Data has become an invaluable asset for the modern insurance industry that helps insurers better judge risk, set prices, and attract and serve customers with greater efficiency.
But, flooded by data from customer-service platforms, the Internet of Things (IoT), third party data providers, and a variety of other sources — combined with a national shortage of data science talent – means that achieving truly “data-driven” operations is still elusive for many insurance companies.
This is especially true at firms trying to embrace the latest generation of advanced analytics and data modeling applications, which require a significant commitment of both time and resources to succeed.
In the same way that DevOps methodology unified development and operations teams, the Composable DataOps model provides a flexible platform to manage and facilitate data management, governance, and collaboration.
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A major feature of the underwriting process is gathering detailed information about the applicant from numerous heterogenous sources, then verifying the accuracy of those assets; these are classic data management problems. In the past, each data point would have been verified by quality assurance (QA) engineers working in a customized test environment, a resource-intensive solution.
Automated data pipelines — a foundational element of the Composable DataOps methodology — can eliminate manual inputs and other data management tasks from the process of ingesting, cleansing, and preparing new data.
By replacing complex scripts with an intuitive, visual interface, automated data pipelines streamline data quality checks, reporting operations, and similar operations, empowering your underwriters with access to high quality data assets, which they can leverage for better decision making.
New frontiers in the insurance industry, such as cyber insurance, require your underwriters have access to timely, accurate data. Under legacy data management paradigms achieving the required levels of ETL speed is virtually impossible but automating the ingestion and transformation of new data empowers underwriters to respond to a changing market in near real-time.
Better Leverage Third Party Data
Data from social media platforms and third-party data suppliers can enrich the underwriting process, but only when you have a reliable means of cleansing that data, ensuring that it’s accurate enough for your analytics applications, and preparing it for your analytics applications.
Claims are by far the biggest expense at the average insurance company. To achieve an efficient, accurate claims process that maximizes your profits and long-term stability, you should be giving your adjusters the most relevant, highest-quality data, so they can apply their personal judgement and make the right choices.
While conceptually the need for good data is easy to understand, integrating datasets into intuitive, functional workflows is of full complexity and ambiguity. How can you ensure those insights result in executable outputs?
The Composable DataOps model combines automation and machine learning tools that lets claims professionals make use of the data they’ve collected about each individual case and run complex analysis a seamless process.
Stronger fraud detection
According to the Federal Bureau of Investigation, there’s a bout $40 billion dollars in fraudulent insurance claims ever year. By providing your data engineers with powerful tools to build and share high-quality datasets, you enable them to move past traditional heuristics and embrace machine learning and predictive analytics, gauging claims accuracy, enabling stronger loss control, and lowering false positives.
Enterprise AI Driven Claims Settlement
Being able to settle claims quickly and efficiently is a key strategic differentiator for insurers, with 87% of policy holders saying that the claim processing experience impact their decision to renew an existing policy. By helping your organization consolidate both structured and unstructured data, eliminate data siloes, and streamline cross-organizational data orchestration, Composable DataOps tools and processes can significantly improve the accuracy and speed of your claims process.
Insurance companies must contend with a rigorous regulatory compliance requirements, including the Gramm-Leach-Bliley Act (GLBA), HIPAA, NY SHIELD, and more. In addition, the recent wave of privacy rules like the CCPA and CDPA have put additional strain on both compliance and business teams, as they complicate the operationalization of company data.
To stay confidently data driven without violating compliance responsibility takes a comprehensive, methodical approach to data governance that incorporates your processes and technology.
The Composable DataOps approach empowers data managers and consumers with advanced tools and processes that eliminate many of the arduous data governance tasks. In addition to automated ETL, Enterprise AI, the Composable DataOps framework includes several tools to help with compliance:
Composable architecture, another major feature of the Composable DataOps methodology, is a method of building software that replaces monolithic, cumbersome software products with microservices or modules, which staff can access as needed through APIs.
This lightweight offer vastly improved flexibility, scalability, and agility beyond what could be achieved with traditional models; it also has regulatory compliance benefits. By allowing data managers to protect and flexibly grant access to only the data assets need for each individual workload, sensitive personally identifiable information (PII) to only where its needed, without the cumbersome process of provisioning data from a single warehouse.
Composable Analytics has been at the forefront of the DataOps industry since its inception. With direct expertise helping insurance firms realize the power of a single ecosystem for ETL automation, data management, data governance, and enterprise analytics, we’ve learned exactly how to help those companies achieve truly data driven operations.
Want to find out more? Schedule a DataOps Consultation.
Composable Analytics, Inc. builds software that enables enterprises to rapidly adopt a modern data strategy and robustly manage unlimited amounts of data. Composable DataOps Platform, a full-stack analytics platform with built-in services for data orchestration, automation and analytics, accelerates data engineering, preparation and analysis. Built with a composable architecture that enables abstraction and integration of any software or analytical approach, Composable serves as a coherent analytics ecosystem for business users that want to architect data intelligence solutions that leverage disparate data sources, live feeds, and event data regardless of the amount, format or structure of the data. Composable Analytics, Inc. is a rapidly growing data intelligence start-up founded by a team of MIT technologists and entrepreneurs. For more information, visit composable.ai.