White Paper: MarkLogic
''Big data analytics in insurance industry” has played an important role in managing their business processes. Today, carriers and intermediaries are engaged in improving data capture to help them to better manage their business, manage their risk and know their customers.
As the business and regulatory drivers are pushing the industry to manage its data better, it’s become necessary for firms to adapt big data analytics in insurance industry.
This whitepaper discusses the implications, characteristics, and benefits of the new data management era in Insurance, paying particular attention to specific use cases driving new technology trends. It will also explore the capabilities of both new and traditional data management technologies within this context.
What big data can offer organizations in their businesses and how these new data management technologies interact with their existing database systems?
How you should look to benefit from new technologies associated with big data in your insurance industry?
Where these new technologies associated with big data can provide value beyond the core transaction processing associated with policy administration and claims management?
Which big data technologies are available there in market that can be used in the insurance industry?
How the new technology is providing specific strategic benefits to insurance industry?
By: Kavi Global
Analytics strategy has to be very closely aligned with the business strategy as business transformation is not an isolated activity. An analytics strategy can identify the gaps in terms of organization and people, processes, technology and data are and proactively address them to be successful. So, what really is an analytics strategy? The key to success is a clearly defined vision and an analytics strategy. Whether you are planning an enterprise wide analytics program to transform the business or an analytics initiative that would benefit a single department, a well-planned Analytics Strategy can make all the difference by proactively addressing the reasons for the failures. Are you struggling to bring analytics to the business strategy?? Then read this whitepaper that explores enterprise data analytics strategy, best practices, risks associated and how to mitigate them. It highlights: How to successfully execute an enterprise analytics strategy? Where in the business processes can analytics strategy be applied? What are the quantifiable benefits of doing so? What are the best known strategies to prepare data for analytics? What are the keys to develop a successful analytics strategy?
With technologies like Customer 360 and Mobile Device Management (MDM), you can obtain an accurate assessment of customers by extracting customer details from all possible data sources. Customer data management is becoming more complex than ever. Hence, it is recommended to choose the right technology for the most comprehensive view of the customer and to integrate the data within the various technologies. Read more about MDM and Customer 360 in this report that includes: 1. The Purpose of MDM and Customer 360. 2. The Benefits of Customer 360. 3. The biggest challenges in developing a 360-degree view of the customer. 4. The Challenges of Unstructured Interaction Data.
What is Big Data ?
Big data is the act of collecting huge amount of enterprise data that can be used for future analysis. Big data helps all kind of industry data to grow securely that includes government, banking, retail, education and healthcare. Big data stores all kind of data including structured, unstructured and semi structured data, which contains valuable information about core functions of an enterprise such as finance, marketing, procurement.
What is Risk Management ?
Risk management is the way of identifying, measuring and dealing with the threats to an organizations capital and earnings. Definition according to ISO 31000 Risk management is the way toward assessing the chance of loss or damage and finding a way to battle the potential Risk.
What is Data Management ?
Data management is the development and execution of policies and procedures in order to manage the information lifecycle needs of an enterprise ensuring the accessibility, reliability, and timeliness of the data for its users. Data Management enables organizations and enterprises to use data in: Organizing the enterprise data, Storing and preserving data for future re-use, Making data ready to use anytime, Share data with colleagues