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?
- 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.
- Data Set
- Proxy Categories For Technology Spending
- Market-Makers, Broker-Dealers, and Other Specialists
- Asset Managers and Technology Spending
- Assembling Technology Spending Segments
- Scaling the Business
- Segmentation Analysis
- Assembling Asset Manager Segments
- Assembled Framework
- The Map
- Case Study on Estimation: The Quant Gods
- Case Study on Explanation: There’s Something About Och-Ziff
- Case Study on Recommendation: Exposing Franklin Templeton’s Challenge
- Case Study on Location: Discovering Intelligence About Manager X
- Next Steps
- Closing Thoughts
- Author Paul Rowady
Exhibit 2: Annotated Framework Hypothesis – Human Capital Leverage Benchmark
Exhibit 3: Annotated Framework Hypothesis – Technical Leverage Benchmark
Exhibit 4: Annotated Framework Hypothesis – Technical and Human Capital Leverage Benchmarks
Exhibit 5: List of Asset Managers, Key Attributes (Core Modeling Data)
Exhibit 6: Representative List of Asset Managers, Core Strategy (Supplemental Data)
Exhibit 7: Buyside Asset Managers (37) – Overview Statistics
Exhibit 8: Buyside Asset Managers (135) – AUM and Headcount, Ranked By AUM (2016)
Exhibit 9: Buyside Asset Managers (135) – Distribution of Headcount (2012, 2016)
Exhibit 10: Technical Leverage Benchmark - Segmentation Proxies
Exhibit 11: Market Makers, Broker-Dealers, Execution Tech – TCO/E, Total Tech Spend (5Y Avg.)
Exhibit 12: TCO/E Ranked By AUM With 2016 Highlight (37 Models), 2005-2016
Exhibit 13: Complete Technical Leverage Benchmark—TCO/E By AUM (60 Models), 2005-2016)
Exhibit 14: Ranking of Five-Year Average TCO/E (60 Models), 2012-2016
Exhibit 15: Headcount Relative to AUM Growth (135 Managers Max Range), 2005 to 2016
Exhibit 16: Segmented Analysis—AUM/E Ranked By AUM (135 Managers Max Range), 2005-2016
Exhibit 17: The Drivers of Scale (135 Managers Max Range), 2005 - 2016
Exhibit 18: Complete Human Capital Leverage Benchmark—AUM/E Ranked By AUM (158 Companies Max Range), 2005 - 2016
Exhibit 19: Framework Prototype - Combining Technical, Human Capital Leverage Benchmarks
Exhibit 20: Strategy Segmented AUM/E ($ Millions) 2005-2016
Exhibit 21: Converting Framework Into Map of Strategy Categories, Manager Coordinates
Exhibit 22: Framework Segmentation and Attribution
Exhibit 23: Applying the Framework – the Quant Gods, 2012-2016
Exhibit 24: The Quant Gods—Using the Framework to Estimate TCO (Ranked By TCO)
Exhibit 25: Applying the Framework – Analysis of Oz Management, 2005-2016
Exhibit 26: Oz Management – TCO/E, 2005-2016
Exhibit 27: Oz Management – Change in Tech Spend, TCO/E Components, 2005-2016
Exhibit 28: Human Capital Overallocations – Headcount Aberrations Relative to AUM, 2016
Exhibit 29: AUM Growth Indices – Blackrock and Franklin Templeton, 2005-2016
Exhibit 30: Blackrock, Inc. – AUM/E, Headcount Growth, 2005-2016
Exhibit 31: Five Year Average AUM/E – Top 10 AUM Managers in Sample, 2012-2016
Exhibit 32: Applying the Framework – Analysis of Franklin Templeton and Others, 2005-2016
Exhibit 33: Locating Manager X - Map of Strategy Categories, Manager Coordinates
Exhibit 34: Locating Manager X - Using the Framework to Estimate TCO
Exhibit 35: Supplemental Manager Roster and Core Strategy
- Aberdeen Asset Management
- Adams Street Partners
- Advent International
- Aegon Asset management
- Affiliated Managers Group
- Alliance Bernstein
- American Securities
- Ameriprise Financial
- Amundi Asset management
- Anchorage Capital Group
- Apollo Capital Management
- Angelo, Gordon and Co.
- Ares Management
- Balyasny Asset Management
- Bayview Asset Management
- Berkshire Partners
- Bank of New York Mellon
- Blackstone Group
- Bluecrest Capital management
- BNP Paribas Asset Management
- Bracebridge Capital
- Brookfield Asset Management
- Calamos Asset Management
- Carlyle Group
- Caxton Associates
- Canyon Capital
- Capstone Investment Advisors
- Carlson Capital
- CarVal Investors
- Cheyne Capital management
- Citadel Advisors
- Clayton, Dubilier and Rice
- Coatue Management
- Colony Northstar
- Crescent Capital Group
- D. E. Shaw
- Davdison Kempner Capital Management
- Charles Schwab
- Deutsche Bank
- Ellington Management Corp.
- Eton Park Capital Management
- Farallon Capital Management
- First Reserve
- First State Investments
- GC Advisors
- GCM Grosvenor
- E*Trade Financial
- Eaton Vance
- Federated Investors
- Flow Traders
- Fortress Investment Group
- Franklin Templeton
- Goldman Sachs
- Interactive Brokers Group
- Investment Technology Group
- JP Morgan
- Jefferies Group
- Jones Financial
- Lazard Asset
- Legal & General Group
- Legg Mason
- Man Group
- Morgan Stanley
- Nataxis (Global Asset Management)
- Northern Trust
- Oaktree Capital Group
- Och-Ziff Capital Management Group
- Old Mutual
- Oppenheimer Holdings
- Platinum Asset Management
- Prudential Financial
- Raymond James Financial
- SEI Investments Company
- State Street Corp.
- T Rowe Price Group
- Tetragon Financial Group
- Virtu Financial
- Waddell & Reed Financial