Most financial service firms, which includes banking and insurance companies, are engaged in a big data project to increase the pace of innovation and uncover game-changing business outcomes. The pressing challenge now is how to drive more continuous value and unearth opportunities more rapidly.
No matter where you might be in your big data journey, the following three-step approach to integrating big data into an analytics strategy can lead to success:
Step One: Outline business objectives and outcomes
To drive continuous and transformational improvements through big data-driven analytics projects, business units IT, marketing, risk, compliance or finance, for example should agree on and outline a mutually beneficial business objective. For instance, driving a better customer experience or improving customer value management. While developing the common objective, financial services firms should also determine the aligned and desired outcomes, such as decreasing fraud and offering more personalized services to customers in real-time.
Once business outcomes have been determined and prioritized, the firm can then decide on the best big data technologies needed to modernize their enterprise infrastructure to better mobilize the data across the business for consumption, enabling it to realize the desired outcomes. It's also important for the business units to decide on how the analytics activity will be tracked to make sure the project is on a path to success.
Step Two: Understand the environment and drive innovation
As desired outcomes are established, banks and insurance companies should seek opportunities to accelerate how they leverage their data to drive optimal value. For example, cloud enabled analytic service environments powered by big data technologies can shorten the previously lengthy technology and business planning cycles.
These environments can not only be used to discover hidden insights rapidly, but they can also be used to, for instance, help a company more deeply understand how these transformative technologies can best work in their enterprise and model how analytic services can be managed and operated across the enterprise. When pursuing this approach, a firm can determine, in an agile and innovative way, how their data and non-traditional technologies can come together to transform the enterprise into a fierce data-driven competitor.
Traditionally, financial services companies only used transactional data such as customer payment and deposit data, but today they can analyze the transactional data along with interaction data such as online, call center, and even social media data. When looking to analyze and uncover insights from the new data types and sources, firms may discover they have a gap in their technology infrastructure that would allow them to manage the new data to reach the specified business objective. As a solution, companies should look to build a hybrid technology environment this can be created by adding emerging technologies such as Hadoop to an established technology infrastructure. As a result, data can be quickly mobilized and analyzed in a cost-effective and timely fashion to chase the business outcome.
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