There are few industries with more of reason to embrace data analytics than the banking industry. Not only do they deal with an inordinate amount of data every second, but there is an intense expectation of privacy, accuracy and a myriad of regulations to navigate.
However, there is more to analytics than how you utilize functional data. The banking industry in the West is largely viewed in a negative light and this puts an impetus on financial institutions to provide the very best value-driven services. With the number of variety and personalization possibilities, the room to improve is always growing.
To explore this further, I spoke with Paul E. Mann, the current Analytics Manager for Wells Fargo’s Wholesale and Head of the Corporate & Investment Banking divisions’ Customer Data Management office. He leads numerous initiatives utilizing data, supervised/non-supervised machine learning & other analytic approaches to enhance operations, reduce the functional burdens associated with regulatory and compliance requirements; and successfully build data-driven statistical quality processes.
Ahead of this year's Big Data Innovation Summit we discussed the importance of efficiently leading a data-driven enterprise in a chaotic world and how to do so.
In your opinion, is it important for companies to be data-driven, or do you find that some companies actually put too much emphasis on it?
It is very important and will be increasingly more important for businesses founded before the digital age. They especially need to understand that data is as important as the service and product provided by organizations. Most digital age companies are already well aware of this fact.
As it is so important, how can an enterprise ensure it is data-driven?
In his book 'The Digital Transformation Playbook', Dr. David Rogers suggests five areas of focus as companies move to digitally transform both their operations and their managerial mindset. Those five areas are customer, competition, data, value, and innovation. Data is the single thread that will allow organizations to leverage the other four dimensions. Becoming a data-driven organization is a massive undertaking, but it is ultimately the role of the Chief Executive Officer. Their commitment, vision, and ability will drive the organization’s success or failure by leading the way in the pivot towards becoming a data-driven organization.
If being data-driven is so important, then the ability to understand the data is equally as important, right? In your opinion, how important are data visualizations to the analytical process and what do you think are the key factors behind the creation of truly impactful visualizations?
I believe the ability to appropriately visualize data will become increasingly more important. Beyond the benefits of simply visualizing the data – the audience gains greater insights into relationships and characteristics of the data in a very thoughtful and well-constructed visualization. I believe that as organizations continue to mature, visualizations once used mostly within statistics and engineering will become better spread.
How about unstructured data? How important is it and are there technologies enabling you to more efficiently analyze it?
As we continue to move towards the future – unstructured texts will continue to increase in both volume and relevance. I believe I saw a statistic that stated that 70% - 75% of the data to be analyzed in the future will be unstructured data.
Another mildly terrifying stat to do with data! How about regulation? Do you feel more regulation is necessary or do you feel like it’s a hindrance to innovation?
Neither, I believe that disruptive innovation will drive towards operational excellence. Thusly, processes will generate more data, this data will, in turn, be used to generate performance metrics. Their metrics will be used to measure, manage and control processes. Companies using AI to completely transform their operations and management mindset will outpace regulators. In my humble opinion, only second movers or companies whose efforts to effectively use AI have stalled will be at the mercy of regulators.
And do you foresee technologies changing the way you use data over the next five years? Do you have any strategies in place to prepare yourself for them?
I envision the companies that will truly thrive are those that can leverage the relationship between users with deep domain and data knowledge and data science platform technologies and, natural language processing API that can integrate with multiple data sources. Additionally, I believe that machine driven language transition APIs will be increasingly more important.
However, I humbly assert that it will be organizations that drive how data will be used within the business context. Tools are helpful and serve a purpose, but the rise in the use of AI will bring with it enhanced insights that will be human-driven. The next wave of data utilization will be human, not technology driven.
Finally, can you tell us a little bit about the presentation you will be giving at this year's Big Data Innovation Summit
As companies and industries continue to evolve – so must their the ability to observe and address “catastrophic” business disruption events. The concept of “Blind Side” risks/threats while they mistakenly appear as natural consequences of evolution - their true dangers (& opportunities) are many times hidden and/or have not yet been detected from casual observation. Given their exponential, vice linear nature, “Blind Side” threats are normally evident through “catastrophic” events.
There are few surviving industries that have been more adversely impacted by “Blind Side” threat/disruption than the National Football League (NFL). The NFL case study is even more interesting given their equally “catastrophic” resolution to address their “Blind Side” threat.
Within the past few years, there has been no shortage of claims offering dire warnings of the obvious adverse “catastrophic” events (dystopian society, massive job losses, technology as an instrument of control, etc.) resulting from embracing AI innovation. The potential impact of AI to both business and society are dizzying – could the NFL “Blind Side” approach bring greater clarity to the underlying AI innovation threats and opportunities not yet revealed within the popular press? My presentation will attempt to apply the common threads of NFL “Blind Side” threat resolution to AI innovation.
To hear more from Paul E. Mann and other thought leaders and experts about how they are using data and creativity to excel on a big scale, attend this year's Big Data Innovation summit July 17 - 18, Las Vegas.