Data-Driven Decision-Making: Minimizing Risk through Automation
The Information Technology landscape has been transformative for many industries for the past five years, especially for the finance and commodity markets. For the Insurance and Reinsurance industry, IT infrastructure and data-driven decision-making tools need to catch up. Multiple factors, such as a rapidly expanding data universe, cloud, and distributed technologies, and highly granular and detailed data sets (Internet of Things, high-frequency data publication) provide opportunities for Insurance and Reinsurance organizations. Additionally, low-value, high-volatility commodity markets mean that the industry must make good decisions, not only to mitigate risk but to also capture opportunities.
The Science of risk mitigation has always been based on the data that was readily available. From government statistics to medical studies, hydrology data to wind speed, the ability to capture and analyze data for analytical insights on both the macro and micro levels allow risk managers to better mitigate risk, leading to less volatile margins and larger profit.
However, Data capture and governance itself poses problems:
- Volume: capture of large amounts of data from both internal and external sources.
- Variety: the ability to compare disparate data types, capture data from multiple locations and formats.
- Veracity: capturing data on a real-time basis, as it is published from the source or becomes available to collect.
- Revisions and Corrections: the ability to capture these changes in published data in a transparent, auditable way.
- Variability: capture data continuously even when the format and location of data sources change.
- Transparency: optimize business performance through transparent data processes.
- Normalization: allowing the comparison of disparate data sources through a consistent metadata structure and definitions across an organization’s data universe.
- Automation: collection of data automatically from all data sources with multiple granularities.
- Quality: ensuring data that is collected is complete, timely and correct.
- Governance and data management: creation of a centralized enterprise data access layer through which entitled users can derive value through data-driven self-service, while the organization remains in compliance with their data vendor agreements.
- Data Ownership: maintaining complete and unreserved control and ownership of data as a corporate asset, especially proprietary or private data.
These difficulties for an accurate, complete and continuous capture of an organization’s data universe can be overcome. A centralized, end-to-end data management platform, with industry-recognized expertise, allows companies in many industries, including Insurance and Reinsurance, to offload the responsibility and resource-intensiveness of data universe capture. This would allow risk professionals and other associated personnel refocus on the company’s core objectives, namely mitigating risk, and increasing revenue.
With such a platform, not only would the issues highlighted within this article could be potentially solved by incorporating a universal data collection, structuring, and quality engine, but risk can be additionally mitigated. With increased regulatory compliance risks, IT risk, disaster risks, market risk, emerging risk and human error risk, along with other operational risks, the ability for companies to mitigate these proactively cannot be understated.
Given the recent rise in cloud technology, the aforementioned risks can be mitigated through automation, transparency, swift time-to-market of data capture and robust information security, compliance, change control and disaster recovery processes and procedures. A robust data management platform vendor will be able to offer these risk mitigation techniques while ensuring that the data that is captured remains the organization’s property, even if the organization leaves the vendor.
ZEMA is this platform, which has proven itself in many other data-intensive industries, such as Oil and Gas, Power, Commodity-trading, Hedge Funds, Financial Firms, and Utilities. Now, the Insurance and Reinsurance industry can take advantage of such a platform to make their data-centric processes automated and validated, efficient and effective. Minimize risk and maximize self-service data insight by utilizing the end-to-end data management solution ZEMA.
Book a demo with us to know more about ZEMA, decreasing the risk of data-driven decision-making through automatic data collection, secure centralization, dynamic analytics, automation of processes and workflows, and integration with any third-party applications.https://blog.ze.com/our-industry-views/data-driven-decision-making-minimizing-risk-through-automation/Data-Driven Decision-Making: Minimizing Risk through Automationhttps://blog.ze.com/wp-content/uploads/2017/05/data-driven-decision-making-minimizing-risk-through-automation-1024x1024.jpghttps://blog.ze.com/wp-content/uploads/2017/05/data-driven-decision-making-minimizing-risk-through-automation-300x300.jpgIndustry ViewsThe ZEMA SolutionAutomtaion,Data-Driven Decision-Making,riskThe Information Technology landscape has been transformative for many industries for the past five years, especially for the finance and commodity markets. For the Insurance and Reinsurance industry, IT infrastructure and data-driven decision-making tools need to catch up. Multiple factors, such as a rapidly expanding data universe, cloud, and...Dr. Tyler Robb-SmithDr. Tyler Robb-Smithtyler.email@example.comEditorBlogs by data management Experts & Analysts | ZE