7 Realities of Developing Big Data Analytics for your Financial Institution

A Helpful Checklist Before You Dive In

Big Data. The term itself is mysterious — addressing the massive amounts of volume, velocity and variety of customer data available. But it has value. The trick is finding a way to collect, condense and translate your voluminous data into profitable decisions.

The biggest reality is that the largest financial institutions have already pumped hundreds of millions of dollars into building and cultivating sophisticated, data-driven cultures. But for midsize banks and credit unions, there are tough decisions to be made on how to maximize value, and begin turning mountains of customer data into sustainable revenue growth.


Here are a few things to consider when you begin or expand your search for better Big Data:

1. Don’t Build It From Scratch:  In-house developed Big Data programs can come with a big price tag, and drag on and on. A recently publicized Big Data initiative by Wells Fargo is expected to cost $100 million, according to American Banker. Finding a proven advanced analytics partner, with both expertise and products, will help turn your raw customer data into valuable information in a fraction of the time and cost of building your own platform from scratch.

2. Data Talent Is Hard To Find And Keep:  Hiring, training and managing highly skilled, knowledgeable, data-savvy personnel is costly. Plus, once trained, these highly sought after data scientists often fly the coop for better opportunities. Seek out partners with embedded data scientists that can share industry best practices with your financial institution, as an alternative to investing in costly data-talent.

3. Look For Lift:  Most financial institutions use only 10-to-15% of their technology’s true capabilities. Look for Big Data analytics partners that offer technology products that can demonstrate a marked revenue lift, justifying the cost and ROI in the first year.

4. Remove Silos:  Big data isn’t just for your IT folks. From the C-Suite to management — all should be able to access, share and see data on any aspect of the institution or branches to monitor performance, challenges or emerging opportunities.

5. Predictive vs. Prescriptive Analytics:  Advanced analytics isn’t just about finding and mining data. At financial institutions, it should be about showing segmented customer data and prescribing actions to capitalize on newfound knowledge. Now that’s sophistication.

6. Better Segmentation:  It’s typical that only 2% of your customers produce 100% of your profit. Therefore, Big Data’s greatest value comes from quickly segmenting and visualizing your customers by income, risk, frequently used products and services. In addition, this advanced customer segmentation allows staff to take specific actions, such as finding opportunities to cross and up-sell customers without a debit card, or mature mortgage holders without an equity loan.

7. Data Security:  You do not want sensitive, private customer information to become exposed. Search for a solution that is SSAE 16 certified, and will exchange data via SFTP (a secured file transfer protocol), omit NPI (non-proprietary information), and will at least pass data through an encryption / scramble routine that’s specific to your financial institution.


Saggezza’s TruVantage™ can help you accomplish these 7 critical components without spending a fortune on technology and human resources. TruVantage is an advanced analytics product for decision makers at financial institutions.  The product is a combination of revolutionary modules, supported by a data scientist partner that together empower informed business decisions and deliver a competitive advantage.


Interested in a free advanced analytics demo? Email us at FIAnalytics@Saggezza.com