About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Unifying Data for the End-User

Subscribe to our newsletter

The data that traders, researchers and risk managers need is often located across many systems. While ETL, middle-ware and traditional golden copy approaches are useful for data management, they don’t provide the business user with an easy way of analyzing this distributed data, says Xenomorph in its new white paper “TimeScape Data Unification”.

A key business and technical challenge for many large financial institutions is to knit together their many disparate data sources, databases and systems into one consistent framework that can meet the ongoing demands of the business, its clients and regulators, the vendor says. For example, post-crisis it is clear that risk managers need easy and more timely access to all kinds of data, regardless of data type (positional, market, entity), hosting system or data source.

Some have responded to this issue by implementing a middleware or ETL-type infrastructure. However, this is a technical solution to the data integration problem and does not go far enough in delivering a business-focused way of presenting data and allowing the more complex business objects to be analysed. As a result, risk management and trading staff are still left with the challenge of how best to access related datasets across a variety of disparate systems.

The data warehouse approach to golden copy data management can be very effective, but this can be difficult to implement in a short time-frame, particularly if the technology used lacks flexibility in its data model and is faced with a variety of intertwined legacy systems. Traditional golden copy management is often siloed alongside a particular asset class, type of data or business function and does not deal with the management of data once it has been delivered to downstream systems and users. Additionally, these traditional data management approaches often lack real-time analytical and business functionality.

Companies looking to satisfy the need for business-user access to data across multiple systems should consider a “distributed golden copy” approach. This federated approach deals with disparate and distributed sources of data and should also provide easy end-user interactivity whilst maintaining data quality and auditability. Access to data is normalised in real-time but the data itself is not duplicated in other databases and systems, hence problems with inconsistency are reduced.

“Data management for the end consumer is an often overlooked area and the federated approach to accessing distributed systems is an attractive architecture, one that can be used to augment ETL and centralized golden copy. This form of data virtualization is a very powerful data management technique, but it should be applied with the needs of both end users and technologists in mind,” concludes Brian Sentance, CEO Xenomorph.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: In data we trust – How to ensure high quality data to power AI

Artificial intelligence is increasingly powering financial institutions’ processes and workflows, encompassing all parts of the enterprise from front-office to the back-office. As organisations seek to gain a competitive edge, they are trialling the technology in variety of ways to streamline and empower multiple use cases. Some are further than others along the path to achieving...

BLOG

Experts Urge Data-Focussed Prep for Asset Management AI Adoption

Leading data practitioners have urged financial institutions to ensure they have suitable data management and infrastructural setups to accommodate artificial intelligence (AI) applications following a report that suggested asset managers are struggling to roll out the technology. The latest in an annual study by professional services giant KPMG found that while asset managers in the...

EVENT

AI in Capital Markets Summit New York

The AI in Capital Markets Summit will explore current and emerging trends in AI, the potential of Generative AI and LLMs and how AI can be applied for efficiencies and business value across a number of use cases, in the front and back office of financial institutions. The agenda will explore the risks and challenges of adopting AI and the foundational technologies and data management capabilities that underpin successful deployment.

GUIDE

AI in Capital Markets: Practical Insight for a Transforming Industry – Free Handbook

AI is no longer on the horizon – it’s embedded in the infrastructure of modern capital markets. But separating real impact from inflated promises requires a grounded, practical understanding. The AI in Capital Markets Handbook 2025 provides exactly that. Designed for data-driven professionals across the trade life-cycle, compliance, infrastructure, and strategy, this handbook goes beyond...