitable products to micro-segments of their customer base.
This offers an enormous opportunity for insurers to go granular in assessing risks and offer personalized schemes and products to each individual. A mature data management framework that can derive value from large volumes of data, perform complex formatting, and handle the challenges posed by ever-changing regulatory policies is the need of the hour for insurers. If you are interested, read on to find out more about how data management can drive business growth.
Challenges of Accumulating Data
Employees are faced with an avalanche of data coming from an ever-increasing number of data streams such as historical enterprise data and external third-party sources of data. This poses a huge challenge for actuaries employed to make sense of relevant data to quantify risk and uncertainty. There is also an overall exponential growth in the influx of data coming from everyday interactions with customers and distributors originating from an increased emphasis on process digitization. This herding of data poses a significant challenge for business people wrestling with mapping out their data environment, as data enters the ecosystem ad-hoc, leading to numerous separate data workflows within the organization.
Once the data has been gathered and confined in a central repository, data governance can become an issue especially when data repositories have been siloed. There is also the question of standardization as data is stored in different formats during ingestion into the storage repositories. Such an organization of data is solely a technical challenge but also depends upon the question of how value can be derived from data. There is also the issue of security and effective data governance arising out of the need to modernize a business’ data environment. Regulatory changes have become stricter in recent years with documentation and reporting becoming an integral part of an effective data management platform. These challenges can seem daunting to surmount, but they can be overcome with an effective data management platform.
Essential Components of Data Management Platform (DMP)
Data Management is a vast domain with a very broad area of focus with four operations to be performed which are common to any data management platform. These four operations may be already being performed within your organization but in a sidelined and duplicated manner across the departments. A good data management platform should avoid a siloed architecture and make the following operations in a cohesive and unified manner:
- Organizing Data
A good DMP should be able to corral and consolidate data coming in from different siloes and distribute data across storage repositories to enable effective use
- Enhancing data quality
A good DMP should be able to support business intelligence activities performed by actuaries by assessing and validating data across all departments in the organization
- Ensuring data security
A good DMP should have built-in security features to ensure the safety of proprietary and important customer data
- Data storage and Backup
A good DMP should ensure and maintain reliable access to data along with fail-safe procedures to limit downtime during crashes
Benefits of a Data Management Strategy
The above four components can ensure that insurers operate with efficiency at the top of the game in this highly competitive industry. By centralizing and giving access to appropriate data to more employees and partners across the spectrum, people can drive more business value out of data by gleaning actionable insights. By laying out an effective data management strategy as the foundation, insurers can also employ advanced strategies such as AI and predictive analytics to lay out a path toward maximum utilization of the promises held by the data in their storage repositories.
Features such as fraud prevention, risk analysis and management, and claims adjudication can be automated with machine learning and predictive analysis with an effective data management strategy as the foundation. These benefits ripple across areas such as customer experience and customer satisfaction, leading to the development of effective new products and services.
Aspire’s Data Management
The data management practices of Aspire systems profoundly offer services on core data concepts like data strategy, data quality, master data management, metadata management, data visualization and data governance for the betterment of business.
Conclusion
A mature data management strategy is highly crucial for businesses looking to bolster their operational management and efficiency. Some businesses are still in the early stage of unlocking value out of data – new business models of operation, products, and services can be developed with an effective DMP as the foundation. Lastly, data management strategy is a crucial foundation for success as business goes forward in this new digital era.
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