How Data Governance Helps Financial Institutions. Report Title in the blue bar

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1 24 October 2014 How Data Governance Helps Financial Institutions Achieve Report Operational Title size Excellence and position and text Satisfy box to Regulators center Report Title in the blue bar Stratecast Analysis by Jeff Cotrupe Stratecast Perspectives & Insight for Executives (SPIE) Volume 14, Number 39

2 Frost & Sullivan reports are limited edition publications containing valuable market information provided to a select group of customers in response to orders. Our customers acknowledge when ordering that Frost & Sullivan reports are for our customers internal use and not for general publication or disclosure to third parties. No part of this report may be given, lent, resold, or disclosed to noncustomers without written permission. Furthermore, no part may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the permission of the publisher. For information regarding permission, write: Frost & Sullivan 7550 West Interstate 10, Suite 400 San Antonio, TX United States SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 2

3 How Data Governance Helps Financial Institutions Achieve Operational Excellence and Satisfy Regulators Introduction 1 Banks and financial services firms have a Big Data problem. Financial services companies were early adopters of business intelligence (BI), using BI as far back as the 1970s for risk assessment and customer acquisition. Today, however, financial services companies are facing serious challenges on two fronts: Master data management (MDM) MDM provides a blueprint for how organizations discover, access, integrate, and manage data; how they ensure data integrity; and how they distribute data to where it is needed. All of these support data governance, which, in Stratecast s Big Data model, is part of MDM. The systems in use at most banks and financial services firms provide inadequate data governance; and these firms suffer as a result. Regulatory compliance A long and growing list of regulators and regulations across world regions are increasing regulatory reporting requirements to the point that financial services companies are having a hard time keeping up. Harnessing the power of Big Data, and putting it to work for the organization, is crucial in every vertical; and for financial services firms it is especially critical. These companies need to access growing volumes of detailed data across the enterprise, and ensure that the data is accurate. This requires effective data discovery and collection; integration of data from multiple internal/external sources; data aggregation and matching; data integrity, QA, and continuity; and data distribution. Financial institutions also need to submit complete, correct reports to regulators in a timely manner. Transparency and the ability for front-to-back auditing are mandatory. So is agility: the ability to respond to ad hoc requests and changing regulations. Yet, while the need for complete, accurate data and the requirements for regulatory compliance have changed, the tools that most financial services firms are using have not. Compliance requirements have surpassed current capabilities. Existing reporting processes and systems are inefficient and error-prone. Front office data and reports are typically manually managed by what the financial industry terms end user-developed applications (EUDAs), which are often nothing more than spreadsheets. Back office systems are complex, inflexible, and costly to maintain. As a result of these issues, firms are challenged when it comes to both data governance and regulatory compliance. This can have costly impacts: companies and teams that fail to address the issues in their legacy reporting systems and processes miss the mark when it comes to following sound business practices. They also struggle to meet new regulatory requirements, and will pay a 1 In preparing this report, Stratecast conducted interviews with these organizations and executives: Lavastorm Analytics Mark Knudsen, Director of Solutions Development Executives at three financial institutions who spoke on condition of anonymity Please note that the insights and opinions expressed in this assessment are those of Stratecast and have been developed through the Stratecast research and analysis process. These expressed insights and opinions do not necessarily reflect the views of the company executives interviewed. SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 3

4 price first, through fines levied by industry regulators, and, perhaps just as importantly, through a loss of credibility in the marketplace. This Stratecast report will explore some troubling impacts of the failure of financial services organizations to achieve effective data governance; and will provide insights on technologies and business approaches that can help them change their fortunes by harnessing the power of Big Data. Bumpy Economic Conditions and Incomplete or Stale Data are Posing Challenges to Financial Institutions The banking and financial services industry is at a critical juncture. The uneven economy is constraining growth, such that, for example, while U.S. corporations have more than $5 trillion in worldwide liquid assets, they remain cautious about opening their wallets for capital expenditures and operating expenses. This is creating a $500 billion shortfall between expected and real private, non-residential investment. 2 These and other factors are exerting pressure on the banking and financial services industry to generate new revenues and to enact cost-cutting measures. As they look to create revenue growth, however, financial institutions also need to keep a watchful eye on key performance indicators. Yet, their current abilities to access and manage Big Data often do not support this, as illustrated in these two scenarios: Capital May Be within Acceptable Parameters, but Liquidity is Tougher to Achieve A bank may have sufficient capital but still find itself in trouble if it fails to manage liquidity. Managing liquidity risk requires operational metrics that, in most cases today, are simply not accessible in usable form to banking decision makers. These desired metrics include: Intra-day cash positions such as automated sweeping of cash surpluses, manual adjustments, and payment throttling. Intra-day unencumbered collateral positions such as margin calls, liquidity positions across the institution, and predictive positions. Cash flow validation and proactive payment investigation to support operations. False Positives Make it Difficult to Assure Credit Quality Another area where data is needed but often not available is credit quality. Assessing credit quality requires metrics that enable the bank to quickly measure, aggregate and monitor credit risk to ensure that its customers are willing and able to honor their financial commitments. This is easier said than done. Changes to credit quality and credit exposures, which can be impacted by a variety of factors including ups and downs in the business cycle, need to be evaluated regularly, ideally in real time, so decisions can be made with the most current information. Dated information that gives a false positive of credit quality can lead the bank to extend loans it should not and, likewise, false negatives can lead the bank to deny loans it should have made. 2 Sources: Stratecast, U.S. Internal Revenue Service, U.S. Census Bureau, U.S. Bureau of Labor Statistics, Progressive Policy Institute, Reuters, The Atlantic SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 4

5 Regulations Designed to Ensure Best Practices Create Even More Challenges In the wake of the global financial crisis of 2008, policy makers in the world s financial centers issued a wave of new regulations designed to protect consumers and investors by enforcing best practices in terms of capital, liquidity, and credit quality, and by ensuring transparency and accuracy in internal processes and reporting. Exhibit 1 displays these regulations. Exhibit 1: Regulatory Complexity Creates Big Data Problems for Financial Services Firms Source: Lavastorm Analytics Different regulations apply in different jurisdictions, and some overlap each other, which increases the complexity of the effort required for companies to keep up. For example, Dodd-Frank applies to firms doing business in the U.S.; but banks must also report to the Office of the Comptroller of the Currency (OCC), an independent bureau of the U.S. Department of the Treasury. Organizational Silos and Data Integrity Issues are Adding to the Problem Financial services firms employ an army of skilled professionals to try to ensure regulatory compliance; and these people and the functions they support fall into four categories: 1. Corporate compliance Compliance, Regulatory Compliance, and Regulatory Affairs 2. Executive compliance Financial Crimes Compliance 3. Risk management Risk Governance, Operational Risk, and Risk Analytics 4. Reporting and Internal Audit SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 5

6 The situation is ironic for at least two reasons. First, part of the data that a financial services organization needs in order to ensure regulatory compliance is locked up in these organizational silos. Each team may be accessing at least some of the same external data sources to get at the information it needs, and expending resources to perform essentially the same tasks when it comes to assimilating, managing, and querying the data. One team may have multiple non-integrated systems pulling together data from 60 sources, while another team is pulling from 30 data sources, some of which are the same sources the first team is accessing. Second, while companies are employing skilled people to ensure compliance, in most cases, the data management systems with which they support these people are simply not up to the task: Fragmented data The source data that companies are using is often fragmented and inconsistent; and, when sources are aggregated, there are often data quality issues. As a result, organizations report a situation where No one believes the numbers. Adding insult to injury, data validation and cleansing exercises are being duplicated, on what is essentially the same data, by each team. Inflexible systems and manual intervention Trading and other legacy systems are often inflexible and require significant manual intervention to perform transaction reconciliations. These manual processes are time consuming, expensive, and prone to error. Complex systems and delayed results Legacy systems are also complex, which requires scarce specialized resources when changes are mandated by regulators or management. The result is an unacceptably long lag time between need identification and fulfillment. Expensive data warehouse initiatives Creating a unified data source such as a data warehouse can be expensive, and often fails. 3 Data Consequences: Penalties, Sanctions, and Fines, Oh My! Because of these challenges, firms are struggling to comply with regulatory reporting requirements. Current reporting systems that rely on Excel, Access, and department-specific applications make it hard for firms to complete full front-to-back reconciliations, to manage change, and to move from random sampling of a limited number of reconciliations to full reconciliation testing. The upshot of all of this is that failure to ensure regulatory compliance may result in sanctions, penalties, or fines: While financial services companies employ an army of skilled professionals to achieve regulatory compliance, in most cases, the data management systems with which they support these people are simply not up to the task. In July 2014, the Federal Reserve Bank of New York (FRBNY) assailed a large Western European bank, with major presence in the U.S., over its mishandling of regulatory compliance matters, citing hundreds of manual adjustments in its financial reporting processes, involving $337 billion in transactions. The Fed said the bank had, among other 3 This situation is mitigated by the broad availability of open source software such as Hive, a project of the Apache Software Foundation (ASF), which, when used along with the ASF s Hadoop open source data ecosystem, facilitates querying and managing large datasets residing in distributed storage, such as an enterprise data warehouse (EDW). Hadoop, Hive, and other ASF open source projects offer a viable technology direction for replacing legacy, specialized applications. However, while Hive can address some technology and cost concerns, assimilating all relevant data sources into the EDW, and integrating the EDW with existing IT infrastructure, can be time-consuming and costly. SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 6

7 things, a lack of transparency; inadequate monitoring and regulatory reporting infrastructure; and poor data governance and data integrity; and that the firm s entire U.S. regulatory reporting structure required wide-ranging remedial action. This large global bank responded with damage control measures totaling 1 billion (approximately $1.28 billion) including hiring 500 more people in the bank s compliance, risk, and technology areas in the U.S. In September 2014, regulators fined Barclays Plc twice in a single day for client account failures in the U.K. and the U.S., for, among other acts, failing to maintain an adequate internal compliance system. The financial services firm agreed to pay $77 million in penalties, including $15 million to the U.S. Securities and Exchange Commission (SEC). The U.K. s Financial Compliance Authority (FCA) fined the Royal Bank of Scotland 5.6 million (approximately $9 million) for failing to properly report more than one-third of its transactions from November 2007 through February How Data Management Supports Best Practices and Regulatory Compliance Banks and financial services companies need a solution more robust than spreadsheets, and less complex than multi-year, multimillion-dollar deployments; one that optimizes the business while helping compliance teams address regulatory concerns. The market is ready for such solutions: respondents to Stratecast s 2014 Big Data and Analytics Survey ranked financial analytics third among solutions they are currently using, and fourth among solutions they are evaluating for the future. 4 In its Annual Report and Accounts 2013, global financial services provider HSBC noted the importance of an enterprise data management strategy to meet the volume, granularity, frequency, and scale of regulatory reporting requirements, as well as other internal and external information demands. The firm s data management strategy is to establish consistent data aggregation, reporting, and management for the entire firm. HSBC s actions represent recognition by financial services firms of a set of data management needs and reporting requirements, shown in Exhibit 2. Exhibit 2: What Financial Services Firms Need from Data Management Requirement Acquisition Accuracy, Validation, and Reconciliation Analysis and Automation Reporting Description and Impact Systems must provide completeness of data, supported by automated data aggregation to render all relevant data available for analysis and reporting, without creation of data marts or data warehouses. Systems must deliver accurate reporting to enable proper understanding and business decisions. Reconciliation and validation are critical, and data quality must be ensured at each checkpoint : at each source and data transformation point. Systems must have the ability to implement business rules in performing data analysis to ensure that analytic insights match up well with tangible business needs. This must occur in an automated fashion to speed processes and avoid errors from manual intervention. Reports must be formatted in a manner that is clear and understandable; and, where necessary, that aligns to predefined forms, such as those provided by regulators. 4 Stratecast, 2014 Big Data and Analytics Survey, and initial presentation of findings in Big Data and Analytics Market Survey: Initial Observations (BDA 2-09, July 2014) SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 7

8 Requirement Timeliness Transparency Flexibility Interactivity Data Governance Description and Impact Data used for reporting must be current. As such, the data that goes into the reports must be as close to real-time as possible, and reports must be distributed within required timeframes, some close to real time. 5 Data and data analysis in reports must be clear and auditable, both internally and by regulators; particularly since some regulations require full end-to-end visibility into not merely the results but also into the source data and analysis. 6 Systems must enable firms to meet current reporting requirements while being able to adapt to new or revised requirements, and must support ad hoc requests, especially during times of stress or crisis. Processes and solutions must be amenable to change management. Processes and systems must enable required interactions and information flow across divisions, departments, and senior management, and to ensure a consistent baseline of information companywide. Systems must provide continuous control over data versions and flows. Source: Stratecast To help financial services firms achieve compliance, the data management system must leverage data from three of the six major data source categories defined by Stratecast, as shown in Exhibit 3. Exhibit 3: Data Governance Must Use Transactional, Organizational, and External Sources Source: Stratecast 5 For an exploration of real-time analytics what it is, how to achieve it, and the impact of real-time analytics on business results Stratecast suggests: Real-time Analytics: NOW Would Be Good (BDA 2-10, September 2014). 6 As such, this requirement is not merely about the ability of regulators or any interested and authorized party to understand the results of the analysis, but for the financial institution to clearly show all work. SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 8

9 Exhibit 4 lists specific data sources under these three broad categories. With the exception of sources such as population census data, weather and climate data, and some transactional data pertaining to retail and loyalty programs, financial services firms need to access data from all of these sources to optimize operations and ensure compliance. Exhibit 4: Specific Transaction-based, Organizational, and External Data Sources Transactionbased Organizational External 1. Transaction data: retail 2. Transaction data: e-tail 3. Geolocation, point of sale (POS), and inventory data 4. Shopping/loyalty programs 5. Customer, partner, member, and user-generated data 6. Machine to machine (M2M), transactional and automated systems 7. Log and sensor data including tracking and RFID 8. ERP, BI, and other productivity tools 9. Supply chain 10. Operations and process designs and records 11. Key performance indicator (KPI) metrics 12. C-level records 13. HR 14. Sales & marketing 15. Product development & management 16. CEM/call center/helpdesk/crm 17. Accounting & finance 18. Data from third-party sources (e.g., demographics), fraud prevention feeds, and other aggregated/anonymized data 19. Population census data 20. Economic data; financial data including quotes/trades 21. Research and competitive intelligence 22. Weather and climate readings Source: Stratecast Lavastorm is Helping Financial Firms Gain Data Clarity and Business Value Lavastorm Analytics offers a next-generation orchestration layer, built on its Lavastorm Analytics Engine (LAE), which is helping financial services firms build flexible, mission-critical regulatory reporting processes and infrastructure. The LAE enables financial services firms to quickly create auditable regulatory reports by letting business users aggregate data, check data quality, derive analytic insights, and generate reports, without requiring a major IT initiative to make it happen. The system supports production-level automation, and enables compliance teams to do their jobs efficiently, while helping them rapidly address regulator concerns. Lavastorm Analytics executives and customers provided some apt examples of how the system provides tangible benefits, in what may at first seem like data details but that add up to big headaches in competitive or internally-built systems: The LAE s join and go capability parses through what may seemingly be disparate data records, to find common trade numbers, and join the records for efficient data storage and SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 9

10 access. The system performs statistical analysis to say, in effect, This is the reconciled trade transaction that represents the trade across multiple systems. The LAE accommodates something that can terrorize other analytics engines: mapping and reconciling different structured data types, despite the presence of differing values. LAE reconciles different representations of a trade across systems, to create a singular, unified view of that transaction. While structured data normally requires a rigid data model in a relational database, LAE s capabilities allow for sophisticated scoring and matching algorithms that improve reconciliations across heterogeneous transaction representations. Once a company has a set of rules built, it can use LAE as its eyes and ears to correct data errors or inconsistencies such as mismatched fields, blank data, and other anomalies. This was usually an incredibly time-consuming manual process for most companies in the past; but LAE can automate continuous detection and resolution going forward. LAE normalizes data such that the same data can easily be used for one purpose by one team and another purpose by another team. This is the essence, at a granular level, of moving away from the purpose-built and department-specific systems. Optimizing data operations, as shown in these examples, supports regulatory compliance. Lavastorm s LAE fits into the Business Process and Strategic Analytics component of the Big Data and analytics (BDA) market, which is illustrated in Exhibit 5. Exhibit 5: Stratecast s Technology Overview of the Big Data and Analytics Market Source: Stratecast The key challenge for buyers and sellers of solutions in this category is to efficiently integrate with or migrate from those critical legacy systems that cannot simply be ripped and replaced. Case Study Snapshots: Leveraging Big Data Yields Compliance and More Two brief case study snapshots provided by Lavastorm illustrate the value of leveraging Big Data to help financial services firms achieve data consistency and ensure regulatory compliance: SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 10

11 Large Global Bank Reduces Manual Data Adjustments by 1.5 Million per Month A large global bank based in Western Europe provides a textbook example of how to manage the challenging data scenario presented by a large organization that has acquired many other banks and financial services companies. Problem Given the patchwork of data systems across its acquired properties, getting data consistency and quality right upfront is crucial. The bank needed to implement new enterprise data standards to ensure consistency and accuracy across all financial data; establish quality checks for legacy systems; and institute flexible compliance evaluation to support evolving data standards and regulations. Solution The bank deployed an LAE-based application to evaluate and monitor data standards compliance for each source system. LAE indicates compliance level, provides a detailed view of where noncompliance occurs, and enables prioritized remediation based on issue impact. LAE delivers a federated data set for subsequent analysis and reporting. Results The LAE-based solution has reduced manual adjustments by 1.5 million per month across all enterprise financial data, and improved data compliance from 93% to 99%. Manual adjustments involving transactions were key to the Fed s dressing-down of a global bank, as discussed earlier in this report, which the bank is addressing through more than $1 billion in damage control measures. Another Global Bank Boosts Intercompany Transaction Match Rate to 98% and Slashes Reporting Cycle from 12 to 4 Weeks For another global bank, a solution developed in its center of excellence is allowing the bank to increase intercompany transaction report accuracy and support its regulatory compliance team. Problem The bank needed to improve its intercompany reconciliation process. This was challenging because it has to handle large data sets (approximately 1.3 million transactions per month for just one department) with differing representations of each transaction, and to apply complex matching logic. Using its existing data infrastructure, solution development was expected to take months. Solution The new system, built on LAE, provides a framework for data governance around internal transactions processes. LAE combines end-to-end data across front office, back office, and other systems, applies sophisticated matching algorithms, and enables visibility and transparency across the entire data flow and reconciliation process. Results By identifying root cause issues, and reducing manual adjustments, the bank substantially increased report accuracy, increasing its data match rate from 66% to 98%. That led directly to a dramatic reduction in its reporting cycle from 12 weeks to 4 weeks; and that, in turn, is estimated to save five FTEs (full-time equivalents, or employees) just for this one reporting need. It also addresses visibility and transparency, which are hot button issues for regulators. SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 11

12 Stratecast The Last Word In the wake of the global financial crisis in 2008, regulators in the world s financial centers unleashed a new wave of regulations designed to ensure that banks and financial services companies follow best practices thus reducing the chances of the world s financial systems ever again teetering on the brink of collapse. Although well-intentioned and probably necessary on the part of the regulators, this is placing a growing burden on the banking and financial services industry. Financial services firms are challenged when it comes to data governance, and that impacts both their businesses and their ability to achieve compliance with regulators. The case of the large global bank that ran afoul of the Fed in New York is instructive because it illustrates the almost knee-jerk reaction of some financial heavyweights to continue to throw bodies at the problem. Rebuked by the Fed s New York Bureau for a variety of compliance failings driven by poor data management practices, one of that bank s first responses was to hire 500 more people in the U.S. for its compliance and other teams. Stratecast is all in favor of full employment, but also asserts that before anyone hires an armada of new people in pursuit of compliance, basic principles of IT and network management should be brought to bear on the problem: eliminating organizational and data silos; accessing all relevant data and integrating it to provide end-to-end visibility; making systems and teams more efficient by eliminating duplicate tasks and workflows. The good news is that financial institutions have plenty of reasons to invest in a Big Data solution beyond demonstrating regulatory compliance. Effective data governance supports profitability through cost reduction, improved customer servicing, faster time to the market, and much more. One provider helping financial services firms leverage Big Data to achieve operational best practices and regulatory compliance is Lavastorm Analytics. The provider s Lavastorm Analytics Engine (LAE) is currently available as an on-premises installed solution, but not yet via the cloud. Given that cloud-based solutions make Big Data more accessible to a wider market, Stratecast recommends that the company develop a cloud-delivered LAE offering; and, indeed, Lavastorm has confirmed to Stratecast that a cloud offering is on its product roadmap. Another area of expansion Stratecast recommends for Lavastorm Analytics is executive compliance. Financial institutions tell Stratecast they are focused on using Big Data to monitor possible financial crimes by their executives, because financial crimes can bring a regulatory firestorm raining down on a financial institution just as surely as non-compliance can. In the meantime, the substantial positive results Lavastorm Analytics is generating for large global financial institutions should provide continued market momentum for the company, as well as working capital for these product enhancements. Jeff Cotrupe Industry Director Big Data & Analytics Stratecast Frost & Sullivan SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 12

13 About Stratecast Stratecast collaborates with our clients to reach smart business decisions in the rapidly evolving and hypercompetitive Information and Communications Technology markets. Leveraging a mix of action-oriented subscription research and customized consulting engagements, Stratecast delivers knowledge and perspective that is only attainable through years of real-world experience in an industry where customers are collaborators; today s partners are tomorrow s competitors; and agility and innovation are essential elements for success. Contact your Stratecast Account Executive to engage our experience to assist you in attaining your growth objectives. About Frost & Sullivan Frost & Sullivan, the Growth Partnership Company, works in collaboration with clients to leverage visionary innovation that addresses the global challenges and related growth opportunities that will make or break today s market participants. For more than 50 years, we have been developing growth strategies for the Global 1000, emerging businesses, the public sector and the investment community. Is your organization prepared for the next profound wave of industry convergence, disruptive technologies, increasing competitive intensity, Mega Trends, breakthrough best practices, changing customer dynamics and emerging economies? For more information about Frost & Sullivan s Growth Partnership Services, visit CONTACT US SPIE #39, October 2014 Stratecast Frost & Sullivan, 2014 Page 13 For more information, visit dial , or

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