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June 28, 2016

[Part 2 of a series on effective data management strategies; part 1 examines the benefits of a holistic approach to data management.]

Role of a Chief Data Officer

A successful analytics and data strategy cannot be purely an IT initiative. An entirely IT-driven data strategy wont go anywhere in terms of value it will result in fragmented solutions. Thats why insurers require data specialists who understand not only how to leverage the data using analytics, but who also can connect the data to business outcomes. Specifically, they need Chief Data Officers.

Any organization that appoints data officers demonstrates that it is serious about using data as a strategic asset and finding new ways to leverage data for a number of purposes. In order to successfully leverage data for competitive advantage, insurers need to look at where they want to end up and then work backwards to make that happen. They begin by identifying their desired business outcome and then determine how the data can help achieve their goals. Then, they need to consider adopting new technologies as a next step. This is a dramatically different method than the traditional IT approach, which looks at how technology can enable transformation and help an insurer be more competitive, increase customer satisfaction or achieve efficiencies, for instance. And the different approach is driven not by CIOs, but by Chief Data Officers (CDOs), whose job it is to align all the data assets to support business decisions.

A good insurance chief data officer has a combination of experience. For the most part, those in this position have grown within the insurance industry. They understand the business processes and challenges and how data can help solve those challenges. Unless someone understands insurance, and how it works, its very difficult to align those initiatives.

Chief Data Officer Vs Data Scientists

Chief Data Officers are different from Data Scientists, another title that is showing up more frequently at insurance companies. Data officers have broad responsibilities around data, including how data is aggregated, managed and stored. The CDO is also responsible for the infrastructure supporting the data analytics initiatives and data security, and for ensuring that data-related efforts support business objectives.

Data scientists, meanwhile, focus on analytics and how to use data with different techniques (including statistical analysis) to exploit its value and provide insights to various stakeholders in the organization. They understand particular parts of the business and know insurance processes very well. For example, with a good understanding of what underwriting is and what an underwriter or actuary does, a data scientist can look at the data that is available from an insurers enterprise systems and external data sources, bring the data together, analyze it, and suggest a better way of supporting the underwriter function. Data scientists are focused on analytics and algorithms and how to find relationships in the data and use these techniques to derive insights.

It is imperative for insurers to understand the importance of these roles, it can go a long way in helping you to effectively and efficiently exploit the value of data.

Pankaj Sinha heads the Information Management, Analytics and Big Data practice for the Insurance and Healthcare unit at Tata Consultancy Services. He has over twenty years experience in the information technology industry with expertise spanning strategy, blueprint, roadmap, planning, architecture and implementation of complex business systems. Over the years he has led several consulting engagements and large-scale implementations of information management, analytics, and CRM solutions. He works with leading industry partners and research organizations to build innovative solutions in the data and analytics space. He has been advising several insurance and healthcare organizations on enterprise data and analytics strategy, helping them effectively leverage data as an asset for business growth, customer insights and operational efficiencies.


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