In our latest EY wealth and asset management viewpoint, we consider what asset managers can do to achieve the elusive goal of becoming truly ‘data-driven enterprises’. Joined-up data — the ability to effectively link all data sets in an organization — is increasingly vital to a range of board-level objectives. We believe a holistic data strategy, underpinned by an operating model and extensible technology architecture that deliver joined-up data as a service, is the ideal approach to take. Extensible architecture can deliver a wide range of lasting benefits at similar or lower costs than many asset managers are already incurring. However, we see board support and commitment as vital to realizing the value of joined-up data.

The data revolution

A few years ago, only a handful of observers would have expected data to play a central role in the evolution of asset management. Yet, today, data is the heart of the industry’s development. It has emerged as the fourth pillar of business in addition to people, process, and technology. Firms of every size are working to become data-driven organizations in which vast amounts of information can be analyzed in real time and put to a myriad of uses. Good data is vital to effective management and reporting. Data analysis is seen as a growing source of insight and an increasingly important way to identify opportunities for investment or expansion. For example, joining financial data to investment data can help shape product development strategy by identifying the best performing and most cost effective funds.

Of course, in the real world, few asset managers have succeeded in becoming truly data-driven enterprises. It is not hard to see why. Most asset managers have expanded through a mixture of organic and inorganic growth, going through periods of under and over-investment in the process. In many cases, the result is a fragmented, inconsistent data infrastructure that is overly reliant on manual processes.

In this viewpoint, we provide a high level assessment of the gap between the data ideal and the current reality, and what we believe asset managers can do to bridge it.

Joined-up data is increasingly crucial to creating shareholder value

With the benefit of hindsight, it is easy to see why data has had such a meteoric rise up the agenda of asset managers’ executive teams. The explosion in the volume and speed of data accumulation across the industry — and the interest in developing new analytic capabilities to make sense of that data — has been phenomenal.

Data and analytics are already being used in activities as diverse as client segmentation, investment research, risk management and fraud prevention. Boards are beginning to realize that joined-up data is essential to achieving core shareholder value objectives such as cost reduction, revenue growth, investor satisfaction and, of course, total shareholder return. An effective data strategy is also recognized as crucial to meeting the challenges of digital distribution, enabling asset managers to distribute their products directly to their investors online. The emergence of robo-advisors, the growing power and diversity of Financial Technology (FinTech) and technology giants’ interest in asset management are focusing the minds of established firms on the need to deliver radically-different customer experiences in future, especially to younger investors.

Above all, though, executive teams are aware that compliance with a range of regulatory requirements will be reliant on data management. Dodd-Frank, FA TCA , MiFID II, PRIIPs (Packaged retail and insurance-based investment products) and others call for asset managers to provide regulators with ever more detailed, timely and granular data. Addressing these overlapping requirements (see Figure 1) is the greatest single driver of firms’ need for effective joined-up data. This in turn is creating a broader awareness that reliable, centralized data management systems and processes can help boards achieve many of their strategic objectives.

Many asset managers are struggling to become “data-driven enterprises”

The desire to move from silo-based data management to a more centralized approach is about much more than eliminating duplication and reducing costs. It also reflects growing awareness of the value hidden in asset managers’ data and the potential synergies of comparing or combining different data sets. Ideally, firms would like to achieve strategic insight by having total transparency of their operational and investment performance — gaining the ability to “view the organization as a portfolio.” This not only implies a holistic view across all securities, asset classes, legal entities, products, clients and markets, but also the ability to look at that data from a range of different perspectives such as compliance, profitability, and investment performance.

Many asset managers are spending significant amounts in their quest for transparency, but there is no consensus on the best approach to follow. Multiple requirements from a variety of directorates are encouraging some firms to pursue a piecemeal, silo-focused approach. This can appear to achieve comparatively quick, low-cost wins but, in our experience, isolated projects rarely provide a lasting or reliable solution. They are often a response to the limitations of existing technology, or the result of excessive delegation to a single team or function, and, when replicated across a large organiz ation, they inevitably lead to cost multiplication.

At the other end of the spectrum, some asset managers are migrating to completely new, full-featured investment platforms supplied by major software vendors. “Big Bang” projects such as these have the potential to be more effective but, without the right governance and oversight, they tend to overrun in terms of time and cost. They can also generate uneven value for different functions or business units, and are often poorly integrated into the broader organization. Additionally the lack of flexibility impedes organizations when requirements change.

EY - Figure 1: the overlapping data demands of regulation

Figure 1: the overlapping data demands of regulation

A holistic data strategy is the key to unlocking joined-up data

Whichever approach they choose, we believe that too many asset managers are failing to derive full value from their investments in data. However, we see an alternative way to achieve data transparency that combines the benefits of both approaches.

This alternative approach involves creating a new operating model for data management, made up of several vital elements. The first is a lightweight governance structure that unites senior executives such as the CFO, COO, and CTO around data. This coordinates core shareholder value objectives with tactical data goals, key performance indicators (KPIs) that measure success and the levers available to staff — empowering the whole firm to deliver joined-up data. That governance framework then needs to be supported by an integrated organizational structure that brings all data management skills and processes — including technology, finance, risk, compliance, operations and change management — under the centralized oversight of a single figure, such as a Chief Data Officer.

Finally, the operating model is underpinned by an extensible technology architecture that delivers joined-up data as a service. The defining feature of this approach is that it places an infrastructure for gathering, cleaning, analyzing and reporting data on top of an asset manager’s existing systems. Instead of creating a data warehouse, it captures data from a wide variety of sources and uses multiple metadata — descriptive features — as the basis for subsequent analysis. Conceptually, this extensible architecture is made up of three “layers” ( see Figure 2 ):


The defining feature of this approach is that it superimposes an infrastructure for gathering, cleaning, analyzing, and reporting data onto an asset manager’s existing systems. Instead of creating a data warehouse, it captures data from a wide variety of sources and uses metadata — descriptive information — as the basis for subsequent analysis.

EY - Figure 2: conceptual design of extensible architecture

Figure 2: conceptual design of extensible architecture


The extensible architecture can also be defined in purely technological terms (see Figure 3). Once up and running, the architecture delivers joined-up data by:


The importance of the ‘overlaid’ nature of extensible architecture is hard to overstate. It enables users to drill down into data where required, but also to extract simple, single-figure measures of performance, cost and risk. Equally importantly, it allows firms to leverage their existing technology without the need to decommission systems — even if multiple platforms are performing similar functions — and permits underlying platforms to be maintained or upgraded without affecting analytics or reporting. It makes use of unstructured external data, as well as proprietary information. Finally, it allows analytics and reporting to evolve dynamically over time.

Extensible architecture can be implemented internally or externally, on a managed service basis. In our experience, either approach can work well, as long as asset managers have access to the right capabilities. Technological expertise alone is not enough; firms also need regulatory, tax, and legal knowledge, along with guidance on best practices in management information, financial, and regulatory reporting.

This approach delivers lasting simplicity, not temporary fixes

In our experience, using extensible architecture to deliver joined-up data can bring asset managers a wide range of operational and strategic benefits. These can be summarized as “simplicity on the far side of complexity.” In other words, by giving firms a granular understanding of their data, extensible architecture enables them to derive clear, powerful insights from it.

This contrasts with the illusion of simplicity provided by a smaller, piecemeal approach. Projects focused on individual data requirements tend to simplify each problem down to the bare minimum of delivery, increasing inefficiency, and only achieving temporary solutions. Simplicity on the far side of complexity can help to achieve a range of internal and external objectives, as outlined below.


Looking further ahead, we believe that the simplicity, adaptability, and flexibility that extensible architecture delivers will also give asset managers some lasting strategic advantages. Separating the reporting layer from underlying systems makes it easier for firms to grow. This includes the ability to integrate acquisitions without affecting key reporting processes — reducing the cost and risk of M&A — as well as to incorporate new platforms as products and markets evolve.

Value lies not just in joining up data, but also in joining up spending

We recognize that some asset managers will be instinctively wary of using extensible architecture to deliver joined-up data. In our experience, initial skepticism is typically based on perceived levels of cost or disruption.

These are understandable views, given the institutional fatigue that many asset managers are experiencing after several years of upheaval. All too often firms will already have made failed attempts to deliver joined-up data. Historic obstacles to success can include cultural factors, such as organizational inertia or a lack of broad-based stakeholder support; legacy issues, such as complex organizations or data landscapes; and governance weaknesses, such as poor accountability or the absence of a robust data model.

Even so, we believe that there are powerful arguments in favour of extensible architecture. The most important point is that most firms are already making heavy investments in data-related projects, even if fragmentation between work streams may mean that this is “under the radar” or simply seen as a cost of doing business. By coordinating and centralizing existing spend, most asset managers could implement an extensible architecture approach for the same — or lower — level of cost that they are already incurring, while realizing far greater benefits.

Furthermore, it is worth remembering that regulation will, in any case, compel all asset managers to make significant changes to their data infrastructure in the coming years. The benefits of an overall end state that all projects contribute to are illustrated by a quick look at the requirements of MiFID II. By 2017, European firms will need to be able to report total investment costs at client, portfolio, and fund level. Successful compliance with MiFID II is likely to involve many of the elements covered by an extensible architecture approach (see Figure 3).

In summary, we believe that channelling current areas of effort and expenditure into the creation of a new operating model for joined-up data will overcome historical obstacles, enabling asset managers to meet all their data management goals without increasing their total level of expense.

EY - Figure 3: architecture required for MiFID II compliance

Figure 3: architecture required for MiFID II compliance

Joined-up data is a board-level issue

An extensible technology architecture that sits above existing systems offers many asset managers an ideal way to improve the value of their data while reducing disruption and cost. However, we could not conclude this viewpoint without stressing that clear central leadership is critical to achieving the full benefits of a holistic data strategy.

Vision, drive, and support at board and C-suite level are essential to achieving buy-in from all functions and business units, and to confirming that the approach is implemented fully and successfully. This includes creating an information management framework for the whole organization covering data governance, quality, usage, management, and architecture. Otherwise, weak links in the chain will develop and firms will struggle to extract full value from their investments. Board-level commitment is also vital to fostering a culture which recognizes the value of data management and prioritizes accordingly.

In short, joined-up data is not a concern for one function or department. It needs to be identified as a board-level priority. Executive teams can then set a holistic data strategy supported by an extensible technology architecture which realizes the value of a truly data-driven enterprise.

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