Despite increasingly stringent data privacy regulations and compliance requirements, data privacy and data intelligence can coexist
The General DataProtection Regulation (GDPR) in the European Union, the California ConsumerPrivacy Act (CCPA) in the United States, and the growing list of globalconsumer data protection regulations are putting new pressure on financialinstitutions. These data restrictions, combined with the growingreluctance of increasingly well-informed consumers, are complicating theprocess of acquiring data, making it all the more difficult to derive valuableintelligence. Balancing data privacy and dataintelligence has become a digital house of cards — one that’s fragile, to saythe least, and an issue that the financial sector is struggling to navigate.
Today’s leading organizations that have a lotto answer for. Voracious and non-strategic collection of data from all customertouchpoints has created vast lakes of decaying data that offer far fewerinsights than originally promised. Meanwhile, the cost of managing that data is rising. On top of theexpense of storing and analyzing the data and complying with new regulations,organizations must hire highly skilled talent.
Beyond the complexity of data management isthe necessity to glean actionable intelligence from the data. For big dataacquisition to be worth the complexity it introduces, organizations need to getreal value from their efforts. Instead, most companies are still justcollecting as much data as they can, and hoping it will give them theintelligence they need.
Understandably,companies want to know as much as possible about the customer, so they cancreate targeted, personalizedexperiences and stand out in a crowded market. Yet customers are often averseto the idea that those companies are keeping a record of their every thoughtand action. This tension is creating a rift in the business-customerrelationship.
This has to change.
For companies in the financial sector tobenefit from data while addressing consumer privacy concerns, complying withregulations and all the while providing a frictionless customer experience,, aless-is-more approach is necessary. Theview 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 touse it can put both the company and the customer at risk.
Without a plan for how to manage and leveragedata effectively to produce actionable intelligence, companies can open thedoor to data leaks, breaches, fraud, and security vulnerabilities. Organizations that fall victim to suchattacks face reputational damage, regulatory punishment, consumer backlash, and— at worst — financial collapse.
Finding the Forest Amidst the Trees
To truly benefit from data while achievingcompliance, banks must focus on collecting data that’s highly relevant andusing 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 customerbehavior and engagement, enabling a financial financial institution to take thenecessary steps to differentiate and disrupt.
The In “Centralizing Intelligence withAdvanced Feature Engineering,” Dr. Yinglian Xie, cofounder and CEO ofDataVisor, examines the best platform and approach to drive deeper dataintelligence and results. The ebook focuses on how companies can leveragerelevant data through features engineering, an essential process for buildingadvanced machine learning models.Leveraging this approach, you can analyze aggregated datasets with precision.For example, you can calculate the total number of transactions processed froma particular device where the amount of each transaction exceeds a specifiedthreshold. This approach offers significant insight into business and customerbehavior — and you don’t need individual transaction amounts to see thebenefits.
Holistic data analysis at the group levelenables financial institutions to unlock trends, patterns, and commonalitiesand uncover actionable insights that wouldn’t be visible at an individuallevel. Derived data can also determine multiple additional features from onesingle 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 sectorare under far greater regulatory pressure than many other industries, and, as aresult, must harness technologies such as machine learning and artificialintelligence to get more value with less data. To this end, a new approach todata analysis that reduces risk while maximizing data efficiency and customeracceptance emerges. This enables organizations in the financial servicesindustry to balance ethics and regulatory compliance with intelligence tocreate cohesive solutions that maximize results.