The term “Cambrian Explosion” is probably a familiar one to those among us who have a passing interest in biology and evolution. Put simply, the Cambrian Explosion refers to a short evolutionary period about 541 million years ago during which the profile of life on the Earth changed fairly dramatically from mostly simple, single-cellular organizations into complex multi-cellular life across both plants and animals. This evolutionary step-change is significant because of the richness—in functional ability to move, eat, respire, reproduce, and appearance—that emerged at the end of this phase. This richness then became the foundation for much of the flora and fauna that we see today on the planet.
Now, why is this concept of the Cambrian revolution relevant to banking analytics? Because there are a combination of forces already shaping the banking industry and creating conditions for a similar explosion of ideas and solutions in banking analytics over the next two-three years.
• Access to banking through digital and mobile channels to an information-savvy customer base, which expects “digital-native” ease of use for all financial services
• The explosion of data storage and computation capabilities
• Increased pressure on traditional banks to deliver greater value and a better experience to customers
• Lastly, heightened requirements that regulators are placing on streamlined banking activities and particularly around the use of data
To respond to these forces, banks need to look at innovation differently to make banking and managing finances more convenient and easy on the one hand, and more efficient and well-managed on the other. One of the key points of leverage is better management of data and step-changes to how data analytics is applied within their operations.
"Banks need to look at innovation differently to make banking and managing finances more convenient and easy on the one hand, and more efficient and well-managed on the other"
Key Areas of Analytic Innovation in 2017
Analytic innovation will show up in a number of different ways in 2017.
• Getting insights out of real-time customer events, through the ability to tap into real-time data streams before the data is archived and stored in a traditional data warehouse. This will help move analytics from a time-lagged, batch activity to closer to the point of customer interaction i.e. moving intelligence to the edge of the organization.
• Analytics on unstructured data (images, text, voice), facilitated through the growth in advanced Machine Learning (ML) algorithms like deep learning. This analytics of “messy data” is going to be a big area for banks to understand customer needs and sentiment in a more nuanced manner, and (importantly) before it shows up in traditional analytic data warehouses.
• Cloud-based analytics will be an exploration area for banking institutions to take advantage of the scalability, variety, and elasticity of cloud data environments. What is also attractive is making these investments without incurring the associated fixed costs of both infrastructure and people. Banks will continue to review and adapt their security policies to use these environments judiciously without putting customer data at risk.
• Interactive Business Intelligence tools will continue to transform the world of traditional business reporting. These tools will put more capability in the hands of lay users of analytics to drilldown and explore data, and do so for extremely large data volumes and to unstructured data.
• And finally, with the growth in focus on risk and regulatory areas, banks will lean into coming up with analytically intensive solutions for data discovery and information security. The sheer volume of data that modern banks have to manage and the growing attention on data management practices means that automated and ML-driven approaches will need to augment the work that was traditionally all manual.
To be clear, many of the analytic innovations described above have already become the norm for analytics in digital native companies and e-commerce leaders. However, adoption of these innovations within banking has been slow. There are a number of organizational roadblocks (or enablers, if you are a glass half-full type person!) that need to be understood and overcome for banks to reap the greatest value out of analytic innovation.
Legacy Infrastructure Needs to be Augmented by Modern Hardware and Platforms
“If it ain’t broke, don’t fix it” is an adage seen in several industries, and traditional banking probably is close to the top of the list. Much of the banking infrastructure in place today has a late twentieth century vintage–also, the complexity of the business means that these applications are similarly complex and messy. No technologist at a legacy bank relishes the task of either opening up the hood and upgrading these core applications, or undertaking the effort of rebuilding them (unless there is an overwhelming pressure to do so). The next best option is to build out new applications based on newer hardware and software components and operate them alongside these legacy platforms. This calls for flexible and agile architectural visioning in the IT departments of banks to allow these vastly different patterns to co-exist.
Newer Software Engineering Practices and Agility of Delivery need to become Part of the Organizational DNA
The venerable Software Development Life-Cycle (SDLC), while providing an illusion of solidity, is highly unsuited for the ever-evolving customer, the changing business requirements, and, ultimately, the speed at which business opportunities appear and vanish. This is the main reason why various forms of agile software development and continuous deployment practices are making their way from the Internet companies to banking.
Data Security, Governance, and Privacy Practices Need to Keep Pace
Data security/governance, and opportunities to use data to bring valuable outcomes to customers used to represent the two sides of a trade-off; that luxury doesn’t exist any longer. It is not sufficient any longer to keep data secured behind vaults to ensure security and governance; this also has the side effect of limiting its use and value. Data security and governance needs to act on data in motion, which means thoughtful incorporation of encryption and tokenization technologies.
Fight for Analytics and Engineering Talent will be a Critical Differentiator
All of the analytic opportunities and their corresponding enablers require a highly trained, motivated and fast-learning work force. Attracting top talent from the outside, identifying and grooming the stars from the inside, finding ways to reward and motivate, and actively targeting the bureaucracy that can stifle and ultimately de-motivate the talent are all critical success factors on the people front. Organizationally speaking, people managers and the inter-departmental operating models need to also evolve to accommodate the ways in which this talent likes to work and collaborate together–else the realized ROI from this investment in talent will be below potential.
The Winds of Change are upon us
The opportunity to make financial services easy and safe for its customers is imperative for all banking. Those banks that internalize the need and therefore, the opportunity will reap value in terms of sustained customer trust and loyalty. And, one of the few tip-of-spear capabilities that can drive this change is analytics. Let the Cambrian Explosion begin!