On the Cloud? Data Integrity for Insurers in Cloud-Based Platforms
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In our undeniably digital world, data is one of the most precious assets in business. This is especially true for the insurance industry, which is why many are leveraging modern cloud-based platforms to improve performance, reduce costs, and capitalize on new opportunities to innovate. While all industries feel the pressure to preserve or enhance the integrity of their data through their cloud migration initiatives, insurers are especially impacted given how crucial data is to their operation. With their high volumes of claims, policies, and premiums, an ineffective approach to data quality and validation, not only slows down cloud migration but leaves organizations open to threats and risk. Although there is no one-size-fits-all solution for implementing and maintaining data integrity for insurance companies, ensuring the potential to extract value from their data is maximized is universal. If you are thinking of moving into a cloud platform or wondering what is next, join us to learn about: Integrating data silos and ensuring better securityLeveraging data observability to proactively identify data issues before they impact the businessDelivering quality data attributes that are trusted and fit for purposeEnhancing business data through data enrichment and location intelligence solutions to unlock valuable, hidden context, and reveal critical relationships transforming raw data into actionable insights
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On the Cloud? Data Integrity for Insurers in Cloud-Based Platforms
- 1. On the Cloud? Data Integrity for Insurers In Cloud- based Platforms Ashwin Ramachandran Senior Director Product Management
- 2. “Businesses that follow the lead of cloud-migration outperformers stand to unlock some $1 trillion in value.” – Mckinsey & Company
- 3. On the cloud? The demand placed on the insurance infrastructure is increasing! • Innovative InsureTech companies are gaining ground in the market • Client’s demand for flexibility and accessibility accelerated • Uncertainty about customized legacy applications • Slow data analytics due to complex data silos • Difficulty innovating with data-driven strategies • Costly mainframe maintenance • Skill risks due to outdated programming languages • Developing future-proof and strategic applications
- 4. Data integration is more critical than ever Businesses of all types are adopting the cloud faster than ever before. Hybrid cloud is the environment of choice for flexibility, control, and security. Real-time access to data is required for fast, confident decision making 1 2 3 On the cloud?
- 5. “85% of organizations will embrace a cloud-first principle by 2025” - Gartner “55% of leaders cite data modernization as the reason for their shift to cloud” - Deloitte “Approximately $100 billion of wasted migration spend is expected over the next three years” - McKinsey & Company So despite the excitement around getting to the cloud, organizations need to be careful… On the cloud?
- 6. Implementing a modern data integration framework isn’t easy 0 10 20 30 40 50 60 Real-time CDC Skills Governance Data Accesibility Budget Data Quality Legacy % of People Who Consider this a Top Challenge (Rated 1 or 2) (Rated 1 or 2) 1. Real-time CDC: Keeping data up to date, accessing and processing data in real-time 2. Skills/Staff: Shortage of skills and staff who have an understanding across cloud and legacy technologies 3. Data Accessibility: Making data accessible to users across the business 4. Budget: Spending on maintenance and not innovation 5. Data Quality: Poor data quality and lack of trust in data 6. Legacy systems: Difficult to leverage data from legacy systems (mainframes, EDWs, IBM i etc.) with modern cloud platforms 7. Scalability: Ability to scale with and process massive data volumes Q. What are your top challenges when it comes to implementing a modern data architecture? On the cloud?
- 7. You need data integrity… data with maximum accuracy, consistency, and context for confident business decision- making On the cloud?
- 8. 47% of newly created data records have at least one critical error 68% of organizations say disparate data negatively impacts their organization 84% of CEOs say that they are concerned about the integrity of the data they are making decisions on Data Trends Survey 2019 Forbes HBR Data integrity is a business imperative On the cloud?
- 9. Data Integrity for your cloud migration journey Consistenc y Accuracy Context Data Integrat ion Data Observabilit y Data Quality Geo Addressi ng Spatial Analytic s Data Governance Data Enrichme nt Data Integrity Foundation On the cloud?
- 10. Data Integration Break down data silos by quickly building modern data pipelines that drive innovation
- 11. On the cloud? Strategic partnerships support expanding data integration strategy Native integrations with partner platforms Solving use cases together that extend platform value
- 12. Precisely + AWS = Success Simple integration Single solution to integrate, prepare, load, cleanse, transform & stream mainframe data to the AWS cloud Real-time replication Databases are kept in-sync between mainframe and cloud for reporting, analytics, and data warehousing Scalable and resilient High-performance replication is able to recover from network and DB connectivity loss Mainframe expertise Leverages Precisely’s 50 years of experience implementing complex mainframe solutions On the cloud?
- 13. On the cloud? Helping enterprise customers solve strategic problems • Reduce time to market of revenue-generating applications • Simplify data integration and data life cycle • Differentiate through superior customer experience • Enable the adoption of new platforms for strategic data initiatives, including. • Increase the value of cloud investments • Gain strategic value from all enterprise data • Meet regulatory and compliance requirements for data integration
- 14. On the cloud? Focus on Data Integrity for cloud migration success • Break down data silos by connecting traditional systems, such as mainframes, to next-wave technologies in minutes • Real-time data delivery to feed business applications and analytics • Design once, deploy anywhere to future-proof your business with easy to-use GUI and self-tuning engine - no coding or tuning needed • Work with mainframe expertise to help drive innovation – directly access and understand VSAM files, COBOL Copybooks, IMS, mainframe fixed and sequential files, and Db2 data • Build data governance and quality Into your data-centric processes to ensure accuracy and context
- 15. Learn more about us at Precisely.com On the cloud?
Notas do Editor
- 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.
- 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
- 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…
- 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)
- 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.
- 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!
- 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.
- 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.
- 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.
- 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.
- 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
- Weather with precisely or someone else, we are all about data integrity, the individual journey and the benefits that it brings,