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Big Data: Not Rocket Science – Data Science

Big Data: Not Rocket Science – Data Science

Smaller banks can combine data science and advanced analytics for profitability

Banks have always had big data. Today, however, it’s often the institutions building big teams of data scientists, or seeking out innovative analytics partners with embedded analysts, that are gaining a true competitive—and profitable—advantage from advanced analytics.

Volume of data keeps growing. In recent years there has been exponential growth and availability of data, both structured and unstructured, within banks, arriving at extreme velocities from a myriad of new sources.

The more a bank knows, the more it can predict about its customers’ wants and needs. Soon, most major decisions to grow revenue, control costs, or to identify risks will be driven by data and analytics.

But will the smaller banks be able to harness—and act on—their big data?


Big Data analytics refers to data creation, storage, and retrieval of large quantities of data—often from many different sources. When coupled with advanced analytics, usually based upon advanced regression techniques such as “random forest,” we have “Big Data Science.” (Random forest refers to trademarked term for analysis based on generation of a multitude of decision trees.)

A March 2014 Gartner report—2014 CIO Agenda: A Banking/Investment Perspective—revealed the No. 1 technology spending priority in 2014 for financial institution CIOs was investing and utilizing business intelligence and data analytics. Why? Understanding customer behavior trends and purchasing influences is key to a sustainable competitive advantage for financial institutions. Data scientists at larger banks have been trained to leverage fast information and quick insights, allowing them to take action on immediate opportunities, and helping to improve cross-sell opportunities that enhance the bank’s competitive advantage.

What started as a ripple has become a tidal wave. Collecting data from a wide variety of bank sources can often include two dozen data streams and involves account information, real-time customer transactions, customer communication preferences, social media mentions, channel usage,  loyalty behavior, and more.


As the banking industry continues to struggle with tight margins and profit challenges, it’s vital for financial institutions to uncover new opportunities to reduce expenses, retain customers, and create new revenue streams.

Advanced analytics can help overcome such challenges. Consider these five advantages:

1. Better knowledge: Gain new insights on your most loyal and profitable customers. Data analytics can help track and measure progress, while servicing customers on the right channel for their needs.

2. Customer retention: Manage your customers’ experience and find ways to cross sell and develop relationship pricing for loyal customers.  Also, have a metric to identify “loyalty” and identify the risk that certain customers will “churn.”

3. Cost-effective marketing: Develop more effective marketing and lead generation campaigns that are targeted to the right person at the right time. Having a system that allows you to segment, manage, and track specific actions will enhance your marketing ROI.

4. Mitigate risk: Enhance risk and fraud management by easily and quickly spotting pattern changes that are an indicator of potential risk.  Also, have an engine to easily review transaction concentrations and assess “customer average.”

5. Take action: Once key data segments are identified, take action and measure the effect over time.  Incremental improvement leads to best practice.


Big banks are making big investments in data and analytical staffing. Having the right information to target at the most granular level allows banks to make more informed decisions, take action, and track and measure tangible goals on specific segments of data.

Typifying the trend, a year ago Wells Fargo announced the bank had hired its first Chief Data Officer, and allotted a budget of $100 million to a team of 600 dedicated to refining customer data.

Wells’ goal? To make big bank data  actionable—and the bank more profitable.  A focus on data science has helped the five largest banks increase U.S. market share dramatically—gaining 108% from 2000-2013, according to FDIC statistics.

In the face of such spending, how can smaller and midsized banks to compete?


Small and midsized banks face tough decisions on how to turn mountains of customer data into scalable profitability.

The reality is the majority of banks do not have the budget to hire mathematical statisticians and an army of analysts to sort and sift through data. If you are ready to implement a plan with the right team, consider these four questions:

1. Staff and budget: Do you have the budget to hire, train, retain, and manage highly skilled, knowledgeable, data-savvy tech and analytics personnel?

2. Analytics partner: If you consider an analytics partner the right approach, consider whether they offer an advanced analytics solution that is fast, does the heavy lifting, and is still competitively priced. Also ask whether they will help you utilize and customize all the technology capabilities available.

3. Analytics: How advanced is your current data or business intelligence manager regarding advanced analytics and data science coupled with experience of analyzing bank data?

4. Capabilities: What tools and software will you use to capture, secure, store, search, share, analyze, and visualize your data?


To comply with the unprecedented amount of regulations, remain relevant, and compete in this new era of data management, financial institutions need to rethink the way they manage the data deluge.

If you opt not to invest in costly data talent, specifically seek out those partners with a customized, sophisticated, yet simple analytics solution, and/or one with embedded financial institution data scientists that can share industry best practices.

With the right analytical tools—whether it be an advanced analytics solution, a team of data scientists, or both—big data can be simplified. This can lead financial institutions of all sizes to much more than just data exploration.

It can lead to answers, action, and information that turns into profit.

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