Analytics - The New Level Playing Field
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Analytics - The New Level Playing Field

By Manesh Prabhu, SVP & Chief Technology Officer, People’s United Bank

Manesh Prabhu, SVP & Chief Technology Officer, People’s United Bank

Even if you don’t pay particular attention to professional sports, the chances are excellent that you know about “Moneyball,” and the ever-increasing use of analytics in every significant facet of most professional sports. Just like analytics-enabled small market teams to compete and win against major teams and transformed how most professional sports are managed and played today, the use of analytics will process every aspect of Banking.

The economic characteristics of data management and analytics are becoming cheaper and cheaper with technology innovation. You don’t have to be an economist to understand that as the cost of analytics comes down, more and more of it will be used, and more and more problems will be solved using analytics. Open-source technologies and the availability of tools that continue to make even advanced analytics more accessible without requiring advanced skills presents a level playing field. Organizations that may not have had the budgets, access to skills, and experience of the larger institutions, now have the potential to compete and win on analytics.

Analytics presents one of the unique levers for business leaders that when used appropriately, can lift business performance across the enterprise and build competitive advantage. Analytics refers to a broad set of capabilities comprised of traditional reporting; data visualization; quantitative modelling that includes descriptive, predictive and prescriptive modelling, and adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies. These capabilities can co-exist and evolve at different maturity levels. Traditional reporting using tools such as IBM Cognos, SAP Business Objects etc. or Data Visualization technologies such as Tableau, Qlik, etc. are still powerful business tools to understand and manage a business.

While traditional reporting and dashboards are still very useful, acting like the instrument panel in a car describing what’s happened in the recent past, how it compares to the distant past, etc.; quantitative modelling, especially with predictive analytics with use of AI/ML technologies, can give you the perspective to navigate ahead in the ever-changing business climate, much like a GPS App suggests alternate routes based on traffic patterns. Advancements in tools that aim to democratize AI and ML makes adoption of those capabilities within reach for most organizations.

"Analytics presents one of the unique levers for business leaders that when used appropriately, can lift business performance across the enterprise and build competitive advantage"

Use of Quantitative Modelling and adoption of statistical and quantitative techniques presents untapped opportunities for many enterprises, especially in areas other than its traditional use in domains such as Credit Risk, Capital Adequacy and Stress Testing domains. An incremental/experimental approach can help organizations who haven’t yet started on this front, especially with the availability of open source libraries and the use of tools that democratize the data science. Organizations that are starting, or are in the early stages of maturity, can find plenty of opportunities to get started and move further along the maturity stages. For example, a simple Value at Risk type scoring model can help fraud analysts focus on reviewing transactions that present the most significant loss potential, rather than just looking at sales by their amount.

Getting specific use cases to production use is more important than any planning you can do; it will exercise the organization's operational muscles in ways that will help build out the operations model more quickly, benefiting from the learnings along the way. Leadership buy-in, experimentation, opensource technologies, cloud services, talent development, adoption of agile methodologies, can all be critical in paving the way for success.

Adoption of analytics inherently attempts to re-tune the organizational culture by influencing how decisions are made and how business operations are performed. While there is always going to be value in formulating an analytics strategy, there is a lot of wisdom in the maxim often attributed to management guru Peter Drucker: “Culture Eats Strategy for Lunch.” While it may be the Intrepid Sailor that will win battles, cultural changes are more like what Edmund Burk stated: “Enlightenment is a product of tradition evolving, not their abandonment of it.”If you are an analytics leader, especially in well-established enterprises, you should know that it’s a journey you need to take the organization along and be cautious with a trail-blazing approach, unless you are operating in an environment where radical change is what the organization is charting to set on. In our experience, it’s easier when top executives understand the culture change aspects with analytics and champion that change. A maturity model can be a useful tool in communicating the analytics strategy to the organization. Don’t think there is one right way or one right approach for every organization. It’s helpful to think in terms of maturing the analytics capabilities over time, no matter where you are in the different maturity stages as an organization. Look to sophisticated analytics capabilities over time, being careful not to out-run the businesses capability to adapt.

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