Financial institutions have long contended with demands to modernize their business and build scale for their operating platforms.
Substantial resources and cross-functional collaboration are essential to architect a platform that combines internal and external components at each stage of a firm’s data value chain. The proliferation of data and tools can mean a time-consuming selection and implementation process.
As a result, many firms need support in the lifecycle of adapting generic technology, sourcing data, and generating insights. Leaving these important aspects of the value chain unresolved can lead to data quality issues and sub-par business outcomes.
If your organization is asking questions like “Why is reporting never quite right?”, “Why does it take so much manual effort to address recurring and ad-hoc data requests?”, “Why do processes take so long?”, or even if you simply whisper to yourself, “Why is our data so terrible?”, this series may be for you.
RELATED READING: Build a Robust Data and Analytics Infrastructure
Fragmented data
One of the primary challenges in managing a modern financial operating platform is fragmented data. Point solutions may have worked well for siloed user groups focused on specific workflows. All too often, though, disparate technology cannot unify a firm’s data footprint to provide a holistic view of the business.
With cloud-based storage and compute costs evolving to enable scale, nearly all buy-side firms are moving aspects of their operation into public and private cloud instances. According to a CRISIL and Google Cloud survey, 9 in 10 firms use cloud instances as their central data hub.1 These hubs enable firms to aggregate and manage internal and external golden sources of data for operational workflows across the front, middle, and back office.
Firms with more data are better positioned to generate more accurate forecasts, diagnose root causes, react quickly to major macroeconomic events, and enhance strategic decisions. However, most firms don’t have the tools to tap into available data or a structured way to organize and retrieve unified, authoritative data to build scale.
Lack of domain awareness
Generic, low-touch cloud-based data platforms are appealing, cost-effective options. However, they can come with hidden costs. The absence of industry and subject-matter expertise built into the data platform is particularly challenging for firms that want to embed a robust data strategy in all aspects of their business.
Domain-aware data platforms – or the concept of a functionality-rich technology foundation with inherent, out-of-the-box investment industry awareness – enable users to structure a wide breadth of data into a single format for downstream insights. By creating a consistent data model, firms can build scalable aggregation across multiple sources for a given data genre. For example, they can support the full investment lifecycle or normalize ESG data across providers to enrich portfolio data.
Without unified architecture, insights generated using the platform will lag and be less accurate due to increased time and effort to integrate new and existing data. Firms may also notice the cost of their data operations grows linearly compared to the amount of data managed. As a result, this can erode a firm’s return on investment for insights generated using the platform.
Underinvestment in data governance
The governance layer is critical to a robust data platform and ensuring the right downstream users have high-quality data. Firms must ensure they incorporate all elements of governance — including data cataloging, quality control, lineage, security and access, and versioning frameworks — into their data platform.
While the goal is to automate as many components of the governance framework as possible, firms must understand the role that people and processes play in achieving a high-quality data platform. Relying on decentralized teams to define, execute, and manage vast amounts of data for various stakeholders as a part-time function with little accountability is a recipe for disaster. Data governance is an organizational function that must be properly resourced as a Data Operations team’s full-time job. Resources with full responsibility and accountability over critical data workflows — like validation, exception management, and administration — promote downstream analytics attribution and trust in the data.
Unharmonized data flows
Poor design that feeds downstream user applications, but doesn’t consider how data is staged for consumption, can be a leading cause of data inconsistencies.
Staging data through a unified data matching and mastering solution creates a golden source of operational data users can trust. A consistent identifier, reference data, pricing and valuation information, and market events taxonomy throughout the investment lifecycle enables reliability and efficiency across strategies. Managing operational data at even the most granular level provides a foundation for accurate bottom-up position, transaction, portfolio, asset class, and geography-level insights.
Many data platforms cannot support their firm’s requirements and aspirations. To continually empower growth, modernizing, re-platforming, and harmonizing all systems is a table-stakes requirement. For firms gearing up to scale AUM through new asset class expansion, business launches, or fundraises, a centralized data platform is critical to enable success and power unique insights.
