On the Cloud? Data Integrity for Insurers in Cloud-Based Platforms

Editor's Notes

• #3: Lets chat about cloud migration for insurance, the hardships, and opportunities that exist with data, and how we can ensure success when dealing with this topic.
• #4: If we look at the market forces shaping the wider industry, insurers are being called upon to be more agile so as to keep pace with rapidly changing demands. This is due to a range of factors, from higher inflationary environment expenses for insurance operations and increasing claims, to the impact of the global pandemic and predicted recession. New insurance companies that use AI and ML technology are taking over the market. An Accenture study estimated that “Underwriters are spending 40% of their time on non-core activities, representing an efficiency loss of $85-$160 billion over the next 5 years.” these are things that could be achieved with AI/ML but only of insurers have the capacity to use their own data efficiently, that same study found that up to “$170 billion in premium is at risk over the next 5 years as customers switch carriers due to not being fully satisfied by the claims process.” From a cost perspective, the case for mainframe modernization is clear.  The demand placed on the insurance infrastructure is increasing, with business capability needs driving up Million-Instructions-Per-Second (MIPS) usage and cost. A legacy mainframe also has an impact on talent, as dedicated staff will be required to provide maintenance and upkeep – a diminishing skillset as more and more senior employees retire from the workforce. Down the line, this manifests as a considerable business risk. Modernizing the mainframe is not just about overall agility, but securing a foundation for important technological transformation
• #6: Research shows that: 85% of organizations will embrace a cloud-first principle by 2025 55% of leaders site data modernization as the reason for their shift to cloud Approximately $100 billion of wasted migration spend is expected over the next three years So despite the excitement around getting to the cloud, organizations need to be careful…
• #7: It is clear that trying to bridge this integration gap is not easy. We surveyed the marketplace and these were are all identified as top challenges when it comes to implementing a modern data architecture Real-time CDC: Keeping data up to date, accessing and processing data in real-time Skills/Staff: Shortage of skills and staff who have an understanding across cloud and legacy technologies – good developers are hard to find Data Accessibility: Making data accessible to users across the business Budget: Spending on maintenance and not innovation Data Quality: Poor data quality and lack of trust in data Because moving to cloud comes with a heavy cost if not done strategically. McKinsey & Company recently conducted a study that shows that: Over 75% of cloud migration projects are overbudget 37% is spent on systems integrators since they do not have the cloud skills in house to manage these projects 15% on decommissioning costs for other platforms 38% of cloud migration projects run behind schedule Companies are looking to staff 50% of their cloud talent in house so they do not need to rely as heavily on third parties (If you are interested in learning more about this topic, all of this data came from this McKinsey Study https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/cloud-migration-opportunity-business-value-grows-but-missteps-abound)
• #8: And that’s why we at Precisely are focused on delivering data integrity in a new and different way. There has been no consistent definition of data integrity that the market all uses, and the definitions out there often focused on collections of technical capabilities related to data quality. But that is not good enough. Today’s businesses need a definition of data integrity based on their need for data that is not just accurate and consistent, but is also rich in context. And that’s why Precisely has redefined data integrity as data with maximum accuracy, consistency, and context for confident business decision-making. A critical aspect of data integrity is data availability and in order to have that, businesses need to be able to integrate all of their mission-critical systems.
• #9: And you can certainly find any number of articles and surveys in the press, or analyst reports, that dig into this issue of data integrity. Here you see just a few stats from Forbes, the Harvard Business Review, and Precisely’s own Data Trends Survey. When two-thirds of organizations say siloed data negatively impacts their data initiatives and almost half of newly created data records have at least one critical error, it is no wonder that 84% of CEOs doubt the integrity of the data on which they make decisions!
• #10: The most common mistake insurers have committed is assuming they need to go all-in on a cloud migration. Many companies are learning they can be successful with an incremental approach lift and shift or deep cloud integration? Public, private, hybrid, or multi-cloud? Cloud tools and KPIs Cloud service providers Governance and security considerations Cloud migration strategy and roadmap The modular, interoperable Precisely Data Integrity Suite contains everything you need to deliver accurate, consistent, contextual data to your business - wherever and whenever it’s needed. The seven modules of the Data Integrity Suite are built on proven Precisely technology. Not only do the Suite’s modules work seamlessly together, they also work alongside the portfolio of Precisely products, enabling you to easily adopt Suite capabilities for new use cases whenever you choose All elements sit on a common foundation that’s modular, interoperable, intelligent, and business friendly. The foundation provides a range of services, including a metadata management engine that shares data between the modules and third-party programs to deliver incremental value.
• #11: Typically, in any major data initiative, you first need to connect to sources, and sometimes move or replicate data to another environment. With Data Integration, you can easily create streaming data pipelines that integrate data from core environments such as relational, and of course, mainframe and IBM I, with modern cloud-based data platforms like Snowflake to drive analytics and innovation and extend the value of your mission-critical systems. We understand that pipelines must scale for your needs today and extend for tomorrow.
• #12: In order to make sure that we deliver seamless integration, Precisely is partnering with the biggest names in the market. The data integrity suite and connect offer native integrations with these platforms, and we work together to solve use cases that extend platform value. These aren’t just marketing partnerships, we customize our platform to integrate with these partners.
• #14: Connect can translate mainframe data on the fly with no MF expertise required. Connect has the ability to handle complex mainframe copy books. Connect has the ability to distribute variable length data with ease, all while retaining the integrity of data.
• #15: Connect empowers customers to solve strategic problems like: Reduce time to market of revenue generating applications Simplify data integration and data life cycle Ensure a better customer experience Enable the adoption of new platforms for strategic data initiatives including Increase the value of cloud investments Gain strategic value from all your enterprise data Meet regulator and compliance requirements for data integration
• #16: Weather with precisely or someone else, we are all about data integrity, the individual journey and the benefits that it brings,