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Special ReportData Management

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Special Report Data Management

Data represents the lifeblood of all capital markets firms’ operating environments. But thanks to new regulatory requirements, firms are having to take on great loads of information, while at the same time they need clean and reliable data to manage risk and generate alpha. It’s a balancing act between data management and analysis, overseen by a proper data governance structure.

Lifeblood Improving the

Q Which business processes across the buy side and sell side are currently most reliant on clean, consist-

ent data, and why?John Bottega, senior advisor and consultant, EDM Council: Every function is reliant on clean consistent data. But what we have seen in the past several years is a focus from a “defensive” posture of data to an “offensive” posture on data. One of the outcomes of the financial crisis has been an increased focus on regulation—specifically, focus on risk. Risk requires consistent, timely and accurate data. But it also requires consistent and accurate “engineering” of data, including proper design of information so that inherent linkages and dependencies of data are exposed and known. Linked risk, counterparty risk and capital management are all depend upon this interconnectedness of data.

John Fleming, chief data governance officer, BNY Mellon: The world of analytics has exploded under the umbrella of big data. As a result of that, people are looking for new insight into the data that they have. To perform analytics and to get value out of what you’re analyzing, your data has to be good, clean and fit for purpose. Analytics on troubled data is going to lead to undesired results.

But I think the big change right now—in a world where you could argue that not a lot has changed—is the fact that the regulators have come in and given their perspective as to what “good data” means, and what the management of that data means. Everywhere you turn around in finance and in other sectors, you’re seeing the hiring of data scientists and putting together large analytics projects.

Michael Engelman, director, North American data practice, GFT: For the sell side, you’re looking at the monitoring and reporting of risk—both market risk and credit risk—and regulatory compliance, ranging from the Volcker Rule and BCBS239, to anti-money laundering (AML) protocols. On the buy side, strong data management is critical for monitoring fees, along with a number of reporting needs, including internal and external position report-ing, performance reporting, and financial reporting. All of these processes require a consistent, consolidated, cross-asset class and cross-region view of the firm’s trading activity and positions.

In order for these processes to work correctly, these building blocks need to be gathered horizontally from multiple trading and accounting systems within an organization, and represented in a standardized way. This allows the data to be resolved to a consistent set of reference information and harmonized across an enterprise.

Roundtable

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Dominique Tanner, head of business development, SIX Financial Information: On one hand, you have the core operational business processes like order routing, clearing and settlement or corporate actions processing, which tend to be highly automated. Any defect in the data that drives those processes leads to exceptions, which usually need to be resolved manually, causing delays and increasing cost. On the other hand, you have your high-stakes processes, which are less time critical but can have a considerable impact financially or reputation wise. These include client on-boarding, regulatory reporting or tax reporting/process-ing. Portfolio valuation for NAV calculation on the buy side or risk management on the sell side are other examples.

Paul McInnis, head of enterprise data management, Eagle: You really can’t overstate it—your data has to be clean. If people within an organization—whether it’s in the back, middle or front office—have reason to question the veracity of the data or come across inconsistencies, they will lose faith in it and disregard it altogether.

That being said, there are a few areas in particular, in which there is quite literally zero room for error. Functions and processes such as trade execution, compliance and regulatory reporting, risk and exposure measurement, and client reporting are all areas in financial services where “being close” doesn’t cut it. The operational and reputational risks are too great.

Q What are the biggest challenges facing

buy-side and sell-side firms in terms of their data man-agement practices? Are these challenges mostly technology related, or are they a mix of technology and operational/governance issues?Bottega: There are many challenges that face banks with respect to their data management. If I had to narrow it down to few key challenges, I would say first, the challenge of legacy systems and environments. We have many years of disparate infra-structures and business processes that make holistic data management difficult. Second would be culture. Banks have to realize that one of their most valuable assets is their data, and they should treat it as such.

Thus, changing their business processes and mindset (their data program support and governance), to properly acquire, curate and utilize data according to industry best practices is key. Finally,

and probably most important, is driving consistent meaning of data. As an industry, we have been focused—and are pretty good at—data “processing” and the mechanics of capturing, storing and moving data. But we have not been good at managing “con-tent”—getting agreement across an organization or, dare I say, across an industry, as to the semantic meaning of data.

Fleming: One of the things that a good data governance program will create is the notion of helping people understand what data is important, versus all data. You can’t stand on attention for all

the data in the firm; there’s just too much of it.

Years ago I worked for a gentle-man who taught me this phrase: People know what they do, but not what they are doing. People who are in a data manufacturing job never understood what the implications of getting it wrong meant; they did what they did and other people would come along and fix that data. One of the very important things that’s come along is this idea of data lineage: Where has this data come from that you’re using, and how good is that data? So for the first time, people are starting to think about that end-to-end process of data.

The last piece is putting together some kind of metrics or scorecard to go over. When you have so much

data, you need to figure out how good the data is and you have to provide some kind of empirical evidence back to the firm.

Engelman: Most financial services firms move data from place to place and transform it into multiple formats. This constant transformation introduces hundreds or thousands of reconciliation

Paul McInnisHead of Enterprise Data ManagementEagle Investment SystemsTel: +1 781 943 2200Web: www.eagleinvsys.com

“Years ago I worked for a gentleman who taught me this phrase: People know what they do, but not what they are doing. People who are in a data manufacturing job never understood what the implications of getting it wrong meant; they did what they did and other people would come along and fix that data. One of the very important things that’s come along is this idea of data lineage: Where has this data come from that you’re using, and how good is that data? So for the first time, people are starting to think about that end-to-end process of data.” John Fleming, BNY Mellon

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Special Report Data Management

points, which is extremely costly and introduces potential failure points into a firm’s operations. Moreover, large-scale metrics and measurement programs are nearly impossible to execute if data representations are inconsistent across the firm.

As a result, the majority of data management programs are being executed in silos; front-office repositories, risk repositories, compliance repositories and finance repositories each transform and reconcile the data in separate functions. This creates duplica-tion of effort and cost and does not move the bank forward towards a common understanding of data on an enterprise level.

These difficulties are further compounded by a lack of common standards and common data language. As most banks do not understand their data lineage to the degree required to allow the business to confidently state that the information they are viewing is correct and complete, critical components to successful program delivery—such as data definition, documentation, test-ing and implementation—are often left out of corporate software development life cycle processes.

Tanner: Managing the complexity of normalising and mapping all inbound data formats to the required outbound data formats poses a sizeable challenge to data managers within buy-side and sell-side firms alike. The solution requires a clear focus on the manage-ment of metadata. Managing metadata needs technology, i.e. data dictionary systems, clearly defined processes and a corresponding governance framework that defines ownership and responsibilities. But above all it requires expertise of the underlying data formats and its interpretations, as well as its usage across data sources and con-suming systems. Acquiring and maintaining this expertise within the data management function is key to a sustainable solution.

McInnis: The biggest challenge facing the industry unquestiona-bly revolves around data governance. You saw it during the global credit crisis in 2008, when very large and diverse asset managers were basically in the dark as it related to their total exposures. For many, their data was in silos and they didn’t have the tools to be able to go in and quickly figure out their liabilities or the

risk sensitivities across their entire portfolio. Thus, following the crisis there was this renewed appreciation, industry-wide, for an Investment Book of Record (IBOR), for example.

There is growing awareness that data governance is not an “IT problem” anymore; it’s been elevated to the business owners, particularly as regulators, clients and other constituencies demand accurate, consistent data. Data lineage and the ability to perform an audit trail are also vitally important given the regulatory scrutiny.

But there is still a level of cultural resistance. For instance, when you think of the financial services industry today, you’re talking about these sprawling global organizations that operate across vari-ous asset classes and across geographies. So it can be a herculean task just to get enterprise-wide alignment on a business conceptual ontology standard, one that provides a strict and immuta-ble interpretation of the structure and obligations of financial instruments, legal entities, market data and financial processes. It seems simple, but it’s incredibly complex, and this work is vital. This is one of the reasons there needs to be a strong advocate from within and atop the organization to ensure there’s buy-in company-wide.

Q Is it realistic in 2015 to expect capital markets firms to design and build enterprise data warehouses to

cater for their data needs, or does the answer lie with discrete data management tools?Bottega: This assumes that building a data warehouse is the correct approach to implementing data management. Warehouses and discrete data management tools are just that: tools towards an objective. In certain cases, warehouse implementation may not be the best approach. Other technologies like visualization and semantic technologies are introducing new ways to manage information. In my opinion, the goal in 2015 should be to drive consistent meaning of data, coupled with adopting and driving a program of disciplined and organized best practices. With this as the foundation, technology becomes the enabler of this new “data hygiene” and can utilize any number of technology solutions so long as they build in support of these principles.

Engelman: Financial services firms are seeing a “new normal” environment in 2015 in relation to regulatory reform and data management. To meet these regulations, firms must not only have tighter governance policies, but also robust and secure systems for storing and sorting through the mountain of data that needs to be collected to produce the required reports.

John BottegaEDM Council

Michael EngelmanDirector, North American Data PracticeGFTTel: +1 212 205 3400Web: www.gft.com

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Roundtable

As a result of these new needs, capital markets firms are turn-ing towards data management tools to provide them with crucial capabilities; in addition to automating the data governance process and housing data definitions, these tools can be used to design and build data warehouses. However, tools alone will not ensure the delivery of a successful data warehouse. Sound information architectures must include the components of data modeling, data quality, data stewardship and governance.

Tanner: Different strategies can be applied. Centralised data repositories or data warehouses presenting the data in a uniform and consistent way would ultimately solve many of the issues common in today’s environment. Some firms have already or are close to achieving this, but many have learned that this is a com-plex time- and resource-consuming endeavour. The environment changes constantly, not least due to the volume of new regulation introduced. The business wants to see a shorter time to market and benefits realised in months and not years. This requires more agility in data management and with this the application of tacti-cal solutions to specific data needs. Discrete tools or systems can help gaining speed and flexibility but on the other hand increase the cost and complexity of the overall data management function.

McInnis: I really don’t think firms can achieve what they want to—or need to—with either discrete data management tools or an in-house enterprise data warehouse built from scratch. Discrete tools will just result in siloed data, which creates the governance issues I mentioned earlier. It’s one of the reasons Eagle offers a continuum of services that span from an on-premise solution to a co-sourced or fully outsourced offering through BNY Mellon.

Q Can capital markets firms realistically look to partner with specialist data management vendors and

“outsource” their data management functions? If so, what functions would such an arrangement cover? Fleming: The vendor community has multiple responses to these problems, and each of them has their own spin. Some of the larger vendor companies have the perspective, “If you do everything with us, you won’t have a problem.” The fact of the matter is that there’s not a single firm that has just one way of doing stuff. Where we really need to focus is that a lot of people are focusing on structured data—because that’s what they know best—but what we really need to do is look at the world of structured, unstructured and semi-structured data. The problem is that there are different classes of tools with different capabilities, so that’s an area where the industry could do better.

Engelman: Capital markets firms have rightfully developed a mixed view of outsourcing. On one hand, the cost savings and increased efficiencies that are promised by outsourcing vendors are

too tempting to ignore, particularly when considering the continu-ously shrinking margins in the “new normal” world. On the other hand, the increased governance and communication problems that result from offshoring arrangements frequently outweigh the ben-efits offered by outsourcing, particularly for processes as sensitive and complex as data management and regulatory compliance.

Capital markets firms must ensure that their selected vendor can work as an extension of the firm, operating in a collaborative outsourcing, or co-sourcing, business paradigm.

Tanner: Financial firms are increasingly looking at other sectors to find ways of industrializing their own value chain. Firms no longer see the need to produce all services in-house, specifically if they are not considered to be part of the firm’s core competences. Data management is an obvious candidate in this respect. Key concepts of this strategy are process innovation and standardisa-tion. Existing processes need to be analysed and their potential for standardization needs to be assessed.

Those with a high potential, usually more routine and less firm specific tasks can easily be handed over to an external partner. They may include data feed management, system operation, data checks and validations. On the other side there are those with less potential. They could include exception handling for time critical processes, managing changes with wide ranging impacts on downstream systems or producing value added data which is unique to the firm.

McInnis: Absolutely. That’s where the market is going, toward managed services and a service-oriented architecture, or SOA. Firms don’t really care who provides the security master record, as long as it’s clean and accurate. It’s the integration piece that clients are increasingly seeking to hand over because it requires a tremendous amount of bandwidth to keep up with it, as the inputs are changing all the time.

Third-party vendors, who are working with hundreds of firms, have the economies of scale to dedicate the necessary resources to stay on top of things like alterations to benchmark feeds. Also, it doesn’t necessarily help clients if they simply outsource one piece

Dominique TannerHead of Business DevelopmentSIX Financial InformationTel: +1 203 353 8100Web: www.six-financial-information.com

Special Report Data Management

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of it, or just one component of their data, because it still requires considerable resources to monitor and integrate everything else like core security data, ratings and corporate actions.

Q Typically where do capital markets firms go wrong when it comes to managing their data? What areas on

data management do they tend to overlook or underestimate in terms of cost, time and complexity?Bottega: In general, although we’re getting better, people need to realize that data management is not a finite, short-term project, but a multi-year, culture-changing initiative that must become part of a firm’s operational DNA. I’ve seen too many projects cut short because the expectation was immediate return on investment.

Data management will improve efficiency and increase transpar-ency of data, which leads to better risk management and marketing. But this will take time (and money) to build the proper infrastruc-tures needed to support these goals. Firms have to continue to realize that there is a cost to all this—not so much in building good data practices, but in not doing so. A fragile and fragmented information infrastructure was not helpful during the financial crisis. Strong discipline, with accurate and transparent content, is critical to sound-ness and safety of the banks and the industry.

Engelman: Firms that govern data in an ad hoc fashion are inher-ently setting themselves up for failure. The collection, storage, reconciliation, and analysis of data is unavoidably complex, mean-ing firms that do not implement a formal governance program stand to lose substantial time and money in data management. To avoid such expenditures and ensure enterprise-wide alignment, data governance programs must be highly standardized, adhering to a common language and business-domain model. A centralized process and dictionary for standardization should be supported by a common technical infrastructure to ensure the information derived from the data is consistent across the enterprise. This standardization requires that compromises be made across business applications, which introduces complexity in finalizing and sign-ing off on specifications. Moreover, keeping standards current can only be accomplished by establishing an organizational structure that owns the standard (i.e. a run the bank function)—which can be difficult year over year as budgets and priorities change.

Ownership of these tasks often falls to IT organizations, who lack the authority to define and enforce standards or to implement change. This power vacuum often results in decentralized data management, which causes a multitude of problems. Without central alignment on how adjustments are made and more impor-tantly, how they are communicated, it is nearly assured that the same adjustment will be made multiple times within the organiza-tion and may be made in an inconsistent way. An overall approach to the communication of adjustments across all data repositories is important to ensure the integrity of data.

Finally, and perhaps most crucially, firms must ensure that their employees are capable of understanding institutional data. Most capi-tal markets firms do not understand their data lineage to the degree

required to allow the business to confidently state that the informa-tion they are viewing is correct and complete; knowledge of data is, in many cases, limited to what people view on user interfaces. This is further challenged by vendor proprietary systems, attrition of staff supporting in-house systems and lack of usable documentation.

What you really want to have is a good data architecture program. Every time you look to build a new system along the way, you don’t want people going out there and just solving the problem for themselves. How are people going to take the data? Consume the data? And if this new system is going to create data, you have to make sure that you’re not making another copy of something that exists someplace else. You need to have something like an data architecture review board for the on-boarding of data to make sure everything is tied together.

Tanner: For some data, it is essential to keep a history over time and to apply historic changes to them. Imagine a regulator spotting an error in a report that was produced three month ago and asks for a correct one covering the same period. The data that caused the error might be frequently changing and is now in a different state. If you can’t correct the mistake in the history and re-run the report for that period, you would not be able to meet your obligations unless you revert to a manual process of recreating it. This is what many firms have to do because data management did not pay enough attention on identifying data that needs to be keep over time. Incorporating time in a data repository is complex and system support is limited.

One of the reactions frequently seen is that of applying brute force and maintaining a history of everything. This however is very costly and adds a lot of unnecessary complexity. An analysis of the history requirements of the business processes should be conducted, identifying the data points for which history needs to be maintained. A differentiated approach will ensure that a firm can fulfil the essential needs for access to historic data by the business while at the same time manage cost and complexity.

McInnis: Unfortunately, a lot of firms still view data management solely through a “security master data” lens, and fail to leverage the information that’s available to them across their organizations. Make no mistake, data management projects are costly, time-consuming and truly complex, but they’re well worth it when you consider not only the new efficiencies, but the value-add as well.

For instance, I’ll see some firms who extend their data manage-ment capabilities to their investment operations or to complement their portfolio management—be it through an IBOR, business intel-ligence or equity and fixed-income attribution. When you juxtapose the value these firms are able to generate from their EDM capabilities against those that merely see data management as a reporting tool or for pricing, the difference is night and day and you get a sense of the respective ROI potential or, conversely, the opportunity costs. I think we’ve entered a new era in which those that have a robust data management platform in place and leverage it across their organiza-tions, have a distinct competitive advantage over those that don’t. In time, these capabilities will serve as a true moat and differentiator. n

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