The current fourth industrial revolution is digitizing and automating our world. The digital world moves exponentially faster, freeing up vast time and resources, so we have time to innovate. The mass production of paper has succumbed to the mass production of data. Like it or not, we are all on data journeys. We are trying to protect our personal data, signing interminable consent forms when forfeiting our information, and leveraging business data at work to a firm’s and our own career’s advantage.
Digital pipelines deliver structured and unstructured data in varied formats, frequencies, and methodologies. Asset managers and hedge funds are struggling to execute their front-office ambitions because of legacy systems’ inability to support modern analytical and reporting needs; and datasets siloed across multiple functional applications and multiple fund administrators and agents. On top of that, they are doing business under conditions of macroeconomic volatility, geopolitical disruptions, and shifting investor sentiment.
Buy-side firms have arrived at the point in their data journeys where it is no longer viable for investment professionals to merely “make do” with their data infrastructure. The downside of trying to work with disparate datasets is too low; and the upside of implementing a scalable financial data solution purpose-built for capital markets is too high to not move forward.
Importance of centralized financial data for decision-making
Investment management firms generate and oversee enormous volumes of data, spanning multiple asset classes, accounting records, currencies, risk metrics, diverse portfolios, trading and data-driven investment strategies, real-time snapshots versus historical time series, and performance analytics. They are on a quest to turn raw data into reliable, actionable information.
“Transforming data from a static asset to a strategic asset drives action across the firm. This requires a strong foundation, the new playbook and a cultural shift within the operating model. When combined with cloud technology, this transformation effort becomes scalable, more efficient and more transparent.” -- Capital Markets Vision 2025 report
Here is a hypothetical firm that has yet to deploy centralized financial data infrastructure.
A multi-billion-dollar asset manager that specializes in equities, fixed income, alternatives, and multi-asset strategies uses different systems for each business function’s operations, generating disparate data that must be standardized for reporting, risk analysis, regulatory compliance, and investment decision-making. Let’s look closer.
Portfolio management
One system provides real-time market data, trade execution, order management, and portfolio tracking. Another system is used for pre-trade compliance, order routing, and execution. The firm’s portfolio managers and traders need a standardized trade blotter, aligned trade timestamps, and asset classifications mapped to a unified schema.
Portfolio management becomes problematic when:
Reconciling transactions across multiple systems (OMS, EMS, PMS, custodians) becomes a nightmare; and failure to execute T+0 and T+1 settlement cycles, causes higher margin requirements and egregious counterparty risk.
Risk management
The risk team uses a couple of solutions as well, one that integrates market data with portfolio holdings and another for stress testing and scenario analysis. The risk team needs to align risk factor mappings across the two solutions, unify VaR methodologies for consistent exposure reporting, and map portfolio holdings from the trading systems to risk factors in the two risk systems.
Risk management becomes problematic if:
The firm generates misleading risk disclosures, violating Basel III, AIFMD, and Form PF requirements. Reported risks will not be comparable, increasing the risk of mispriced derivatives, incorrect margin calculations, and flawed risk management decisions.
Fund accounting
The accounting team must convert multi-currency transactions into a single reporting base currency; standardize NAV reconciliation across their IBOR and reconciliation solutions to avoid discrepancies; and align corporate actions data with the market data system.
Fund accounting becomes problematic when:
The firm is unable to handle complex fund structures that are slow to generate accurate views of positions and P&L by desk, asset class, strategy, fund, or legal entity. The firm is at severe operational risk with errors in FX conversion that can lead to misstated P&Ls and regulatory scrutiny; and NAV discrepancies that lead to fund pricing suspensions, causing investor panic and redemptions.
Performance and investor reporting
The IR team needs to ensure performance attribution models match across their fund performance analytics and reporting solutions. Additionally, they need to normalize peer benchmark definitions for cross-platform comparisons and standardize fund classifications for reporting consistency.
Performance and investor reporting become problematic when:
Teams are hampered by their inability to pull up important data for their purposes leading to inconsistent reporting. Inconsistent attribution models make it impossible to compare fund performance to peers. Investors lose trust and confidence.
Regulatory reporting
The compliance team needs to align trade execution timestamps with the firm’s portfolio management solutions’ data for audits; it needs to standardize legal entity identifiers across different jurisdictions and ensure that MiFID II transaction reporting formats match local regulatory requirements.
Regulatory reporting becomes problematic when:
The firm is risking heavy fines from CFTC ESMA, and FCA, as well as trade rejections, regulatory bans, investor withdrawals, and lawsuits. It will fail AML/KYC checks and may get debanked by clearinghouses.
Launching a new data-driven investment strategy
Another particularly thorny issue with using legacy systems and unharmonized data flows is expansion — in any dimension. Fund and asset managers are introducing a wave of new assets and investment vehicles, ranging from pooled loans and CLOs to synthetic risk transfers, and agricultural lending, among many others. Each new market strategy comes with its own unique ecosystem, terminology, and — most importantly — distinct data management requirements.
For example, firms expanding or merging into the private credit ecosystem will need to adapt to unique modes of valuations, investment lifecycles, liquidity, and risk. From asset modeling and transaction capture to holdings reporting, systems must accurately map tranches of pooled loan investments and term loan contracts to their corresponding parent credit facilities. This introduces new reconciliation complexities, requiring firms to implement intelligent matching logic capable of handling both one-to-many and many-to-one reconciliations effectively.
Achieving real-time investment analytics with scalable solutions
By creating consistent, integrated data workflows, firms can build scalable aggregation across multiple sources for a given data genre. In response to changing market dynamics and investor preferences, there is a surging demand for new tools that nimbly adapt across asset classes and allow users to identify insights. You can watch our on-demand webinar on integrating performance analytics with your data strategy.
Our data platform Aquata’s flexible data model, with hundreds of asset- and sub-asset classes, plus adaptors to support any geography, region, or additional asset class, is purpose-built to empower funds and managers to achieve a single source of data truth. Aquata increases the velocity at which new datasets are made available, to drive analytic modeling and strategy development. Firms must be able to rapidly scale and execute any investment strategy with precision and efficiency. To do so, they can implement a robust data strategy that supports both illiquid and tradeable asset classes. An integrated financial data platform like Aquata enables seamless portfolio and accounting processes that will deliver the competitive edge.
