The Context Machine: Estimating Asset Manager Technology Spending

Technology strategy and business strategy are becoming one and the same. Any lingering gaps between them are closing-front to back, top to bottom, and across the entire business segment spectrum. Many firms born in the “analog era” - with long legacies of high-touch offerings - are rebranding themselves as technology companies.

And yet, for all the promise of the revolutions in artificial intelligence, cloud, and big data, attempts to forge a greater understanding of the interplay between technology and business strategy meet with unique challenges. Most players in this ecosystem are still using dull intelligence tools to navigate this rapidly changing and increasingly techno-centric landscape. Finding balance between the primary engines of productivity (IT and human capital)-continues to be conducted like a game of Marco Polo-a blind search with little more than a whisper of empirical evidence. In this space’s new vernacular, operational alpha is a growing theme that seeks to illuminate such challenges. However, despite its descriptive elegance, operational alpha remains a formative and elusive concept.

The publisher of this report set out to influence this dynamic in a new direction, starting with one simple, yet increasingly germane, question: What do banks spend on technology? After all, understanding technology consumption patterns offers unique insights into shifts in both business strategy and operational efficiencies.

The report starts there because banks provide more access to financial and operational data than does any other player in the financial services ecosystem, thanks to regulatory filings and other periodic reporting. They also happen to be the largest absolute consumers of technology in the ecosystem, so they made sense as a starting point. In the end, the attempt to answer this question generated numerous insights and valuable comparative intelligence about business strategy implementations in the global banking sector.

As such, the initial work has already paid dividends in the global banking arena, highlighted by the early development of T-Greeks operational benchmarking framework-particularly in the discovery of T-Spread productivity analytic - and the introduction of the concept of return on technology. It was known from day one that the main challenge in answering this question for the asset management universe was always going to be that these firms are mostly private, if not highly secretive; therefore, accessing the right data was going to present some challenges. How would we answer the question of what asset managers spend on technology?

Synopsis

• Technology strategy is becoming business strategy.
• Measuring technology spending amplifies critical navigational intelligence; a term used to specifically emphasize impact for organizational transformation.
• For the asset management universe, current tools to measure technology spending are ineffectual.
• This publisher has developed a method to estimate technology spending for asset managers in cases when there is unobservable data and to enhance contextual understanding of observable data.
• This methodology’s success hinges on this question: How do managers scale?
• Manager scaling is quantified by the ratio of assets under management (AUM) per employee (e)-known herein as human capital leverage (AUM/e).
• This report offers this hypothesis: There is a persistent relationship between AUM, technology spending, and headcount (e) whereby human capital leverage and technical leverage (TCO/e) move inversely as AUM changes. (In the context of this report, total cost of ownership (TCO) and total technology spending are used synonymously.)
• Factor relationships are persistent because there are only two engines of productivity (IT and human capital), meaning technology spending can be deduced from human capital leverage.
• As AUM changes, new strategy choices may need to be made, and these choices represent the greatest governing impact on the shift in human capital allocations, thereby enhancing the persistent nature of the factor relationships.
• In this study, data from 158 companies over the period of 2005 to 2016 is used to validate the hypothesis and construct a benchmarking framework.
• Study findings offer numerous practical applications to both technology buyers and solution providers as well as a roadmap for the framework’s future development.