As business leaders return from summer vacations they come back to stuffed in boxes and lengthy to do lists. Charged with delivering value they must attend to multiple projects each with tight timelines and tighter budgets. But, before they throw themselves headlong into this maelstrom of competing demands, it is worth taking a breath and considering how they want to apply their data literacy to these situations. One approach is to try and deliver as much as possible as fast as possible. Check things off and move on to the next challenge. The other is to take a step back, discern the wood from the trees and ask themselves what they want to be famous for. Do they want to focus on the tactical, solving today’s problems, or build something world class that will set the context for future competitive edge?
An ESG Model
Environmental, Social and Governance projects will almost certainly be high on that list of to dos. Incoming regulations across many jurisdictions, as well as increasing public and investor scrutiny are making it imperative for banks to collect, integrate and analyse a wide range of data on their ESG performance as well as that of their customers and investments. Business leaders’ approach to this challenge can throw the question above into sharp relief.
There are many ‘quick-fix’ applications, often cloud-based, that offer the opportunity to collect some data, model outcomes and prepare reports. These solve the immediate problem and can seem an attractive ‘once and done’ solution that ticks the box and allows the team to move on. However, ESG, in common with most business processes in today’s fast-evolving environment, does not lend itself to these approaches. The scope and scale of ESG reporting is only going to grow and many solutions simply do not have the scalability needed to keep up. A fast, seemingly low-cost solution rapidly becomes a grinding and increasingly expensive bottleneck as it struggles to manage the variety and velocity of data it needs to analyse.
The alternative path is to create a solution that is built to last; that can scale to meet future requirements and operate as part of an integrated data platform that is in itself sustainable. Rather than creating a proliferation of data silos (each of which consume storage and processing capacity that uses energy and contributes greenhouse gas emissions), this enterprise-wide approach integrates data and makes it available to analytic models across the bank, reducing data movement and eliminating silos. It is an approach that lends itself well to cloud-based architectures.
Identified by industry analysts, Gartner, as a Leader in its Magic Quadrant, Teradata should be considered by any organization wanting a best-of-breed analytical cloud solution for ESG and any other complex workload. Our global customer-base, including many of the leading banks and financial services companies, immediately allows customers to establish themselves among benchmark businesses. Thinking strategically, leaders not only solve today’s challenges but contribute to the foundations of ongoing performance, growth and wide-ranging application of data analytics in pursuit of corporate goals.
Led from the Top
Of course, this level of decision-making needs to originate at the top of organisations and this is where senior leaders need to demonstrate their own data literacy. Leaders must establish a context in which delegated decisions are seen not as tactical point solutions but as contributors to, and beneficiaries of, an enterprise-wide data strategy. Operating within this context changes the motivation of business leaders from simply solving specific problems as fast and cost efficiently as possible, to a wider, far-reaching commitment to building best in class solutions that contribute to Bank’s overall goals.
Building the Bank of the Future
We’ve used ESG as a topical example here, but the principle applies to many other challenges and requirements across banks. Whether its analytics for marketing, fraud prevention, risk assessment or to drive new customer experiences, business leaders are always faced with similar choices. Focusing on the tactical can deliver swift results, but at what cost? Proliferation of data silos, scalability cul-de-sacs that prevent solutions from growing to handle new data, inability to expand to incorporate other departments across the bank, and increasing cost per query are all outcomes that we see as a result of an overly tactical focus.
Those financial services organisations that exhibit true data literacy avoid these bottlenecks and instead choose to build best in class solutions that meet current and future needs. Maintaining a strategic focus and always looking for ways to reuse, integrate and make data analytics available beyond the immediate requirement are hallmarks of those banks that set the global benchmarks as data-driven, customer-centric leaders. So, as you return to your list of tasks ask yourself, do you want to be famous for tactical delivery, or recognised as delivering best in class solutions that contribute to the Bank’s future strength?