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Data ManagementA Foundation for High Performance in Capital Markets
The concept of data management is relatively simple. Firms need access to high-quality, relevant data, provided in a timely and cost-effective manner. Delivering on this concept, however, is challenging, particularly in a dynamic environment marked by significant regulatory changes.
Capital markets firms have shown an increasing awareness of the role of data management in reducing errors, lowering operating costs and mitigating risk. These issues are now front and center in the minds of industry executives, especially since the credit crisis of 2008 and the subsequent proliferation of regulatory initiatives that have been or will be implemented in the near future. At the same time, the growing globalization of the securities markets, along with the continuing consolidation of the industry across borders, creates additional layers of complexity, both in aggregating data and in making sure that it is delivered in a useful format to locations and businesses within an organization.
While capital markets firms are keenly aware of the problems – and opportunities – presented by data management, the typical approach is to address each issue as it arises (or as it demands attention) rather than as an integrated Master Data Management (MDM) effort. New technology solutions may help solve specific concerns, but many firms neglect the non-technical aspects of data management, including culture, communications, and governance, that can make or break the success of an overall program.
Good Data Management Starts with Good DataOne of the most important – and most overlooked – elements of good data management is the quality of the data. Buy and sell-side firms often find that their organizational structures can work against them. Many firms still maintain a decentralized approach to gathering and storing data. This, added to minimal standardization of data and a lack of effective governance, can lead to a high exception rate - as much as 15 percent of data purchased from outside vendors, by some estimates - which affects up to 50 percent of securities owned.
Firms should keep three guiding principles in mind to establish and maintain high quality data within their organizations.
1. Data should come from multiple sources. No single source of data is of high enough quality to be relied upon exclusively. All data sources have their strong points and their weak points. Data that is derived from multiple sources provides the ability to validate against defined rules.
2. Data cleansing requires a “four eyes” approach. When correcting data problems becomes an operating and/or process issue, good data governance dictates that at least two sets of eyes – the person who is making the change, and an expert in that type of data – review and sign off on the item. Proper governance for change and review procedures is essential to avoid compounding the issues being addressed.
3. Data quality should be measured. Many firms try to measure data quality in terms of the potential impact of errors to the business. A flawed piece of data requiring a cancelled trade, for instance, leads to a “measurement” of that cost, without a clear understanding of how good or bad the underlying data really is. Rather than trying to measure the cost of bad data, firms should be able to objectively evaluate data quality, with analytics to track improvement over time.
Data that is measured by consistent standards provides the basis for good business decisions. This is especially true in areas such as risk management. A firm evaluating its position in a particular asset class will have difficulty making accurate assessments if the underlying data is of poor quality. Firms should strive to have all data measured against a numeric score representing clear criteria.
There are two basic approaches to managing data. The first, the enterprise data approach, assembles all data in one central location, managing it centrally and then distributing it where needed. The other, the master data approach, keeps data in different places but creates a consolidated, centralized view so that management can keep an eye on the big picture.
Securities data generally works best in an enterprise model. After a gold copy is created, the data can be distributed so that everyone gets the same view. The enterprise solution can work for client data, as well, but it is usually more difficult to support because of the different requirements of different groups. A firm’s institutional group, for instance, may need one format for client
data, while the retail group may need a completely different format. In such cases, a master data approach may make more sense.
Accenture believes that, no matter which approach is appropriate, any change in data management should be managed centrally. Rather than establishing a number of different groups, each with its own governing principles, one central group – with appropriate representation from all key business units and functions – should take responsibility for how data should be received, organized and delivered. Data governance is not solely an issue of technology; it is a business process that needs equal input and involvement from the business to ensure that all needs are being met.
Amplify Insight with AnalyticsHigh quality data is the foundation for creating analytics, which are used by capital markets firms to realize measurable business outcomes at lower costs to serve. Analytics, for instance, can improve risk assessment and management, as well as demonstrate compliance with regulatory requirements, such as stress testing. With the foundation of high quality data in place, analytics can help firms evaluate the risk that a security is going to default, that the firm has enough liquidity to cover its obligations, and that its exposure to counterparties is reasonable.
Analytics can improve win rates by helping to identify the key factors leading to customer satisfaction (or dissatisfaction). For example, analytics can help wealth management firms retain clients through key life events. When a long-term client dies, his or her heirs tend to take their money elsewhere. Analytics are enabling sophisticated wealth management firms to anticipate this problem and present the heirs with services that are more suitable to them – before they jump ship.
Analytics can also help firms measure the true cost to serve certain types of clients, and develop service offerings that may be more suitable for clients in a particular category. For example, analytics may indicate that for an institutional asset management firm, a small number of clients might account for a large percentage of a firm’s costs, and should be charged additional fees for the higher level of service.
Firms must continually balance the relationship between “high tech” and “high touch” services, and the changing needs and desires of their customer bases. Younger customers, for instance, may prefer to access account information and initiate transactions online, keeping personal contact to a minimum, while older customers may prefer more frequent telephone and in-person contact with their relationship manager. Analytics can help determine the level of service that is appropriate for each customer within an acceptable cost structure.
Integrate Your Approach for High PerformanceAchieving high performance in analytics and data management is a complex, interdependent undertaking. While many firms attack data problems one by one, a comprehensive approach begins with a defined plan that includes a series of steps, building momentum as business units begin to see the results from initial efforts. The typical sequence of events might be as follows:
Assessing the current state of data management
• Identifying sources of reference data and of client or entity data
• Establishing processes for cleansing, validating and enriching data
• Managing exceptions
Establishing an effective governance framework
• Defining appropriate service levels
• Identifying owners of data and assigning responsibility
• Defining the strategy and plan of attack for desired improvements
Bringing benefits of improved data management to internal and external clients
• Using improved data to enhance analytics
• Using analytics to improve key operating functions such as reporting and compliance
• Identifying client needs and providing appropriate service or product offerings
Within each phase are multiple components, each requiring the right
mix of people, process and technology. Improving data quality, for instance, calls for establishing data quality rules and policies; data cleansing standards; and aligning standards with compliance rules. Skilled data administration and data quality service teams must be assembled to do the work, and these teams must work closely with those responsible for data governance. The teams must deploy technology including data profiling, quality, enrichment and monitoring tools, extract, transform and load (ETL) and audit reports.
Many executives mistakenly think, “If we could only just centralize our data – and make sure that updates to that data only occur in one place – we can develop a robust process to update that data, and then distribute the data to all parts of the enterprise.” The concept sounds good, but in practice it is not always feasible, nor desirable, to take a completely centralized approach. Take, for example, the process of client on-boarding. It may not be possible to go to one place to make all updates related to a new client. In addition to basic information (client’s name, address, branch, identifiers such as the Social Security Number or Tax ID, credit limits, types of investments, etc.) other necessary inputs might include:
• “Know your customer” and statutory anti-money laundering disclosure agreements need to be reviewed and signed.
• Other legal agreements need to be reviewed with the client, and signatures obtained.
• The client’s tax status must be determined
All such inputs then need approval, notification and confirmation, with communications back to both the relationship manager and the client when everything is in place. Forcing everyone from disparate locations and functions to go to the same place and use the exact same applications, screens and processes is not always feasible, especially in an organization that deals with a large number of clients. A more balanced approach – with a robust process to ensure data quality, but with flexibility in terms of applications – is needed.
Balance Cost and QualityAs evidenced by the increases in data management budgets since 2008, data quality and other data-related issues have become a major concern for firms. It
is difficult, however, for firms to evaluate where they are in terms of the industry – whether they are ahead of the curve or behind the curve in effectively managing their data.
One of the most common problems for firms is the tendency to measure data management strictly in terms of cost. Indeed, this is a key consideration for firms in deciding whether to outsource some or all data management processes.
However, making a decision to outsource on the basis of cost alone can lead to significant problems in the future. If resources elsewhere merely do the same work at lower cost – without improving data quality – the risk of expensive errors stemming from poor quality data remains great. Better data reduces overall costs in the long run not only by reducing errors, but by enabling management to make better decisions.
However, improving data quality is not a simple undertaking. While the processes involved are not particularly complex, the actual work is tedious and repetitive, making it a candidate for outsourcing.
Many firms that have tried to improve their data quality have acknowledged that these difficulties can cause them to spend too much time on the data business and not enough time on their core business of making money for their clients. An outsourced solution may free up management resources, but a properly structured approach will also generate improvements in data quality leading to long-term structural benefits for the firm.
Reap the Rewards of Good Data ManagementData management involves much more than just technology. A holistic approach to data management coordinates people and business processes as well as technological innovation. Firms need to consider key elements such as data architecture, metadata – data about data—taxonomy, security and storage.
When these elements are properly organized into an effective data management initiative, firms can realize significant benefits including lower operating costs, better risk management, and fewer and less costly errors. And, when improved data is the basis for advanced analytics, firms are in stronger position to establish and maintain competitive advantage in an extremely challenging market environment.
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About AccentureAccenture is a global management consulting, technology services and outsourcing company, with approximately 211,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world’s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$21.6 billion for the fiscal year ended Aug. 31, 2010. Its home page is www.accenture.com.
ContactsFor more information about how Accenture can help you achieve high performance through effective data management, please contact:
Paul J. Obrocki Global Data Management Lead, and Data Management Lead, North America Accenture Capital Markets [email protected]
Steve Scemama Data Management Lead, Europe/ Middle East/Africa/Latin America Accenture Capital Markets [email protected]
Wei Min Chin Data Management Lead, Asia/Pacific Accenture Capital Markets [email protected]
Michael J. Borawski Senior Director, Analytics Accenture Capital Markets [email protected]
