By Don Huff, Global Head of Client Services and Operations, Bloomberg and Maureen Gallagher, Head of Enterprise Reference Data, Bloomberg.
Data has quickly become the hottest commodity in the financial sector: trading and investment teams are laser-focused on accessing the best, newest data to get an edge on the competition. While this arms race for data acquisition has captured headlines, there is another race taking place behind the scenes to fuel this data revolution. The race everyone is on is to build the strongest, most resilient data architecture to future proof and meet the evolving needs of business across the front, middle and back office.
Whether you’re reading this because you need more data to run your business, are focused on data acquisition, or are in charge of fortifying your organisation’s data architecture to withstand an ever-widening universe of data, this piece is for you.
In this article, we’ll explore the value that advanced, sophisticated data architectures deliver for financial institutions, and strategies to build stronger, future-proofed architectures that can evolve with your organisation’s insatiable appetite for data.
The Value of Strong Data Architecture
As data scientists know well, data architecture refers to the processes financial institutions have in place to manage, organise, store, model and access its data. Investors are locked in a race to reduce the time to value – how quickly they can derive insights from their data assets – in order to make differentiated investments in the market. In this environment, financial institutions increasingly need data architectures that facilitate seamless access to consistent, high-quality data across various workflows and applications. Adding to the complexity, firms are also dealing with the idiosyncrasies of data, including sourcing and organizing information from multiple providers, in order to glean insights that the market has not already discovered.
Think about your data architecture challenges just two or three years ago – or ask data engineers on your team. Undoubtedly, the challenges have changed significantly and at multiple points of your tech stack: as your organisation migrated to the cloud, transitioned to new operating models or looked to harness data to feed AI models.
This is a common challenge many financial firms face as they navigate the advancement and increasing adoption of AI and continued migration to the cloud. Automation and operational efficiency are paramount, as organisations strive to utilise technology to streamline data handling while addressing the complexities introduced by increased data volumes and regulatory requirements. By removing data barriers and allowing investors to access data in their preferred cloud environments, firms can position themselves to fully utilise the capabilities of advanced AI models that thrive on real-time, high-quality data inputs, minimizing costly delays. This not only fuels innovation within a firm but also strengthens its positioning within the industry, ultimately driving innovation and competitive advantage.
While these technologies introduce new opportunities for operational efficiencies, they also add additional layers of complexity. As firms prepare for the next generation of AI-driven tools, they must also consider the foundations of their data architecture and data quality with an eye toward guaranteeing that new technology can both function properly and effectively optimise workflows.
Future-Proofing Your Data Architecture
Your data architecture is the starting point of your future and ensuring that it is evolving with the others so that it continues to be accessed in your target state as that target state shifts.
So, what does it take to build a future-proofed data architecture?
Think of strong data architecture for a financial institution as the most complex Dewey Decimal system known to man – a library of precisely tagged and stored data with shelves that appear to have no limit, yet a single data point can be located with a simple keystroke. And librarians are tasked with maintaining this speed, even when this library triples in size – perhaps as investors diversify into new asset classes like private markets to keep pace with market trends.
Now imagine that the same meticulously maintained library needs to get transported to a different city, without disrupting the data architecture. That’s akin to the onerous task data engineers face when their organisations are at critical junctures, such as changing their target operating models or migrating to the cloud.
The most important attribute to a strong data architecture is establishing a Unified Data Model. Much like the Dewey Decimal system, a Unified Data Model refers to data that is labelled, stored and managed following a rigorous set of rules that are consistent and interconnected.
The value of this type of coherent operating model is that it unifies data management, analytics and investment processes. This enables investment managers to effectively navigate the data-driven era and achieve better outcomes. This non-fragmented model integrates technology, expertise and processes into a unified ecosystem, allowing investors to efficiently harness data for more informed decision making.
Interconnected data also helps enable seamless access to high-quality data that is ready for use across various applications, enabling AI tools that can perform complex tasks quickly and efficiently at scale.
Building Strategic Differentiation in the Age of Data Overload
The question of how to efficiently extract value from an abundance of data has become a top priority in today’s data-hungry investment landscape. Ultimately, the drive toward maximizing data ROI hinges on a strategic combination of technology and modern data architecture.
By establishing a data and technology framework that stems from a Unified Data Model, and focuses on interconnected data, accessibility and scalability, firms will reap the benefits of increased automation and efficiency. These benefits will be crucial to tomorrow’s investors as they continue to optimise their strategies and workflows to maintain a competitive advantage.
With all eyes on the financial industry’s insatiable appetite for data, we know the real story is taking place behind the scenes – where data engineers are locked in the world’s largest Lego competition to build the biggest, strongest and most resilient data architecture – built to withstand today, tomorrow and whatever the future holds for finance.
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