Maximize ROI of Insurance Digital Transformation Initiatives with Proven Data Integrity and Data Governance Methods
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Many insurance carriers are transforming the way they do business by deploying new software technologies, migrating data and services to the cloud, and leveraging artificial intelligence (AI) to speed decision-making. Data is at the heart of all these initiatives, and it has a direct impact on success or failure. When that data is integrated into upstream or downstream processes, it can also have a broader impact on the operational, analytical, and compliance needs of the organization. The traditional, and often ad-hoc, tools and processes that organizations employ to support data quality, data integrity, transaction reconciliation, and exception management are often inadequate. They do not provide the speed, technical agility, and intelligence demanded by digital transformation initiatives. Join us to explore proven methods of how insurance carriers are maximizing ROI and minimizing the time-to-value of digital transformation initiatives by: • Aligning data governance with organizational and project objectives to reduce implementation effort and duration • Leveraging automated controls for data quality, including balance and reconciliation of data in motion to avoid operational disruptions and maintain regulatory compliance • Increasing efficiency and capability through centralized data integrity solution
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Maximize ROI of Insurance Digital Transformation Initiatives with Proven Data Integrity and Data Governance Methods
- 1. Maximize ROI of Insurance Digital Transformation Initiatives: Proven Data Integrity & Data Governance Methods
- 2. Housekeeping Webinar audio • Today’s webinar audio is streamed through your computer speakers • If you need technical assistance with the web interface or audio, please reach out to us using the Q&A box Questions welcome • Submit your questions at any time during the presentation using the Q&A box. If we don't get to your question, we will follow-up via email Recording and slides • This webinar is being recorded. You will receive an email following the webinar with a link to the recording and slides 2
- 3. Today’s speakers Chris Reed Manager, Sales Engineer Michael Shelton Account Director, Insurance Practice 3
- 4. The leader in data integrity Our software, data enrichment products and strategic services deliver accuracy, consistency, and context in your data, powering confident decisions. of the Fortune 100 97 countries 100 2,400 employees customers 12,000 Brands you trust, trust us Data leaders partner with us Precisely Confidential and Proprietary - Do Not Copy or Distribute 4
- 5. Data integrity is… data with maximum accuracy, consistency, and context for confident business decision-making 5
- 6. The elements of Data Integrity Change Data Capture ETL Machine Data Integration Process Automation Integrate Data Governance Data Profiling Data Quality Master Data Management Self-Service Analytics Verify Spatial Analysis Geocoding Routing Visualization Locate Geographic Data Business Data Industry-Specific Data Enrich Integrated Comms Personalized Video Chatbots Responsive Messaging Digital Self-Services Engage 6
- 7. 7 $1.3 Trillion Spend on digital transformation in 2020 $2.3 Trillion Projected spend on digital transformation in 2023 “The companies that will stand out are the ones that are going to find ways to move a bit faster, at the pace of the people they’re insuring.” — Scott Simony, head of industry, Google
- 8. Increase Productivity & Revenue GROW Improve Customer Experience New Product & Service Offerings New Business Models Digital transformation examples 8 Corporate goals - portfolio / risk realignment • Shift from commoditized products to higher value offerings • Expand markets / territory / states • Enable analytics / modeling to drive direction • Leverage technology for UW and custom products Objectives to support goals • Modernize systems for flexibility to quickly add/modify products and enter/exit markets • Implement data platforms for analytics, reporting, and rapid integrations • Improve entity understanding and relationship • Leverage IOT
- 9. 9 Data trust Only 23% of business decision-makers are confident that data will support their business objectives.
- 10. 10 Increase Productivity & Revenue GROW PROTECT Improve Customer Experience New Product & Service Offerings Operational Risk Regulatory/Legal Risk Financial Risk New Business Models Strategic/Reputation Risk DX 1. Digital portfolio of products & services with an ecosystem of partners 3. Omnichannel customer experience 2. Advanced analytics 4. Automated operations Growth Brings Risk
- 11. Governance enables & accelerates programs 11 Time The scramble Build & migration How could that be? Degradation We own it Reliability Let’s start with data! Enterprise collaboration Data Readiness Traditional Mobilization point Recognition of Lost ROI 18-24 months to stabilize and begin realizing business value Master data degrades at an avg. Rate of 2-7% per month Proactively mobilize early! 25% reduction in implementation time 40% reduction in development effort 50% less functional spec rework for data VALUE CREATION RISK REDUCTION
- 12. 12 Align data governance to organizational and project objectives to reduce implementation effort and duration Leverage automated controls for data quality, including balancing and reconciliation, to avoid operational disruptions and maintain regulatory compliance Increase efficiency and capability through a centralized data integrity platform
- 13. Align data governance to organizational and project objectives to reduce implementation effort and duration
- 14. The point of data governance is not governance
- 15. The point is… Reporting & compliance • Internal reporting • Industry-specific regulations • HIPAA • SOX • Basel II • GDPR Analytics & insights • Big data • Machine learning • Internet of things • Global visibility • Real-time analytics • 360° view of the customer Operational excellence • Improve working capital • Strategic sourcing • System migration & consolidation • Enhanced customer satisfaction • Product traceability • New product intro Delivering business outcomes and connecting business goals, objectives & value with measured impacts & risks 15
- 16. Business-first approach • Prioritize the data that matters • Link Data Governance to business goals • Build stakeholder engagement across all levels Data Governance programs that prioritize critical data have 5x faster time-to-value 16
- 17. Linkage to value drivers make the program relevant 17 C-suite strategy Improve policy risk & pricing Ability to access the correct data faster Removing excessive process steps Business initiatives & associated KPI’s Policy sales growth – IOT deployment Client renewal / retention – Billing modernization Average cost per claim – Snowflake Loss Ratio – Other DX Data governance operating model & platforms Organization • Organizational support • Process ownership • Productivity improvement • Elimination of non-value-added activities Tools • Metrics definitions • DQ reporting • Workflow • Standards and rules repository Governance • Standards and rules • Measures and metrics • Process standards • Business traceability • Business and data definitions Highly visible strategic “wants” Less visible enablers
- 18. Demo
- 19. Leverage automated controls for data quality, including balancing and reconciliation, to avoid operational disruptions and maintain regulatory compliance
- 21. • Balancing and recon • Data quality • AI / ML • Automation • Exception management • Audit Trail • Communication and visualization Quality fundamentals • Ensure operational continuity • Maintain regulatory compliance • Enhance data quality • Increase visibility • Create efficiencies 21
- 22. Demo
- 23. PROBLEM Success Story • When implementing a new financial data warehouse, this insurer needed to automate reconciliations between the SAP GL daily and monthly. • Confidence in the quality of the data was paramount to the success of the project. SOLUTION • Precisely solutions implement comprehensive end-to-end data quality checks around the new DW and the SAP GL. • Automated monthly reconciliations are reduced from two days to 30 minutes and deliver high confidence in the quality of the data. Global Insurance Provider • Data transformation & rationalization • Business process optimization
- 24. PROBLEM Success Story Core system transformation presented the opportunity to recalibrate how the mechanics of financial reconciliation processes could be improved to reduce: • Process costs • Increase speed • Limit risk and improve accuracy SOLUTION Precisely solutions improved operational and audit efficiency to support regulatory compliance by instituting a standardized and automated end-to-end data integrity process to reduce errors and improve data quality. P&C Insurance Company • Data transformation & rationalization • Compliance & regulation
- 25. Increase efficiency and capability through a centralized data integrity platform
- 26. “Through 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives” Digital transformation of governance and quality • Speed • Technical agility • Intelligence • Automation • Efficiency • Standardization 26
- 27. GOVERNANCE ACCURACY CONSISTENCY CONTEXT On-premises, Hybrid Cloud & Cloud Data Integration Governance & Quality Location Intelligence Data Enrichment SOLUTION AGILITY INTEROPERABLE | INTELLIGENT | PRECISELY ID 27 DATA DEMOCRATIZATION / MODERN USER EXPERIENCE
- 28. Drive organizational success • Incorporate a business first data governance approach into strategic initiatives • Practice the quality fundamentals • Ensure controls solutions are compatible with the speed, flexibility and standardization required for digital transformations. 28
- 29. Benefits of a business-first approach Accelerate program roll-out by 18-40% Increase likelihood of reinvestment by over 75% Generate 2-7x greater ROI 29
- 30. Questions?
Notes de l'éditeur
- In the race for digital transformation, industries are investing more and more in new technologies, such as Big Data, Artificial Intelligence , or the Internet of Things. Admittedly, digital transformation isn't exactly new as a term anymore either, and it seems a bit hackneyed. Nevertheless, huge investments are still being made and the actual drivers and motivations are apparently still omnipresent. IDC, for example, puts global investment in 2020 at 1.3 trillion dollars and forecasts spending of 2.3 trillion dollars in 2023, which are huge sums. So, what do companies expect from digital transformation? What are the drivers and what are the success factors? Why transform at all, and if so, with what goal in mind? What are pre-requisits for success?
- Can also note that HFS Research, 2021 did a survey of Global 200 insurance executives, and they found ‘Deficiency in overall data quality’ is the number two inhibitor holding back from achieving digital transformation objectives.
- MS – Get words for bottom portion from the original slide. Leverage notes below to talk about context of objectives and application Original notes With stronger customer expectations, increased customization demands, the complexity of the global supply chain and changing regulations, manufacturers are in need for change, which they try to achieve through digital transformation and digital optimization. Their main objectives are to grow by reducing manufacturing and logistics costs. For example, through strengthening the traceability and transparency within the supply chain by improving customer experience by developing new products and service offerings by creating new business models. On the other side, they need to protect their business from different types of risks as it can be seen here. To achieve these objectives, quality control is one eminent important pre-requisite – in the physical world of manufacturing as well as in the digital world. Every manufacturer has a very good quality control for their existing physical production processes to reduce cost, improve efficiencies and deliver quality products. Imaging what it means to deliver a car, where the engine does not work, because nobody tested the engines before? What would be the cost when you have to do the recalls of your products to fix it? What would be the customer experience? What would it mean to your brand and revenue? When you have a data supply chain with a weak quality control, the same impact occurs to your business! Let’s have a look at a few initiatives in the manufacturing industry where trusted data are a pre-requisite.
- Get Sue’s words from her presentation on data governance and data quality
- Hierarchy of Objectives Metrics – tied to calculations and data Business Terms – tied to physical data Rules and scores – tied to data Balancing and reconciliation Data quality or transformation or integration Process model showing controls
- High-level overview of centrally managed controls Highlight ease of use – showing reconciliation w/ drag and drop capabilities Centralized and standardized location for controls (show multiple recons in one place) Visibility - dashboard of control status w/ drill downs Integrated exception management - for maintenance and auditing ACTIONS – highlight technical agility, e.g. legacy to current, Streaming, volume, other types of modern needs. Dual use – testing and production (talk track around enabling projects and testing then rolling to production for consistency) Operational / month end process Speed type value – proactive notifications, detailed level validations, exception reporting, centralized exception management and root cause analysis.
- Precisely Data360
- Data360 DQ+
- Modular, SaaS applications allows choice with evolving business needs Intelligent M/L for prescriptive recommendations and actions Low-friction accessibility for rapid time-to-value Designed in the Cloud, can run anywhere Best-in-class user experiences to cultivate data culture Built to partner for interoperability and scalability