Data 2020: Do more with less

Despite increasingly stringent data privacy regulations and compliance requirements, data privacy and data intelligence can coexist

The General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) in the United States, and the growing list of global consumer data protection regulations are putting new pressure on financial institutions. These data restrictions, combined with the growing reluctance of increasingly well-informed consumers, are complicating the process of acquiring data, making it all the more difficult to derive valuable intelligence. Balancing data privacy and data intelligence has become a digital house of cards — one that’s fragile, to say the least, and an issue that the financial sector is struggling to navigate.

Today’s leading organizations that have a lot to answer for. Voracious and non-strategic collection of data from all customer touchpoints has created vast lakes of decaying data that offer far fewer insights than originally promised.  Meanwhile, the cost of managing that data is rising. On top of the expense of storing and analyzing the data and complying with new regulations, organizations must hire highly skilled talent.

Beyond the complexity of data management is the necessity to glean actionable intelligence from the data. For big data acquisition to be worth the complexity it introduces, organizations need to get real value from their efforts. Instead, most companies are still just collecting as much data as they can, and hoping it will give them the intelligence they need.

Understandably, companies want to know as much as possible about the customer, so they can create targeted,  personalized experiences and stand out in a crowded market. Yet customers are often averse to the idea that those companies are keeping a record of their every thought and action. This tension is creating a rift in the business-customer relationship.

This has to change.

For companies in the financial sector to benefit from data while addressing consumer privacy concerns, complying with regulations and all the while providing a frictionless customer experience,, a less-is-more approach is necessary.  The view that ‘more’ data equals ‘more’ intelligence isn’t always true. In fact, simply collecting vast quantities of data with no strategy in place for how to use it can put both the company and the customer at risk.

Without a plan for how to manage and leverage data effectively to produce actionable intelligence, companies can open the door to data leaks, breaches, fraud, and security vulnerabilities.  Organizations that fall victim to such attacks face reputational damage, regulatory punishment, consumer backlash, and — at worst — financial collapse.

Finding the Forest Amidst the Trees

To truly benefit from data while achieving compliance, banks must focus on collecting data that’s highly relevant and using it to extract insights that are smarter, more focused, and targeted.  Although this may sound counter-intuitive, less data, when processed correctly can deliver improved insights into customer behavior and engagement, enabling a financial financial institution to take the necessary steps to differentiate and disrupt.

The In “Centralizing Intelligence with Advanced Feature Engineering,” Dr. Yinglian Xie, cofounder and CEO of DataVisor, examines the best platform and approach to drive deeper data intelligence and results. The ebook focuses on how companies can leverage relevant data through features engineering, an essential process for building advanced  machine learning models. Leveraging this approach, you can analyze aggregated datasets with precision. For example, you can calculate the total number of transactions processed from a particular device where the amount of each transaction exceeds a specified threshold. This approach offers significant insight into business and customer behavior — and you don’t need individual transaction amounts to see the benefits.

Holistic data analysis at the group level enables financial institutions to unlock trends, patterns, and commonalities and uncover actionable insights that wouldn’t be visible at an individual level. Derived data can also determine multiple additional features from one single data point for even richer intelligence. With these techniques, companies can better align their efforts with evolving big data regulations, while improving the quality and depth of the data.

Leaders and innovators in the banking sector are under far greater regulatory pressure than many other industries, and, as a result, must harness technologies such as machine learning and artificial intelligence to get more value with less data. To this end, a new approach to data analysis that reduces risk while maximizing data efficiency and customer acceptance emerges. This enables organizations in the financial services industry to balance ethics and regulatory compliance with intelligence to create cohesive solutions that maximize results.

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