Page 134 - Building Big Data Applications

132 Building Big Data Applications

Investments

Growth

Revenue

Customers
Bank

Security

Transparency

Mobile Wallet

Credit Cards
FIGURE 7.2 Predictive analytics and big data in banking.

leverage the immense mountains of data assets to develop new business models. The
strategy to do this is by monetizing multiple data sources both data-in-motion and data-
at-rest for actionable intelligence.
Data is the single most important driver of customer transformation, impacting
financial product selection, promotion targeting, next best action and ultimately, the
entire consumer experience. Today, the volume of this data is growing exponentially as
consumers increasingly share opinions and interact with an array of smart phones,
connected devices, sensors, and beacons emitting signals during their customer journey.
Business and technology leaders are struggling to keep pace with a massive glut of data
from digitization, the Internet of Things, machine learning, and cybersecurity.
To minimize the negative downside of data analytics and deliver better results, we
today have big data-driven predictive analytics. The Hadoop platform and ecosystem of
technologies have matured considerably and have evolved to supporting business crit-
ical banking applications. Align this with the ability to integrate cloud computing, mobile
and device-driven engagement models and social media integration, we will create new
opportunities and leverage the analytics to the best extents possible.

Positively impacting the banking experience requires data, which is
available and we need to orchestrate models of usage and analytics to
leverage the data
Retail and consumer bankingdBanks need to move to an online model, providing
consumers with highly interactive, engaging, and contextual experiences that span