https://padlet.com/deepshikhabhargava/6exz0zezf5kwolue Background This industry top-50 property and casualty insurance carrier wanted to adopt a more data-driven, customercentric business model that would enable them to improve customer acquisition, engagement and retention while maintaining growth and profitability in an increasingly constrained and competitive environment. They needed to better understand their customers from a historical and transactional perspective so that their actuarial and analytics departments could design optimal policies, set premiums and apply discounts, manage investment risks, identify up-selling and cross-selling opportunities, predict customer behavior and increase customer satisfaction. Many of their existing systems were solution-oriented, and data assets were spread across departments — including IT, actuarial, finance and operations. To achieve their goals, they needed to align their existing systems, integrate data and implement new technologies and processes that would allow them to govern, manage and access data at an enterprise level. Challenges Traditionally, this industry is not used to customer-centric approaches. Nor does it have a history of applying data governance (DG) and data management (DM) concepts to gain better insight into their customers. So it is not surprising that line-of-business executives were skeptical, at best, about how DG and DM concepts could help them gain better insight into their market and achieve both department and corporate business objectives. At worst, it was felt this solution might add little more than extra processes and burdens to already busy departments. Individual departments operated in silos, with separate systems or applications that made it dicult to gain a comprehensive understanding of the customer. The way data was defined, entered and calculated often varied by department, so reports and information could be inconsistent, inaccurate or even contradictory en-us 2020-09-01 08:18:50 UTC 2021-09-09 09:49:17 UTC hello@padlet.com https://padlet.com/deepshikhabhargava/6exz0zezf5kwolue/wish/1727550535 We have divided it in three phases:
1. Assessment
First we'll assess the  data management capabilities of company.
2.Data Governance 
Next we'll define data governance road map
3.Implementing the all the strategies we have made.]]> 2021-09-09 09:22:32 UTC https://padlet.com/deepshikhabhargava/6exz0zezf5kwolue/wish/1727550535 https://padlet.com/deepshikhabhargava/6exz0zezf5kwolue/wish/1727557555 the company would have to focus on its data collection and then analytical teams to see furthermore and we see that they can be very confused about using the new world techniques. but the company will be benefitted once they get all the information of individual preferences]]> 2021-09-09 09:26:37 UTC https://padlet.com/deepshikhabhargava/6exz0zezf5kwolue/wish/1727557555 https://padlet.com/deepshikhabhargava/6exz0zezf5kwolue/wish/1727561472 1. they can evaluate the company’s data management capabilities, data quality and challenges.
collect and compare the information collected.
2. identify data governance responsibilities and provide training, provide preliminary training to DG supervisors
3. define data management business and implement the plan.]]> 2021-09-09 09:28:52 UTC https://padlet.com/deepshikhabhargava/6exz0zezf5kwolue/wish/1727561472