Why The Chief Analytics Officer Role Is So Important

Interview with Dr. Zhongcai Zhang, Chief Analytics Officer at New York Community Bancorp, Inc.


A a senior analytics and BI executive in the Financial Services industry, Dr. Zhang provides thought leadership on and serves as chief architect for numerous innovative analytic solutions to business challenges at various organizations. At New York Community Bancorp, Zhongcai is responsible for enterprise-wide business analytics, modeling, and reporting, providing operational and strategic decision support to such areas as customer acquisition and retention, product cross-selling, deposit pricing, product development, site selection, liquidity management, process optimization, profitability, and the like.

We sat down with him ahead of his presentation at the Predictive Analytics Innovation Summit, taking place in Chicago this November 29-30.

How did you get started in your career? 

Before I started my analytics job (database marketing manager, to be precise) in the banking sector 17 years ago, I was in academia doing applied economic research. Long story short, while I was in my doctoral program, I did a bank location modeling project for a bank who was contemplating on opening up a new market and my site scoring model helped the management pinpoint the viable locations for the opening of the first set of branches in that market. This experience ultimately led me to the analytics field in the banking industry.

How do you think the responsibilities of the Chief Analytics Officer (CAO) have changed in recent years? 

With the appointment of a CAO, the role of analytics has clearly elevated to a new level. A central responsibility of the CAO is to ensure analytics is helping uncover the value-added insights for a variety of enterprise-wide decision-making processes with respect to customers, products, and services, at both strategic and tactical levels. Needless to say, to this overarching end, a CAO has to make sure the organization has a solid data infrastructure, a complementary set of data technologies and analytic tools, and a team of resources (encompassing business analysts, statisticians, programmers, database experts, and analytics managers) collectively tackling the business analytics challenges.

Data democratization is a phrase commonly heard these days. Where does the CAO fit into this, and how do you delineate between other executives who may feel they should control data - the CTO, CDO, maybe even the CFO? 

Nowadays one would rarely find any job out there without the need to rely on data or certain metrics. For virtually all organizations, making data accessible at all levels is essential. For data democratization to be done correctly, a delineation of two-part data ownership is important: while IT owns the technical side of data, the CAO should be responsible for the content ownership of data. The former is focused on data flow, storage (including some cleansing) and retrieval processes, and the latter often has to know the intricacies and pitfalls of the data across the enterprise repositories. This is how an analytics project often consumes 70% of the time in data preparation.

What do you see as the role of the data scientist in the future and how do you think machine learning will impact their role? Will data scientists themselves see their work automated? 

Data scientists, sometimes referred to as unicorns, play a crucial role in the provision of analytics within an organization. Machine learning clearly has an impact on how we do analytics and this impact is poised to increase (directionally) as such machine learning algorithms start to mature. However, such impact is very gradual and likely to be confined to those with a mere technical focus. There are, broadly speaking, two types of data scientists: one more technically oriented and one more business savvy. For data scientists with strong business acumen, they are going to see the demand for their skills on the rise. In the next few years, we are more likely to see a continual increase in demand for data analytical skills afforded to us by business savvy data scientists.

What qualities do you think a good CAO needs? How can they be most effective, and what can a company do to enable them to best fulfill their tasks? 

A CAO needs to be technical but, more importantly, business savvy with strong leadership skills. As a bridge between analytics and business, the CAO ought to be an effective communicator and passionate advocator. Companies need to embrace 'data-as-an-asset' strategy and develop a data-driven decision-making culture, helping cultivate a healthy demand for insight-focused analytics environment in which analytics will grow and thrive.

What challenges do you face in analytics in banking specifically? How can analytics help risk management, particularly predictive analytics? Can they help overcome regulations? 

There are many challenges analytics face in banking. Combatting fraud is one. So is modeling various behaviors such as default under stress scenarios. Analytics is playing an increasingly important role in product innovation and pricing optimization as well. Many companies already have various types of analytical processes in place and are constantly working to recalibrate such processes for optimized efficacy.

What will you be discussing in your presentation? 

My presentation will be focused on the evolving role of the CAO from a practitioner’s perspective. 

You can hear from Dr. Zhang, along with other experts in data analytics, at the Predictive Analytics Innovation Summit. View the full agenda here.

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