To thrive among entrenched rivals and compete more effectively with digital natives, insurers will need to get their data right. That will mean moving to more responsive, AI-enabled architectures that accelerate data management and deliver insights that drive business performance.
To Become a Data-Driven Enterprise, Data Democratization is EssentialCognizant
The document discusses how data democratization through an insights marketplace is essential for organizations to become truly data-driven. It defines data democratization as making data accessible across business lines through self-service analytics and predictive platforms. An insights marketplace allows internal users and partners to search, access, and subscribe to shared data assets like reports, models, and raw data. This facilitates collaboration, reduces duplication of efforts, and can help organizations monetize their data internally through improved products and efficiency or externally through partnerships. Examples of Transport for London and educational institutions successfully applying these approaches are provided.
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
Learn about Addressing Storage Challenges to Support Business Analytics and Big Data Workloads and how Storage teams, IT executives, and business users will benefit by recognizing that deploying appropriate storage infrastructure to support a wide range of business analytics workloads will require constant evaluation and willingness to adjust the infrastructure as needed. For more information on IBM Storage Systems, visit http://ibm.co/LIg7gk.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
The document discusses 7 strategies for enterprises to survive disruptions from the nexus of forces in 21st century IT, including big data, cloud computing, mobile technology, and social media. These strategies are: 1) addressing big data challenges through improved information management and analytics, 2) adopting in-memory computing to improve data velocity, 3) embracing cloud computing while securing corporate data, 4) developing hybrid IT approaches using private and public clouds, 5) managing challenges from the growing Internet of Things, 6) achieving full integration across new IT deployments, and 7) leveraging platforms that integrate solutions to simplify operations.
Modernizing Insurance Data to Drive Intelligent DecisionsCognizant
To thrive during a period of unprecedented volatility, insurers will need to leverage artificial intelligence to make faster and better business decisions - and do so at scale. For many insurers, achieving what we call "intelligent decisioning" will require them to modernize their data foundation to draw actionable insights from a wide variety of both traditional and new sources, such as wearables, auto telematics, building sensors and the evolving third-party data landscape.
- Organizations are increasingly adopting enterprise cloud strategies to enable digital transformation and remain competitive in the face of demands from customers, mobile workforces, and new technologies.
- Digital transformation requires flexible IT solutions and the ability to extract value from massive new data streams through business intelligence in order to empower employees, enhance customer experiences, and improve business processes.
- Successful digital strategies require cloud deployments that are tailored to an organization's specific needs and goals in order to deliver immediate value and support the organization as needs change over time.
To Become a Data-Driven Enterprise, Data Democratization is EssentialCognizant
The document discusses how data democratization through an insights marketplace is essential for organizations to become truly data-driven. It defines data democratization as making data accessible across business lines through self-service analytics and predictive platforms. An insights marketplace allows internal users and partners to search, access, and subscribe to shared data assets like reports, models, and raw data. This facilitates collaboration, reduces duplication of efforts, and can help organizations monetize their data internally through improved products and efficiency or externally through partnerships. Examples of Transport for London and educational institutions successfully applying these approaches are provided.
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
Learn about Addressing Storage Challenges to Support Business Analytics and Big Data Workloads and how Storage teams, IT executives, and business users will benefit by recognizing that deploying appropriate storage infrastructure to support a wide range of business analytics workloads will require constant evaluation and willingness to adjust the infrastructure as needed. For more information on IBM Storage Systems, visit http://ibm.co/LIg7gk.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
The document discusses 7 strategies for enterprises to survive disruptions from the nexus of forces in 21st century IT, including big data, cloud computing, mobile technology, and social media. These strategies are: 1) addressing big data challenges through improved information management and analytics, 2) adopting in-memory computing to improve data velocity, 3) embracing cloud computing while securing corporate data, 4) developing hybrid IT approaches using private and public clouds, 5) managing challenges from the growing Internet of Things, 6) achieving full integration across new IT deployments, and 7) leveraging platforms that integrate solutions to simplify operations.
Modernizing Insurance Data to Drive Intelligent DecisionsCognizant
To thrive during a period of unprecedented volatility, insurers will need to leverage artificial intelligence to make faster and better business decisions - and do so at scale. For many insurers, achieving what we call "intelligent decisioning" will require them to modernize their data foundation to draw actionable insights from a wide variety of both traditional and new sources, such as wearables, auto telematics, building sensors and the evolving third-party data landscape.
- Organizations are increasingly adopting enterprise cloud strategies to enable digital transformation and remain competitive in the face of demands from customers, mobile workforces, and new technologies.
- Digital transformation requires flexible IT solutions and the ability to extract value from massive new data streams through business intelligence in order to empower employees, enhance customer experiences, and improve business processes.
- Successful digital strategies require cloud deployments that are tailored to an organization's specific needs and goals in order to deliver immediate value and support the organization as needs change over time.
The document discusses how IT infrastructure is changing to adapt to new business priorities in the digital age. It introduces the "HEROES" framework for the future of IT infrastructure, which focuses on hybrid cloud architectures, edge computing, robotic process automation, obsolescence of old IT, and enterprise security. Artificial intelligence will be integrated across all areas of the framework and fundamentally change how organizations procure and consume IT infrastructure over the next five years.
Value proposition of analytics in P&C insuranceGregg Barrett
The document provides an overview of the value of analytics in the property and casualty (P&C) insurance industry. It discusses the challenges facing the industry and how analytics can help insurers address these challenges. The document is divided into six sections that cover topics such as the impact of analytics across the insurance life cycle, the value of data and analytics, and considerations for implementing analytics and managing big data. Organizations that effectively adopt analytics are shown to achieve greater growth and returns. While analytics provides opportunities, the document also notes challenges such as developing a data-driven culture and addressing privacy and ethical issues that can arise from certain data collection and analytic practices.
In this edition of our Work Ahead study, we explore the increasing primacy of digital within the context of the COVID-19 pandemic and assess what’s next for the future of work.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
Digital Transformation - Is Your Enterprise Prepared☁Jake Weaver ☁
- Enterprises are undergoing digital transformation to better utilize technologies like cloud, mobile, social and big data. This requires IT organizations to take on new strategic initiatives while still handling daily operations.
- IT leaders will need to partner with managed service providers that can take over routine tasks and support advanced technologies. This will allow internal IT teams to focus on strategic initiatives that drive business value, like using big data analytics for fraud detection.
- A survey found that IT teams expect to devote more time to strategic initiatives in the next two years. They will likely rely more on managed service providers with skills in areas like cloud, data analytics, hybrid IT and security. Partnering in this way can help IT support the business needs of digital
Big Data Analytics for Banking, a Point of ViewPietro Leo
This document discusses how big data and analytics can transform the banking industry. It notes that digital transformation, enabled by big data and analytics, is creating pressures on banks from new digital native customers, large amounts of new data, new channels like mobile, and new competitors. It argues that to succeed in this new environment, banks need to build a 360-degree integrated customer view using big data, and ensure analytics are part of closed-loop business processes to create value. New applications and platforms like IBM Watson Analytics aim to make analytics more accessible and valuable to more users.
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
Utilities are starting to adopt digital technologies to eliminate slow processes, elevate customer experience and boost sustainability, according to our recent study.
IT and business leaders must increase their efforts to evolve from traditional BI tools, that focus on descriptive analysis (what happened), to advanced analytical technologies, that can answer questions like “why did it happen”, “what will happen” and “what should I do”.
"While the basic analytical technologies provide a general summary of the data, advanced analytical technologies deliver deeper knowledge of information data and granular data.” - Alexander Linden, Gartner Research Director
The reward of a smarter decision making process, based on Data Intelligence, is a powerful driver to improve overall business performance.
Wiseminer is the only and most efficient end-to-end Data Intelligence software to help you make smarter decisions and drive business results.
Contact us: info@wiseminer.com
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
The Work Ahead in Intelligent Automation: Coping with Complexity in a Post-Pa...Cognizant
Intelligent automation continues to be a top driver of the future of work, according to our recent study. To reap the full advantages, businesses need to move from isolated to widespread deployment.
Big Data: Real-life Examples of Business Value GenerationCapgemini
The document discusses examples of how companies have generated business value from big data. It provides cases of a US retail chain that realized $23 million in annual savings through a Hadoop cluster to optimize data storage. A global media company improved box office yields by rapidly correlating social media with operational data. A pharmaceutical company more effectively identified key opinion leaders among physicians to improve marketing targeting. Overall the document outlines how companies have achieved benefits like cost reduction, new revenue streams, and improved existing revenues through leveraging big data.
Digital Shift in Insurance: How is the Industry Responding with the Influx of...DataWorks Summit
The digital connected world is having an impact on the technology environments that insurers must create to thrive in the new era of computing. The nature of customer interactions, business processes from product, risk and claims management are continuously changing. During this session we will review recent research and insights from insurance companies in the life, general and reinsurance markets and discuss the implications for insurers as the industry considers implications from core systems, predictive and preventive analytics and improvements to customer experiences.
Millions of dollars are being spent annually by the insurance industry in InsurTech investments from risk listening, customer interactions (chatbots, SMS messaging, smart interactive conversations), to methods of evaluating claims (digital capture at notice of incident, dashcams, connected homes/vehicles).
These are all new types of data which the industry hasn't previously had to manage and govern.
Additionally, at the heart of this is how to create new business opportunities from data. We will also have an interactive conversation on discussing and exploring insurance implications of the new computing environment from AI, Big Data and IoT (Edge computing).
Infographic | The Growing Need for Fast, Secure TelehealthInsight
Could telehealth be the way patients are triaged in the future? Let’s explore the current landscape, the benefits of telehealth and what’s needed for it to gain widespread traction across the industry.
The Internet of Things: P&C Carriers & the Power of DigitalCognizant
The document discusses how the growing Internet of Things can impact property and casualty insurance carriers. It states that IoT sensors collecting data from connected devices can help carriers improve underwriting, pricing, risk management, loss prevention, claims handling, and customer retention. Specifically, IoT data allows carriers to better assess risk exposures, prevent losses through remote monitoring, develop new insurance products tailored to industries and risks, and create a more personalized customer experience across the entire insurance lifecycle.
This document discusses the future of big data, including predictions such as machine learning becoming prominent and data scientists being in high demand. It outlines trends like the growth of open source technologies, in-memory computing, machine learning, predictive analytics, intelligent applications, integrating big data with security and the internet of things. Challenges mentioned include dealing with large amounts of data from IoT and high salaries for data professionals.
1) Cloud computing adoption is growing in India due to rising adoption by large enterprises and SMEs seeking cost benefits over traditional data storage. However, migration challenges like legacy system compatibility and data security issues remain.
2) The market for cloud computing in India is expected to grow significantly in the coming years due to the exploding digital universe and increasing cloud adoption across both private and government sectors. However, data security, vendor lock-in, and lack of IT infrastructure in some areas may hamper growth.
3) Cloud computing provides significant benefits to organizations of all sizes by optimizing costs and increasing productivity, profitability, and business efficiency through accessible and scalable resources. However, concerns around data privacy, security
Staying ahead in the cyber security game - Sogeti + IBMRick Bouter
Cyber security is center stage in the world today, thanks to almost continuous revelations about incidents and breaches. In this context of unpredictability and insecurity, organizations are redefining their approach to security, trying to find the balance between risk, innovation and cost. At the same time, the field of cyber security is undergoing many dramatic changes, demanding organizations embrace new practices and skill sets.
Cyber security risk is now squarely a business risk – dropping the ball on security can threaten an organization’s future – yet many organizations continue to manage and understand cyber security in the context of the it department. This has to change.
This document discusses how data capture technologies like optical character recognition (OCR), intelligent character recognition (ICR), and intelligent word recognition (IWR) can help drive efficiency in the insurance industry by automating traditionally manual data entry processes. It focuses on the benefits of a hybrid human-machine approach to data capture provided by companies that combine machine learning and human intelligence. This approach can provide insurers with high-accuracy, structured digital data faster than traditional OCR alone, reducing costs while improving customer experience and enabling advanced analytics. The document advocates that insurers evaluate their current data capture methods and consider hybrid human-machine solutions to access legacy data more efficiently and utilize data to increase competitiveness.
In the insurance industry, the advantage of custom-built marts and warehouses ensures that the structure and queries match the data, but the customization makes it very difficult and expensive to maintain. On the other hand, off-the-shelf marts and warehouses maintained by the third party and are general and less useful than the custom ones. In either case, they can easily grow beyond anything manageable. This whitepaper focuses on providing an overview of data warehousing in the insurance industry.
The document discusses how IT infrastructure is changing to adapt to new business priorities in the digital age. It introduces the "HEROES" framework for the future of IT infrastructure, which focuses on hybrid cloud architectures, edge computing, robotic process automation, obsolescence of old IT, and enterprise security. Artificial intelligence will be integrated across all areas of the framework and fundamentally change how organizations procure and consume IT infrastructure over the next five years.
Value proposition of analytics in P&C insuranceGregg Barrett
The document provides an overview of the value of analytics in the property and casualty (P&C) insurance industry. It discusses the challenges facing the industry and how analytics can help insurers address these challenges. The document is divided into six sections that cover topics such as the impact of analytics across the insurance life cycle, the value of data and analytics, and considerations for implementing analytics and managing big data. Organizations that effectively adopt analytics are shown to achieve greater growth and returns. While analytics provides opportunities, the document also notes challenges such as developing a data-driven culture and addressing privacy and ethical issues that can arise from certain data collection and analytic practices.
In this edition of our Work Ahead study, we explore the increasing primacy of digital within the context of the COVID-19 pandemic and assess what’s next for the future of work.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
Digital Transformation - Is Your Enterprise Prepared☁Jake Weaver ☁
- Enterprises are undergoing digital transformation to better utilize technologies like cloud, mobile, social and big data. This requires IT organizations to take on new strategic initiatives while still handling daily operations.
- IT leaders will need to partner with managed service providers that can take over routine tasks and support advanced technologies. This will allow internal IT teams to focus on strategic initiatives that drive business value, like using big data analytics for fraud detection.
- A survey found that IT teams expect to devote more time to strategic initiatives in the next two years. They will likely rely more on managed service providers with skills in areas like cloud, data analytics, hybrid IT and security. Partnering in this way can help IT support the business needs of digital
Big Data Analytics for Banking, a Point of ViewPietro Leo
This document discusses how big data and analytics can transform the banking industry. It notes that digital transformation, enabled by big data and analytics, is creating pressures on banks from new digital native customers, large amounts of new data, new channels like mobile, and new competitors. It argues that to succeed in this new environment, banks need to build a 360-degree integrated customer view using big data, and ensure analytics are part of closed-loop business processes to create value. New applications and platforms like IBM Watson Analytics aim to make analytics more accessible and valuable to more users.
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
Utilities are starting to adopt digital technologies to eliminate slow processes, elevate customer experience and boost sustainability, according to our recent study.
IT and business leaders must increase their efforts to evolve from traditional BI tools, that focus on descriptive analysis (what happened), to advanced analytical technologies, that can answer questions like “why did it happen”, “what will happen” and “what should I do”.
"While the basic analytical technologies provide a general summary of the data, advanced analytical technologies deliver deeper knowledge of information data and granular data.” - Alexander Linden, Gartner Research Director
The reward of a smarter decision making process, based on Data Intelligence, is a powerful driver to improve overall business performance.
Wiseminer is the only and most efficient end-to-end Data Intelligence software to help you make smarter decisions and drive business results.
Contact us: info@wiseminer.com
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
The Work Ahead in Intelligent Automation: Coping with Complexity in a Post-Pa...Cognizant
Intelligent automation continues to be a top driver of the future of work, according to our recent study. To reap the full advantages, businesses need to move from isolated to widespread deployment.
Big Data: Real-life Examples of Business Value GenerationCapgemini
The document discusses examples of how companies have generated business value from big data. It provides cases of a US retail chain that realized $23 million in annual savings through a Hadoop cluster to optimize data storage. A global media company improved box office yields by rapidly correlating social media with operational data. A pharmaceutical company more effectively identified key opinion leaders among physicians to improve marketing targeting. Overall the document outlines how companies have achieved benefits like cost reduction, new revenue streams, and improved existing revenues through leveraging big data.
Digital Shift in Insurance: How is the Industry Responding with the Influx of...DataWorks Summit
The digital connected world is having an impact on the technology environments that insurers must create to thrive in the new era of computing. The nature of customer interactions, business processes from product, risk and claims management are continuously changing. During this session we will review recent research and insights from insurance companies in the life, general and reinsurance markets and discuss the implications for insurers as the industry considers implications from core systems, predictive and preventive analytics and improvements to customer experiences.
Millions of dollars are being spent annually by the insurance industry in InsurTech investments from risk listening, customer interactions (chatbots, SMS messaging, smart interactive conversations), to methods of evaluating claims (digital capture at notice of incident, dashcams, connected homes/vehicles).
These are all new types of data which the industry hasn't previously had to manage and govern.
Additionally, at the heart of this is how to create new business opportunities from data. We will also have an interactive conversation on discussing and exploring insurance implications of the new computing environment from AI, Big Data and IoT (Edge computing).
Infographic | The Growing Need for Fast, Secure TelehealthInsight
Could telehealth be the way patients are triaged in the future? Let’s explore the current landscape, the benefits of telehealth and what’s needed for it to gain widespread traction across the industry.
The Internet of Things: P&C Carriers & the Power of DigitalCognizant
The document discusses how the growing Internet of Things can impact property and casualty insurance carriers. It states that IoT sensors collecting data from connected devices can help carriers improve underwriting, pricing, risk management, loss prevention, claims handling, and customer retention. Specifically, IoT data allows carriers to better assess risk exposures, prevent losses through remote monitoring, develop new insurance products tailored to industries and risks, and create a more personalized customer experience across the entire insurance lifecycle.
This document discusses the future of big data, including predictions such as machine learning becoming prominent and data scientists being in high demand. It outlines trends like the growth of open source technologies, in-memory computing, machine learning, predictive analytics, intelligent applications, integrating big data with security and the internet of things. Challenges mentioned include dealing with large amounts of data from IoT and high salaries for data professionals.
1) Cloud computing adoption is growing in India due to rising adoption by large enterprises and SMEs seeking cost benefits over traditional data storage. However, migration challenges like legacy system compatibility and data security issues remain.
2) The market for cloud computing in India is expected to grow significantly in the coming years due to the exploding digital universe and increasing cloud adoption across both private and government sectors. However, data security, vendor lock-in, and lack of IT infrastructure in some areas may hamper growth.
3) Cloud computing provides significant benefits to organizations of all sizes by optimizing costs and increasing productivity, profitability, and business efficiency through accessible and scalable resources. However, concerns around data privacy, security
Staying ahead in the cyber security game - Sogeti + IBMRick Bouter
Cyber security is center stage in the world today, thanks to almost continuous revelations about incidents and breaches. In this context of unpredictability and insecurity, organizations are redefining their approach to security, trying to find the balance between risk, innovation and cost. At the same time, the field of cyber security is undergoing many dramatic changes, demanding organizations embrace new practices and skill sets.
Cyber security risk is now squarely a business risk – dropping the ball on security can threaten an organization’s future – yet many organizations continue to manage and understand cyber security in the context of the it department. This has to change.
This document discusses how data capture technologies like optical character recognition (OCR), intelligent character recognition (ICR), and intelligent word recognition (IWR) can help drive efficiency in the insurance industry by automating traditionally manual data entry processes. It focuses on the benefits of a hybrid human-machine approach to data capture provided by companies that combine machine learning and human intelligence. This approach can provide insurers with high-accuracy, structured digital data faster than traditional OCR alone, reducing costs while improving customer experience and enabling advanced analytics. The document advocates that insurers evaluate their current data capture methods and consider hybrid human-machine solutions to access legacy data more efficiently and utilize data to increase competitiveness.
In the insurance industry, the advantage of custom-built marts and warehouses ensures that the structure and queries match the data, but the customization makes it very difficult and expensive to maintain. On the other hand, off-the-shelf marts and warehouses maintained by the third party and are general and less useful than the custom ones. In either case, they can easily grow beyond anything manageable. This whitepaper focuses on providing an overview of data warehousing in the insurance industry.
Digitizing Insurance - A Whitepaper by RapidValue SolutionsRadhakrishnan Iyer
This document discusses how insurance companies can digitize their legacy systems to adopt modern technologies. It defines digitalization as shifting to electronic channels while balancing traditional practices. Insurers must modernize to drive growth amid rising customer expectations. Technical challenges include outdated, siloed legacy systems that are difficult to integrate and scale. The document recommends insurers establish a digital center of excellence, consult digital partners, assess technologies and identify gaps, target areas and users, and develop strategic IT initiatives to orchestrate their digital transformation.
The document discusses the key shifts underway in the insurance industry as it transitions to a digital model. Empowered consumers demanding personalized experiences, innovative competitors, and new technologies are driving insurers to move from a policy-centric model to one focused on the customer. Insurers must utilize data and analytics to develop new products that anticipate customer needs and can be purchased through any channel. They also need to build ecosystems of partners and modernize legacy systems to keep pace with these changes and remain competitive in the digital insurance landscape.
Digitizing Insurance - Transforming Legacy Systems to Adopt Modern and Emergi...RapidValue
This paper explains how insurers can use the digitization (digitalization) opportunity to deliver greater value to their customers. It is also, revealed how the companies can gain competitive advantage. Insurers are able to engage more intensely with the existing customers and also, attract newer customers with the help of innovative products. Digitizing improves profitability and facilitates growth.
In today’s globalized, competitive marketplace, being able to leverage technology to deliver faster turnaround times, meet lower pricing goals and provide customizable options can mean the difference between sustainability and irrelevancy. In this ebook, we’ll explore some of the leading solutions transforming the manufacturing industry:
- Automation for cost savings
- 3D printing for improved productivity
- Smart data for quality assurance
- Connectivity for safety and communication
- Security solutions to protect it all
Learn more: http://ms.spr.ly/6006Twegg
Being Digital, Fast-forward to the Right Digital Strategy Fabio Mittelstaedt
Do my Company have the right Digital Strategy? Is it compelling enough to beat my competitors? Or to conquer the new digital customers from millenniums to baby boomers? Competing in a world shaped by digital technologies requires a fundamentally different approach to how strategies are developed and executed. 55% of business leaders admit that they do not yet have an enterprise-level digital strategy to support their corporate strategy. But there is a difference between developing some digital capabilities or being a digital lead in your industry. Digital disrupts business strategy. Business leaders must consider a new strategic approach.
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
Exciting it trends in 2015 why you should consider shifting and upgrading yo...lithanhall
This document discusses exciting IT trends for 2015 and high demand IT career paths. It summarizes Gartner's top 10 strategic technology trends for 2015, including computing everywhere, the internet of things, 3D printing, advanced analytics, context-rich systems, smart machines, cloud/client computing, software-defined applications and infrastructure, web-scale IT, and risk-based security. It also outlines 5 in-demand IT career tracks: enterprise resource planning, systems management, business intelligence/big data analytics, technology sales and marketing, and technopreneurship. Finally, it introduces Lithan Hall Academy which provides skills training programs to help workers transition into these high-demand IT roles.
Are you exploring the best way for your business to save expenses, enhance margin, or reinvest in the coming years? Check out the top technological advancements in business that are beneficial for business expansion and that result in a technology roadmap that has an impact on a number of the organization's strategic goals.
For more information, see: https://www.albiorixtech.com/blog/technology-trends-in-business/
#technology #technologytrends #webappdevelopment #mobileappdevelopment #softwaredevelopment
This document discusses opportunities for data-driven growth and innovation. It explains that analyzing large amounts of data from various sources (i.e. big data) can provide valuable insights to create new products and services, improve efficiency, and generate new revenue streams. Specifically, it provides examples of how telecom operators can leverage network usage data and customer insights to partner with other industries and monetize consumer data while respecting privacy. Transparency around data usage is important to build customer trust.
The document discusses how digital transformation is requiring organizations to rethink their datacenter strategies and move to a more distributed approach. It notes that existing inward-focused datacenters cannot accommodate new demands for things like content delivery, real-time analytics, and long-term data archiving. To meet these challenges, the document advocates shifting to an interconnection-oriented architecture and using datacenters in optimal locations that allow for proximity to customers, partners, clouds and the network edge.
Data is poised to play an important role in the enterprises of the future, with businesses looking to scale up production and recover costs. Visit: https://www.raybiztech.com/blog/data-analytics/what-are-big-data-data-science-and-data-analytics
With enterprises putting digital at the core of their transformation, our annual Data Science & AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.
Data Science & AI Trends 2019 By AIM & AnalytixLabsRicha Bhatia
This document discusses 10 data science and AI trends to watch for in India in 2019. It begins with an executive summary noting that enterprises are putting digital technologies like AI, machine learning, and analytics at the core of their transformations. It then discusses each of the 10 trends in more detail, with quotes from experts about how each trend will impact industries and businesses. The trends include more industries utilizing analytics and AI, deploying models for real-time use cases, using data analysis for informed customer engagement, increasing investment in data infrastructure, analytics becoming more pervasive, the need for greater collaboration, personalized products, making analytics more human-centric, replacing centralized data with a single customer view, and the growth of voice and AI assistants.
Big Data Analytics in light of Financial Industry Capgemini
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
How Insurers Bring Focus to Digital Initiatives through a Maturity Looking GlassCognizant
When planning a digital initiative, it’s critical to understand where your company stands today and how it can get to where it needs to go. A new framework lets insurers assess their digital maturity, identify how best to move ahead, and gain insight into the practices of industry digital leaders to guide their own efforts.
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How Insurers Can Tame Data to Drive Innovation
1. Digital Business
How Insurers Can
Tame Data to Drive
Innovation
To thrive among entrenched rivals and compete more
effectively with digital natives, insurers will need to get their
data right. That will mean moving to more responsive, AI-
enabled architectures that accelerate data management and
deliver insights that drive business performance.
October 2019
2. 2 / How Insurers Can Tame Data to Drive Innovation
Digital Business
Data has always been an important asset in the insurance
industry, which is largely built on algorithms and models. But
today, that is truer than ever before. Data can be analyzed to
provide insurers with deeper business insights and the ability
to target the right customers through the right channels. And
it has opened the door to a growing group of digital natives
and insurtech companies.
Today, the advent of artificial intelligence (AI) is increasing the importance of data
across the industry. AI is widely recognized for its potential to bring greater efficiency
and innovation to the entire insurance lifecycle, from customer acquisition to claims
processing. But effective AI depends on large amounts of sound, timely data. AI is key to
competitiveness, and data is key to AI.
However, most mainstream insurers struggle to use data effectively. Our recent research
found that about three-quarters of insurers have low levels of digital maturity,1
and are
pursuing only limited digital initiatives or taking a wait-and-see approach to the digital
technologies needed to leverage data. Legacy systems, siloed data, and growing volumes
and varieties of data make it difficult to manage data effectively and use it to generate
improved business results.
To address the problem, insurers need to fundamentally rethink the technology
foundations that underpin their data-management efforts. While the appropriate mix of
processes and technologies will vary from insurer to insurer, three core concepts can guide
this transformation:
❙❙ Design a responsive data architecture. Simplify, augment and transform the data
landscape to leverage different types of data and quickly deliver it to AI, analytics and
business processes.
❙❙ Leverage intelligent data management. Streamline and automate data management
processes to enable the organization to rapidly create and deliver actionable information
and insights to provide better customer experiences, raise renewal rates and enable cross-
selling of insurance products.
❙❙ Enable delivery at scale. Take advantage of advanced delivery methods, such as Agile,
DevOps, DataOps and asset-based development models, to optimize and simplify data
management processes and dramatically shorten time-to-market for new capabilities.
Executive Summary
3. How Insurers Can Tame Data to Drive Innovation / 3
Digital Business
AI is not only the driver for the increased importance of data management; it is also a key
part of the solution. AI can support all three of these core concepts. It can help insurers
understand the data requirements of customers, operations and products, and accelerate
and automate a range of tasks. It can also help insurers contend with large amounts of
data by quickly assessing the value and relevance of various data sets to allow the data
organization to focus on the most valuable data sources.
To make the most of their data foundations, insurers can benefit by following seven data-
architecture design principles:
❙❙ Plan for scale and elasticity. The data architecture should enable on-demand
computations and use cloud technology to enable the organization to scale up and down
as computing requirements change.
❙❙ Build in the ability to ingest all types of data. The architecture should address different
varieties of data and largely handle data in real time.
❙❙ Be metadata-driven from the start. Metadata extraction should be more than a
compliance-driven afterthought. It should be considered early on rather than later.
❙❙ Provide open access across all layers. Platforms have three layers of data: raw, curated
and consumption — and it is vital that they are all open for access.
❙❙ Enable autonomous data integration. Machine learning (ML) can automatically detect
changes in incoming data and adjust integration patterns, helping companies integrate
new data sources quickly.
❙❙ Get feature engineering right. Feature engineering transforms data into consumable
forms and shapes used by ML, making care and precision in the process critical.
❙❙ Support a unified security model for data. A unified security approach lets companies
consider security from the point that data is produced to all points of consumption and
cycles of enrichment — which is key in today’s complex, hybrid environments.
As they rethink their approach to data, insurers can follow our seven data-architecture
design principles. At the same time, they will need to adjust the data organization’s culture,
and take a fresh look at metrics, talent and leadership — all while viewing the creation of a
new data foundation as a strategic initiative.
4. New opportunities, new challenges
Technology is changing the face of competition in the insurance
industry. Executives don’t have to look far to see digital at work.
Today’s emerging insurtech companies are using technology and
data to automate processes and operate more efficiently, and
to provide fast, innovative service to customers. Some of these
digital natives are focused on customer-facing processes –witness
Friendsurance, for example, which uses a peer-to-peer insurance
model to connect groups of customers and facilitate annual cash-
back payments when those groups remain claims-free.2
Others are
providing back-end solutions to other companies. For example,
TrueMotion offers a platform that collects smartphone data to assess
driver behavior and risk levels; this is embedded in insurers’ products,
including Progressive’s Snapshot mobile app.3
Industry executives recognize this shift. In our recent research that explored how insurers are applying
digital technology, more than 85% of survey respondents said that they are investing in their digital
agendas.4
The range of digital initiatives in the industry is impressive — but it is also deceptive. While some insurers
are moving ahead rapidly, many others are struggling to gain traction with digital transformation. Our
study found that virtually all insurers are exploring digital technology, but they vary widely in terms
of digital maturity. (See the “Measuring Digital Maturity” Quick Take.) We found that about half are
taking a limited approach, using digital at the business-unit or departmental level or, in some cases,
are just beginning to move toward an enterprise-wide approach. Another one-quarter are “dabblers,”
organizations that are essentially pursuing a wait-and-see strategy.
Insurers struggle to scale their digital initiatives for a variety of reasons, but a key is their data
capabilities — which too often are unable to keep up with growing data demands.
4 / How Insurers Can Tame Data to Drive Innovation
Digital Business
5. Measuring Digital Maturity
When developing new data strategies, insurers should make their plans within the context
of their larger digital journey. With that in mind, we developed a Digital Maturity Diagnostic
(DMD) framework that enables insurers to understand where they are in their digital
journeys. (To learn more, read our blog, “Shining a Light on Insurers’ Digital Capabilities.”)
The DMD can show an insurer where it stands today with digital maturity in relation to its
peers across the industry and in industry subsegments; identify gaps and opportunities for
improvement in their capabilities; and chart a course forward. It can also track progress as
digital initiatives are proceeding.5
Quick Take
How Insurers Can Tame Data to Drive Innovation / 5
Digital Business
The critical role of data in generating value
Large volumes of meaningful data are the raw material of the digital
revolution. Sound data management has become a key factor in the
insurance industry — especially in the use of analytics to develop
insights into market trends and customer preferences.
Insurers’ growing interest in AI is making sound data even more critical. AI has the potential to transform
everything from product development to underwriting and claims processing — and it is opening the door
to innovations such as instantly customizable life insurance and on-demand property coverage. Evan
Greenberg,6
CEO of Chubb, recently said that data, AI and analytics technology are making it possible to
transform core processes in the industry. “Imagine our ability to service (small businesses) in claims and
in underwriting,” he said. Currently, his company might ask 30 questions to underwrite a small business,
he explained, but “over the next 18 months, that’ll come down to about seven questions, because we can
scrape the answers from data that is publicly available.” In the future, with AI and technology, he added, that
might come down to two questions — name and address. This means that designing responsible, ethical AI
applications will become ever more important, as covered below.
6. In today’s AI-enabled age, the role and management of data shifts dramatically. Certainly, the insurance
industry has a long history of managing large amounts of data. Traditionally, that has meant keeping
records, administering contracts and analyzing what has happened in the business. Data has been used
to track performance. But now, it is used to drive performance. That means data needs to be treated as a
perishable asset that is gathered, understood and made available to business processes dynamically and
quickly — even in real time. Data’s traditional flow has been fairly straightforward and linear, moving from
source to data warehouse to reporting system. Now, data needs to move freely to a variety of people and
systems across the company.
The systems that most insurers have used to manage data are not up to that task. Data is typically siloed
and stored across different systems in different formats — a problem that has been aggravated by mergers
and acquisitions that leave insurers with disparate collections of legacy technologies. These problems are
further complicated by ongoing growth in the volumes and types of structured and unstructured data
that insurers have at hand. Today, they can draw on data flowing from online interactions, smartphones,
wearable fitness trackers, connected homes, video images, inspection drones, telematics devices in
vehicles, mapping and environmental satellites, and anthropological and psychological profiles of
customers, to name a few sources. Not surprisingly, in our survey of insurance executives in the U.S. and
Europe, the most commonly cited obstacle to the successful implementation of AI-driven capabilities
in the business functions was a lack of accurate and timely data.7
Insurers need to rethink the way they
manage data, and create new data foundations.
6 / How Insurers Can Tame Data to Drive Innovation
Digital Business
Insurers’ growing interest in AI is making sound data
even more critical. AI has the potential to transform
everything from product development to underwriting
and claims processing — and it is opening the door
to innovations such as instantly customizable life
insurance and on-demand property coverage.
7. How Insurers Can Tame Data to Drive Innovation / 7
Digital Business
A strategic approach to the new data foundation
Building a new data foundation should be approached as a strategic
initiative sponsored by executives and business teams, rather than
technology and data architecture teams. It should look beyond point
solutions, fragmented programs and the idea that the company needs
to create another large, monolithic platform.
Instead, companies should adopt a structured approach to transforming the ways they source, interpret
and consume data to consolidate disparate data sources and support data modernization (see Figure 1).
This more flexible and loosely coupled architecture uses “fit for purpose” storage, compute and distribute
strategies, while leveraging the power of ML to accelerate the process of drawing actionable insights from
data. (See Quick Take, page 10.)
Modern
Data
Next Best Actions
Omnichannel
Experience
Augmented Reality /
Virtual Reality
Hyperpersonalization
Intelligent
Decisions
Process Image,
Video, Text
External Data
Acquisition
Expose as-a-service
Modernization
Data Storage
Remote Action
Drone Data
Digital Apps
Local
Regulations
Prescriptive Analytics
Encryption
Real-Time Data
Integration
IoT
Integration
Digital
Experience
Intelligence
Insight
Security
& Privacy
Multi-Cloud
Strategy
Automation
& APIs
Figure 1
Data modernization addresses the tsunami of opportunities
8. Three core concepts can offer a valuable framework for insurers as they shape new approaches
to their data:
❙❙ Employ a responsive data architecture. Data architectures are often rigid and hardwired, and built
around large, relatively inflexible data warehouses. This makes it difficult to bring in new and varied types
of data and use it to develop insights — a critical ability in digitally enabled operations. Next-generation
data architectures can simplify, augment and transform the data landscape to enable insurers to draw
in different types of data and quickly deliver it to AI and analytics applications and business processes.
For example, our Customer 3608
platform can sift unstructured data from local governments to help
business teams understand household composition and risk characteristics to find coverage upselling
opportunities. This can be done without having to involve the IT team to integrate such data sets, which
might take weeks or months. That means that insurers can more easily experiment with the data to
uncover new insights.
❙❙ Leverage intelligent data management. Traditional data management processes are not designed
to handle dynamic data and changing business demands. The management of metadata, data quality,
security and regulatory compliance are labor-intensive processes that often can’t keep up with
changing data sources and applications. Insurers can address that shortcoming by streamlining and
automating many of those processes — especially time-consuming manual tasks, such as reconciling
entries in multiple data sources. This can enable an organization to more rapidly tap its data stores to
create and deliver actionable information and insights. It can also help companies respond to change.
For example, special investigation units can be alerted in real time when there are anomalous patterns
of interactions in an open claim, or when more data is added or changed by claimants, providers,
employees or adjusters.
❙❙ Enable delivery at scale. The processes used to develop and modify data management systems have
not leveraged the advances that have revolutionized application development — which limits the ability
to change and improve. Insurers can take advantage of advanced delivery methods, such as Agile,
DevOps and DataOps, to optimize and simplify processes.9
Asset-based development models can
enable standardization and the efficient reuse of solution components. And continuous integration/
continuous delivery techniques can help ensure that new capabilities are easily and quickly included
in systems. These types of approaches can dramatically reduce time-to-market for new capabilities —
and, in effect, enable the data organization to release them almost continuously. For example, Uber can
support millions of weekly analytical queries.10
Without being able to deliver insights at scale, it will be
difficult for insurers to use data to flexibly launch new capabilities and products.
It’s important to recognize that AI is not only a key reason that many insurers need to enhance their data
foundation, it can also play a vital role in making that possible. For example, in designing and creating
a responsive architecture, bots and natural language processing can be used to sort through data to
understand customers, operations and products to help determine what insights and data the architecture
needs to deliver. Or, AI can automatically scale environments to optimize performance and cost, configure
backups, and monitor and manage workloads and resources.
8 / How Insurers Can Tame Data to Drive Innovation
Digital Business
9. How Insurers Can Tame Data to Drive Innovation / 9
Digital Business
AI can also be used to automate processes such as managing metadata and data quality throughout
the data management lifecycle, helping to make operations “self-driving and self-healing.” In ongoing
operations, it can be used to automate the integration of data with applications and databases, generate
data models and deliver data to downstream applications, external systems and end-user reports.
In an era when insurers are inundated by data, AI can also help them focus on the right data – that is, on
the data that has the highest business value. For example, we developed an AI-based framework called
DataIQ11
that performs an up-front assessment of various data sets to determine which attributes are
relevant and will provide the intelligence needed to support a given purpose — say, improving the insurer’s
claims-loss ratio. This allows engineers to home in on delivering what the business requires.
Meanwhile, when new types of data emerge, ML can be used to quickly experiment with different uses of
that data to determine if it has potential value. This helps the data management organization to perform a
kind of early triage on potential data sources, before applying the rigorous controls and quality-assurance
processes required when data is included in the mainstream data flow.
As they apply this framework, insurers will need to make sure that AI is “responsible” — that it does not make
inappropriate or biased decisions that could limit its value and create significant damage to a company’s
reputation and shareholder value. Insurers will need to establish policies and procedures to ensure that
their AI applications act ethically, and create AI ethics officers to oversee the design and ongoing operation
of AI technology. (See “Making AI Responsible — And Effective” for more insights.)
Overall, this framework can be used to create a flexible but industrialized approach to managing data —
taking it in, processing and analyzing it, and rapidly delivering actionable information and insights to the
business. Data can be delivered as a “product” tailored to processes and users across the company to drive
innovation and efficiency.
In designing and creating a responsive architecture, bots
and natural language processing can be used to sort through
data to understand customers, operations and products
to help determine what insights and data the architecture
needs to deliver. Or, AI can automatically scale environments
to optimize performance and cost, configure backups, and
monitor and manage workloads and resources.
10. 10 / How Insurers Can Tame Data to Drive Innovation
Digital Business
Data Fuels Growth in Small
Business Market Segment
A leading global P&C insurer wanted to use insights to grow its small commercial lines
business by targeting and reaching more small business owners. However, the company
had grown through a series of mergers, leaving it with siloed data. For this client, integrating
data to support customer-focused insights was a challenge.
We helped the carrier build a responsive data architecture using both the cloud and the
company’s existing on-premises systems. The architecture brought together data from
the company’s contract engines and customer-service and billing systems, as well as from
external third-party data providers, to gather insights on millions of small businesses in
the U.S. With the new responsive data architecture in place, the client has access to the
integrated data of small businesses, which has helped speed its quote and bind processes.
For example, with its previous approach, the insurer asked business owners to fill out
lengthy questionnaires covering basic information such as number of employees, when the
business was started, etc. Now, the system draws on external sources to access hundreds
of data points for millions of small businesses in the U.S., which are instantly available when
an owner applies for a policy. Owners have to answer only a handful of questions, with the
system automatically providing the rest of the information.
With better access to more sources of integrated data, the client can quickly price
policies, offer discounts to good prospects and even send pre-approved offers when
appropriate. It can also use data to identify customers that warrant retention efforts and
identify the agents who are most effective at selling insurance to specific types of business,
such as restaurants or home maintenance companies. The insights from data also
position the insurer to act as an advisor to help small business owners run their firms
safely and efficiently.
Quick Take
11. How Insurers Can Tame Data to Drive Innovation / 11
Digital Business
Principles for moving forward
Each company will have its own needs and goals when rethinking its
approach to data. But we have found that seven key principles can
guide the development to a new, more adaptive data foundation that
is ready for AI. As they work on data architectures, insurers should
consider these principles:
❙❙ Plan for scale and elasticity. The data architecture should enable the on-demand performance
of computations; allow the business to use data without needing to check with IT; and use cloud
technology to enable the organization to scale up when additional computing horsepower is needed —
and scale down when it isn’t.
❙❙ Build in the ability to ingest all types of data. The architecture should address different shapes and
granularities of data such as transactions, logs, geospatial information, sensors and social — and handle
data in real time as much as possible.
❙❙ Be metadata-driven from the start. Most enterprises view metadata extraction as an afterthought,
typically driven by compliance. However, metadata is much easier to manage early in the process rather
than later, and it has value far beyond compliance. For example, by cataloging the company’s metadata,
companies can create a library of data sets that everyone in the organization can access, thereby
enabling wider use of insight-generation and AI throughout the enterprise.
❙❙ Provide open access across all layers. Platforms have three layers of data: raw, curated and
consumption. Traditional architectures typically grant access only to the consumption layer. However,
data scientists often like to examine raw data for overlooked elements that may generate more
information — so it’s important that all the layers are exposed and open for access.
❙❙ Enable autonomous data integration. Companies will need to integrate new data sources quickly in
order to keep relevant data flowing to analytics and AI applications. However, mapping data to target
usage environments is still a largely manual process. That can be addressed by using ML to automatically
detect changes in incoming data and adjust integration patterns.
❙❙ Get feature engineering right. Feature engineering transforms data into consumable forms and
shapes that ML models can use. Features describe data points and serve as inputs into the learning
system, so they need to be as precise as possible. Careful feature engineering is key to making ML
accessible broadly within the business.
❙❙ Support a unified security model for data. Companies often rely on complex, hybrid environments
that blend cloud-based and on-premises services, with data scattered in various locations and used by a
variety of individuals and systems. A unified security approach lets companies consider security from the
point that data is produced to all points of consumption and cycles of enrichment.
12. Looking ahead: Transforming the data organization
Insurers need to rethink the technology they use to manage data.
But they also need to bring new approaches to the data organization
itself — approaches that will help them make the most of their
technology foundation.
For example, most insurers need to change the culture of the data organization — to give it a mindset that
is focused on driving innovation and enabling the business, rather than executing technical tasks. This can
start by changing the way the organization is measured. Today, data organizations are typically assessed
on how efficiently they complete specific tasks. Do batch processes run as planned? Are sales reports
complete and delivered on time each morning? Instead, they should be measured on the value that they
deliver to the business, such as how well they support the creation of new services and new customer
experiences.
A shift in talent strategies can also support that new mindset. Most employees in data organizations have
come out of the traditional data management world — one designed for tracking performance, rather than
driving performance. Insurers can provide training and development to their current data professionals.
But they may also need to complement traditional staff with new types of employees — people who have
experience in using technology to support business innovation, or even those who come from the business
itself. That practice should extend to the leadership level as well.
Insurers should consider having data organization leaders who have business rather than IT backgrounds.
Without leadership that is focused on innovation and transformation, the data organization is likely to
simply continue with business as usual.
Above all, architectures should be purpose-built to support business strategy. In designing and operating
new data foundations, engineers should keep a sharp focus on the big picture — understanding the data
needs of customers, operations and products, and using those insights to deliver business outcomes. With
that approach, data organizations will be able to meet the needs of the business. Just as important, they
will also be positioned to help work closely with the business to proactively explore new ways of using data.
Today, data is a key enabler of the business — and with the right data foundation, the data organization can
play a pivotal role in delivering a competitive advantage.
12 / How Insurers Can Tame Data to Drive Innovation
Digital Business
13. How Insurers Can Tame Data to Drive Innovation / 13
Digital Business
Data organizations are typically assessed on how
efficiently they complete specific tasks. Do batch
processes run as planned? Are sales reports complete
and delivered on time each morning? Instead, they
should be measured on the value that they deliver to
the business, such as how well they support the creation
of new services and new customer experiences.
Endnotes
1 “How Insurers Bring Focus to Digital Initiatives through a Maturity Looking Glass ,” Cognizant, www.cognizant.com/
whitepapers/how-insurers-bring-focus-to-digital-initiatives-through-a-maturity-looking-glass-codex4447.pdf.
2 www.friendsurance.com/.
3 https://gotruemotion.com/.
4 “How Insurers Bring Focus to Digital Initiatives through a Maturity Looking Glass,” Cognizant, www.cognizant.com/
whitepapers/how-insurers-bring-focus-to-digital-initiatives-through-a-maturity-looking-glass-codex4447.pdf.
5 For more information on the importance of digital maturity for insurers, please see our paper, “How Insurers Bring Focus
to Digital Initiatives through a Maturity Looking Glass,” www.cognizant.com/whitepapers/how-insurers-bring-focus-to-
digital-initiatives-through-a-maturity-looking-glass-codex4447.pdf.
6 www.insurancebusinessmag.com/us/news/technology/how-big-data-is-changing-insurance-to-predict-and-prevent--
chubb-107465.aspx.
7 “The Insurance AI Imperative,” Cognizant, www.cognizant.com/whitepapers/the-insurance-ai-imperative-codex4307.pdf.
8 www.cognizant.com/customer-360-digital-banking-platform.
9 DevOps and DataOps methodologies automate and accelerate processes associated with software development and data
analytics, respectively.
10 Luyao Li, Kaan Onuk and Lauren Tindal, “Databook: Turning Big Data into Knowledge with Metadata at Uber,” Uber
Engineering, Aug. 3, 2018, https://eng.uber.com/databook/.
11 www.cognizant.com/Resources/cognizant-adaptive-data-foundation-offering-overview.pdf.
14. Digital Business
14 / How Insurers Can Tame Data to Drive Innovation
Chris Blatchly
Chief Digital Officer and Insurance Consulting Lead,
Cognizant Digital Business
Chris Blatchly is the Chief Digital Officer and Consulting Leader for Insurance at
Cognizant. Chris helps insurers harness the power of new technologies and the
information it creates to build their capabilities and transform their businesses.
As a former consulting partner, software company business unit leader and large
company IT executive, he has a unique perspective on technology strategy and
executing process-driven business transformation. Chris has a deep background in
insurance and financial services, and he has often been in the forefront of working
with the latest technologies and successfully implementing them for his clients. He holds an MBA in marketing
from the University of Toronto, a master’s degree in economics from Western University and a bachelor’s degree in
economics from Trent University. Chris can be reached at Christopher.Blatchly@cognizant.com | www.linkedin.com/
in/chrisblatchly-3981a39.
Ajish Gopan
Insurance Business Partner & Commercial Lead, Cognizant AI &
Analytics Practice
Ajish Gopan is an Insurance Business Partner and the Commercial Lead within
Cognizant’s AI & Analytics Practice. An insurance industry expert, Ajish focuses
on applying AI and analytics technology as well as data-driven insights to solve
business problems. He has over 20 years of industry experience solving highly
specific insurance industry core business challenges using advanced AI and
analytics. Prior to Cognizant, Ajish led analytics for New York Life in Asia and
consulted with Aviva, Zurich and other companies in the European region, including
those associated with the Lloyds Syndicates. He currently helps insurance clients in North America to leverage data
and insights to help them achieve transformational business outcomes as well as pivot to digital. His teams across
North America deliver a varied spectrum of projects that span AI and analytics, deep learning, business intelligence,
master data management, data integration, data governance, data lakes and data modernization, as well as cloud
migration. Ajish has an MBA from the Indian Institute of Management, Bangalore, and a bachelor’s degree in
technology from the Indian Institute of Technology, Chennai. He can be reached at Ajish.Gopan@cognizant.com |
linkedin.com/in/ajishgopan.
About the authors
15. How Insurers Can Tame Data to Drive Innovation / 15
Digital Business