The unseen burden: How data fragmentation impacts CDO budgets and compliance

Modern supply chains are dispersed, decentralized, and inherently complex to create and optimize. Whether due to geographical, operational, or technological factors, the lack of cohesion among parts can create adverse consequences, including an increase in costs and a reduction in responsiveness, while also blinding an organization to the inner workings, allowing risk to go unmitigated.

While it does align with the traditional definition of a supply chain, fragmentation in sourcing and supply of data to financial institutions is one of the more recent ‘supply chains’ to come under scrutiny.

Many financial institutions face challenges in procuring and managing data sets to inform investment strategies and support vital business operations. In this context, data fragmentation occurs when data is derived from disparate sources, leading to breaks in data lineage. Simply put, a lack of data lineage means that an institution may struggle to determine where data comes from, how it was or is used, and the impact of changes on the data.

While it is an issue many industries face, for capital markets firms, it is particularly problematic as these firms integrate vast volumes of complex data to facilitate quick and accurate decision-making. They also face an increasingly complex and demanding regulatory environment governing the gathering, storage, and use of data.

A lack of data lineage can impact the quality and accuracy of a firm’s decisions and, therefore, cause clients and stakeholders to lose confidence and seek alternative institutions. The inability to establish and maintain data lineage can also attract regulatory scrutiny. So, how can financial institutions address these challenges, and what is the role of the Chief Data Officer (CDO)?

Navigating data fragmentation: Risks, rewards, and the evolution of the CDO

The data acquisition, management and distribution process has a significant bearing on the quality, consistency, cost and value of data downstream. Operational efficiency becomes essential to an institution’s future success since there is more data to consume and more systems to feed.

To address data fragmentation issues, data managers must tackle the inherent challenges that result from an institution’s insatiable demand for data.

First, symbologies and identifiers vary across sources. Basic data structures and data sets are different, requiring significant time, effort and analytical computing power to make them homogeneous.

Second, financial institutions will notice inconsistencies and contradictions in asset valuations and risk calculations when the data feeding these models is inconsistent or when data sets are incorporated at different frequencies. If a trader and a risk officer have different risk values, they cannot work effectively together, or worse, real risk exposure could be overlooked.

These factors can create disconnects and breakdowns between the front, middle and back offices as inconsistencies from multiple data sources are resolved. Ultimately, data from various sources without robust data lineage underpinning it can lead to higher operational risks and costs.

A Bloomberg qualitative study conducted last July of Chief Data Officers (CDOs), still a formative role in many companies in Asia, found they were grappling with educating key internal stakeholders on the importance of quality data, governance and compliance, particularly in the face of sweeping regulation through Dodd-Frank, MiFID II, GDPR and BCBS239.

The study found that as the CDO role becomes more established and companies get on top of regulatory requirements, CDOs typically focus on surfacing efficiencies, value and insights from business data. For many, this results in the need to tackle data fragmentation.

Data in Asia’s multi-dimensional regulatory landscape

Across the financial services sector, using multiple data sources ultimately results in greater complexity, requiring more significant investments in human and technology capital to create and maintain a more robust approach to data acquisition and usage. For example, multiple distribution technologies can result in duplication in integration and maintenance. The significant data overlap creates inefficiencies. Fragmented data sources require multiple contractual relationships and models, which only increases complexity.

In Asia, data fragmentation has tended to be multi-dimensional, ranging across geographies, jurisdictions and regulatory regimes. While the EU and US are universally bound by regulations such as MiFID II and Dodd-Frank, Asia has no singular regulatory overlay. Combined with radically different political, economic, markets, and business environments, high-level data consolidation can be challenging to achieve in this region.

Calls to better understand and integrate the data supply chain are growing, however, not least because of the increasingly borderless nature of both data and delivery of financial services and the need for companies operating in Asia to still comply with or at least achieve alignment with the US and EU requirements.

That said, Asian firms have an opportunity to leapfrog their Western counterparts. Asian banks, for example, have started building their own technology and have had to make tough data choices, including whether to use one or multiple data sources. Increasingly, machine learning and predictive analytics are compelling companies to build their data lakes in-house so they rely on a single source versus many third-party data sources.

The emerging market opportunity: One data, one source?

The financial services industry’s data needs are broad and ever-evolving. There is a growing need for coverage of all asset classes and instruments. This requires trustworthy reference and master data, real-time market data, pricing and valuation data, business-critical analytics and risk calculations. Real-time data is particularly critical for firms assessing market liquidity, tracking volatility and managing risks.

Notwithstanding these factors, wide-ranging regulatory and accounting regulations mandate creating and maintaining credible and defensible data linkages. This creates a dichotomy. With data needs becoming more complex, there is a parallel need from CDOs for simplification of data and technology processes.

Interestingly, some financial services players in emerging markets may have an advantage over established global players. Being cost-sensitive and often sparsely resourced ironically creates a hedge against falling into the fragmented data trap. Many firms prefer lighter, cloud-ready technology platforms that offer flexibility and agility. With these factors in mind, acquiring high-quality data from a comprehensive source can help create efficiencies in cost and compliance management by reducing the knock-on technological, energy, legal and operational costs associated with multiple data sources.

Over the long term, many financial institutions may see the wisdom in simplifying their data lineage by relying on one data source for their primary data needs. A single data source, delivered by a reliable technology partner, creates data consistency across desks, reduces data breaks across business workflows, and reduces operating risks and overall data costs.

While cost is the greatest pain point CDOs face today, many institutions find that reducing the number of data vendors and relying on a single primary source can generate significant cost savings. With our comprehensive data sets, best-in-class data distribution and unified data model, more and more firms turn to Bloomberg to manage all their data requirements. Data managers we have worked with tell us they have halved or saved up to two-thirds of their data management costs.

One fact is beyond dispute – as market data, analysis and insight become even more critical and regulatory requirements evolve, access to quality data through less fragmented sources will rise up the agenda of CDOs and global data leaders around Asia.