By Stuart Grant, Head of Capital Markets, Asset and Wealth Management, SAP
A host of structural and competitive factors are putting even more pressure on asset and wealth management organizations’ foremost historical challenge of persistently lean margins.
Tight margins have had their benefits, including a buy-side penchant for IT innovation to preserve and, ideally, boost them. But we’re now seeing some unexpected costs of previous innovation, and they are widespread.
Specifically, firms generally invested in best-of-breed point solutions. Due to the technological limitations of their time, these solutions generally run on, and yield, siloed data. While effective where implemented in front-, middle-, or back offices, these point solutions’ data architectures limited their broader utility within the organization or required costly bolt-ons to normalize and share data beyond narrow functional boundaries.
Add to that the technical debt of outmoded systems many buy-side firms face; the erosion of brand loyalty and the ease with which customers can jump ship; competitive concerns unleashed by such factors as Vanguard’s recent reduction in European ETF fees, adding to the largest fee reduction in its US history earlier this year; the emergence of nimble, cloud-first buy-side organizations with clean IT slates; and market uncertainties that demand quick, decisive analysis based on a broad-based, timely grasp of exposures and opportunities (see U.S.-China trade relations, concerns around private market credit risks, and cryptocurrency considerations).
The vision of having comprehensive, immediate access to reliable, relevant data pertaining to pressing business problems is not new. What is new is that technology has finally caught up with that ambition through suite synergies.
Data harmonization brings new business insights to the buy side
Buy-side players can now boost efficiency and improve business outcomes by harmonizing data across ERPs, point solutions, and other systems. That, in turn, empowers AI, capable of drawing connections, deriving business insights and, in the future, taking proactive action.
Consider an example touching one of the biggest information gaps we’re seeing on the buy side: the disconnect between the finance function and the front-office portfolio management teams when trying to incorporate cost data into strategic-planning and budgeting processes.
Harmonized data models help firms bridge that gap. These models can bring in and, through standard or user-defined data products, deliver real-time visibility on transaction-fee-based revenue, assets under management, inflows and outflows related to those assets, customer performance and profitability, costs, and more.
One buy-side firm we’re working with wants to combine portfolio-attribution data on the performance of individual elements of its portfolio with data on customers’ risk appetite and fee structures. The idea is to more firmly grasp the financial reverberations of portfolio rebalancing against customer expectations and future earnings.
Another firm is harnessing a harmonized data model to provide what-if tools to the front and middle offices so they can run scenarios around the sales-commission, foreign-currency-related, and other costs of growing its assets under management.
These scenarios require a shortening of the data gap between front- and middle-office data and the information held in the back-office finance system of record. For the first time, suite synergies are enabling the finance system of record to become a system of action, leveraged by nontraditional users in the front- and middle-office.
Harmonized data empowers AI
AI is critical in mapping the expanding terrain of data buy-side firms have at their disposal, and AI thrives in these harmonized data models. AI can survey a firm’s data from horizon to horizon, providing early notice of incipient trends; discovering new business opportunities exposed through the synthesis of previously discrete, incomplete data sets; and delivering to business users the precise data they need, when they need it, via natural language queries and without having to engage IT staff.
As an example, a financial analyst might ask AI what trades may be affected by policy shifts surrounding Chinese rare-earth minerals – and how that might impact fee-based revenues and accounts receivable over the next four to six weeks.
AI would derive an answer (or answers) by tapping into information from typically disparate, disconnected databases pertaining to fee-based revenue from assets under management, inflows and outflows, fund performance, billing data, historical cash flows, and customer behaviors.
To do that, AI needs unified data, and because no one IT vendor can do it all, that takes suite synergies built on harmonized data models. Otherwise, the fragmented, siloed data architectures that enabled the buy-side innovation of previous eras remains a liability.
A new era of buy-side innovation built on data harmonization
The nature of the buy-side business has led its players to the forefront of innovation before. Now the main thrust of these efforts must focus not on discrete applications to improve specific functions, but rather on exploiting untapped business value that harmonized data models can deliver.
Some of the world’s largest buy-side institutions are working hard on data harmonization – as are smaller players unburdened by legacy systems. The rest should get moving, because suite synergies promise to improve decision-making on multiple fronts, open new niches for the business, lower IT costs, boost organizational efficiencies, and help firms operate more collaboratively and cohesively.
