Forbes recently ran an article that suggests, based on a Stanford study, that while consumers generally value data privacy, they still choose Amazon-like convenience and Netflix-like personalization over that privacy. The writer reasons, since banks already have mandated protections in place to protect data privacy, they need to start putting more of that data to work toward improving the customer experience.
He says they can start with more effective targeted marketing, providing small businesses with anonymous competitive benchmarking data, and alerting customers when they’ve crossed certain spending lines. But that’s really just the beginning. Banks have access to all kinds of data. Whereas they traditionally had to cold-sell financial products to people, regardless of whether they needed them, now with predictive analytics they can make informed decisions about who to target, which products and services to target them with, as well and when and how to target them.
And that’s just some of the ways banks can convert data into more value for the customer. There’s also putting the various streams of data to work toward improving operations, security and so much more.
This is where Waterline Data’s AI-driven data cataloging solutions come in, helping financial customers like Fannie Mae, Nordea and CreditSafe automatically tag and match the large streams of incoming data to glossary terms that in-house data analysts can then use to generate the knowledge to cater to customers in new and more effective ways. Without such automated data cataloging, all the data that’s pouring in simply continues to pile up, unused.
In fact, Waterline’s newly-unveiled AI-driven Data Catalog 5.0 adds to our industry-leading ability to catalog individual data items—automatically and at scale—by allowing organizations to discover and work with related data sets across the enterprise. This provides for far deeper and cleaner insights for analytics than any other catalog can provide.
You can invest all you want in faster data processing, faster analytics and faster response times. But if a financial organization can’t discover, understand and utilize their data fast enough—if it can’t quickly convert all that data into actionable business intelligence—it will have a tough time providing customers with the highly personalized service they’ve come to expect in today’s big data era. It will mean the difference between success and failure.