BIG DATA ANALYTICS: MANAGING BUSINESS DATA COSTS AND DATA QUALITY IN THE CAPITAL MARKETS

Transcription

1 White Paper BIG DATA ANALYTICS: MANAGING BUSINESS DATA COSTS AND DATA QUALITY IN THE CAPITAL MARKETS Abstract This white paper discusses Business Data (Market, Reference, and Pricing Data) in Financial Services: challenges for managing vendor cost and data quality, and potential for solutions with Business Data Analytics. June 2012

2 Copyright 2012 EMC Corporation. All Rights Reserved. EMC believes the information in this publication is accurate as of its publication date. The information is subject to change without notice. The information in this publication is provided as is. EMC Corporation makes no representations or warranties of any kind with respect to the information in this publication, and specifically disclaims implied warranties of merchantability or fitness for a particular purpose. Use, copying, and distribution of any EMC software described in this publication requires an applicable software license. For the most up-to-date listing of EMC product names, see EMC Corporation Trademarks on EMC.com. Part Number H

3 Table of Contents Executive summary... 1 State of the Industry: The High Cost of Business Data... 5 Current State of Vendor Management... 5 Purchasing Model... 5 Current Tools, Data and Metrics... 6 Cost Take-out Challenges... 6 New Analytics, New Data, New Skills... 6 Business Data Analytics Examples... 7 Vendor Mix Optimization... 7 Business Data Usage Analysis... 7 Comparative Data Analysis... 7 Cost of Poor Quality Data... 8 Inputs, Tools, and Analytics... 8 What is Next... 9 Transforming the Way Data is Sold Effect on Data Quality Analysis Conclusion How EMC Can Help

4 Executive summary As Investment Banks, Brokerages, and Asset Managers struggle to stay competitive in the new economy, they are looking to manage costs as never before. For many firms, the cost cutting discussion returns, again and again, to Business Data (Market, Reference, and Pricing Data), often the single largest balance sheet expense after compensation. The casual observer might look at this expense and think that cutting Business Data costs would be a simple exercise. It is not, due to complexity of the businesses that use the data, traditional buying patterns, and the seemingly impossible challenge of making choices based on the true business value of the data: balancing cost, data quality, and data s measured contribution to profitability. In this white paper, we discuss the emerging opportunity to control Business Data costs through Big Data Analytics. We provide examples of Big Data Analytics that provide insight to measure business value, identify cost savings, and provide nontraditional insight into data quality. We also describe toolsets and services that EMC provides to support the effort. We conclude with a vision of new business models for vendors and consumers of Market, Reference, and Pricing Data in a world where continuous analytics are a fact of life. Audience This white paper is intended for business people and technologists in Financial Services: CIO s CTO s, Chief Data Officers, Market Data Vendor Management, and Data Quality Managers. The intended audience also includes vendors of Market, Reference, and Pricing Data. 4

5 State of the Industry: The High Cost of Business Data Large Financial Services Institutions (FSIs) trade in hundreds of different products across the globe - both on behalf of the firm and on behalf of their clients. The decisionmaking process around each trade requires tremendous amounts of information, ranging from simple and public (e.g., prices for exchangetraded securities) to highly specialized data (e.g., mortgage analytics). A top tier financial institution will likely have hundreds of vendors for Business Data (again, market, reference, and pricing), and a vendor may provide many different services. The largest of these, Thomson Reuters and Bloomberg, will consume a majority of the market data budget, and are considered in a category of their own. Other vendors range from large and specialized to boutiques. These smaller vendors offer data and services that are local to a particular region, or proprietary analytics (e.g., mortgage prepayment speeds as input to mortgage-backed security valuations). Current State of Vendor Management The Vendor Management function has traditionally tracked Business Data costs, and negotiated the contracts with vendors, but these purchases are generally spread across business units with overlapping but differing objectives. These business units, i.e., trading desks and supporting functions, have traditionally taken a "more is better" attitude to Business Data and services consumption, meaning a proliferation of often redundant and duplicative purchases across the enterprise. Purchasing Model Large vendor contacts are multi- year, and sold in bulky modules - there is currently no pervasive concept of fine-grained or on-demand usage. Smaller vendor contracts are numerous, and their Business value is often understood only by the business. Two issues exacerbate this problem. First, a single vendor may sell the same data product to multiple consumers in the same enterprise in a manner that carries unnecessary cost. Second, there has traditionally been very little to help purchasing entities understand the commonality and duplication across data sets in order to understand their relative value. This lack of insight has continued the behavior of 5

6 buying new data sources in case they may provide additional business value as opposed to a certainty of this business value. Current Tools, Data and Metrics Today, Vendor Management groups utilize a number of data sources in managing Business Data spend. Much of the data is embedded in tools such as MDSL, which organizes and tracks vendor contracts, and Data Access Control System (DACS), which manages entitlements for fee-liable sources (such as exchanges) and provides user access controls. The controls help address the visibility to multiple purchases from the same vendor, but have limited value in terms of understanding data redundancy across vendors. Today, additional data sources and tools can be leveraged using big data principles to help close this gap: "Click data" textual data that can be observed from the network, and captured into log files. This data can tell what vendor terminal functions (Bloomberg, Thomson Reuters) users are accessing. "Tick" level market data can be observed on the wire by specialty capture products, such as Dart. Dart provides aggregated reporting on user consumption, for example, by exchange. Other products use tick level data for latency reporting as input to automated trading engines. Current management dashboards for usage and cost reporting include Crystal or Microsoft Access/Excel. Data assembly is manual, time consuming, and typically only includes data from Vendor Management systems. New big data paradigms allow for a simpler, more cost effective view of these data. Cost Take-Out Challenges In the past, the sovereignty of the trading desk over Business Data purchase decisions was seldom challenged. But now, as CFOs go after the data spend, they are challenging Vendor Management to produce sustainable cost reductions of up to 10%. This is a complex undertaking, given current buying conventions and existing toolsets. New Analytics, New Data, New Skills New analytics are possible - but require information not previously used by Vendor Management. In addition, Vendor Management will need access to data science skills to mine and correlate traditional control and entitlement information with new sources, such as operational and HR data. 6

7 Business Data Analytics Examples There is a trend that a small number of large financial institutions are starting: a Business Data Analytics function in Vendor Management. The Business Data Analyst s charter is to make new sense of what the FSI buys and produces, utilizing data and analytics to enable them to holistically assess the Business value of the data. Here are some of the ideas we have heard, and work that is underway. Vendor Mix Optimization Business Data Usage Analysis There are efforts underway to create a theoretical model of a Right sized set of vendor products, and minimum costs to provide data to a large FSI. The model would identify the optimal mix of vendor data products, given assumptions on needs of various Trading desks and business units. The model is intended to serve as a baseline for a conversation with the business on how actual usage can be optimized. This analysis intends to discover statistical patterns of communications (networks of users), and content (such as Stock identifiers) from terminal log data. The outcome of this analysis is input to contract negotiations, and identification of lower cost alternatives to current tools. Comparative Data Analysis This analysis intends to discover redundancy or like data by comparing large amounts of market or reference data that is thought to be similar across vendors or internal sources. The intent is to be able to use the best, cheapest source, and to be able to eliminate others. 7

8 Cost of Poor Quality Data An idea that extends beyond the traditional Vendor Management role is a nontraditional approach to Data Quality. These analytics aim to identify patterns that indicate or predict poor quality. This is a step beyond traditional data quality methods: human intervention eyeballs and expertise, and monolithic data management systems that implement rigid, assembly-line style data scrubbing. The intent is to reduce dependence on an FSI s Data Stewards and Vendors to identify quality issues, and to provide faster resolution of issues that cause trade breaks, poor execution, and other negative business impacts. Data Included in this type of analysis includes Fast Data ; streaming/real time Market Data such as Prices: Bid, Ask, Last Trade, and Trade Size. The analysis aims to look at normal and abnormal behavior such as: Absence of Ticks Excessive Ticks Best Execution anomalies Out of Order Quotes and Trades Another area of study is Periodic Data, provided daily or at some other regular frequency. This data includes end of day prices, and Reference Data (Security descriptions). Analytics include: Presence of 0 values content What s changed? day over day/month over month Source- Sink Comparison of a Vendor s data to Security Master Database Inputs, Tools, and Analytics Most sophisticated analytical groups are using all their traditional vendor data for analysis. Other sources include HR Data, P&L data, Trade Data, and the Business Data content itself. 8

9 Analytical platforms include EMCs Greenplum s Unified Analytics Platform for structured data, and Hadoop for unstructured data. Analytics include use of R, an analytics library, and Madlib. Visualization tools include Qlickview and PanOpticon. What is Next EMC Greenplum s Unified Analytics Platform The most sophisticated Vendor Management groups believe that Business Data Analytics are industry game changers with respect to data cost controls and leaders are now leveraging these tools to drive down data costs by up to 30%. Objectives for the most ambitious Vendor Management groups point to a desire to finally transform the industry - to move toward "pay by the drink" or Amazon" model. This vision includes less bulky contracts and a way to "self -serve" data at the field level. This requires classification of data at a finer grain than is currently represented in vendor contracts and bundles. For example, rather than having granularity limited to Vendor 1, Fixed income, Asia, data could be vended by field, For example, fixed income, fixed rate bond, coupon. 9

10 Market Data Vendor Management groups can partner with firm efforts (spearheaded by the Chief Data Officer) to finally standardize classification of data across product lines and entities. The ISO model has been in existence for many years, and is one possibility for classifying security master, product, and pricing information. Vendor Management groups would utilize custom models and algorithms (such as machine learning), to classify and compare sources in a fine grained way, initially creating a baseline of the contents of the "Store", and then setting up automation that manages the stock (data content), and vends the data in a self-serve way. Transforming the Way Data is Sold The implication for vendors is also a transformation of the way they manage and vend their data, with a drive toward standards-based classification. Vendors will be challenged to compete on business value, to unbundle their products, to continuously address quality of service, and must be willing to map to standards (such as ISO). Effect on Data Quality Analysis Changing the traditional approach to vending data can have impact on data quality. Quality feedback could be channeled through the "Store", and potentially reduce Data Steward workloads. Conclusion Realizing cost reduction for Market Data, Reference Data, and Price Data remains a hard problem for Financial Services firms. The bad news is that current costs are unsustainable. The good news is that Big Data Analytics brings new ways to assess the fully loaded business value of the data, enabling Vendor Management to advise business users on what data they truly need in order to run their business. We assert that reducing Business Data spend by 10% is an impossible mission in the absence of Business Data Analytics. Expected results include: Strong evidence of unused or redundant data Correlation of Business Data to profitability (or lack thereof) New Indications of Data Quality We anticipate that this is at least a 5 year journey, with many hurdles and resistance along the way. The destination is not yet clear, but Business Data Analytics, done well, will illuminate the path. 10

11 How EMC Can Help Many of our Financial Services clients have worked with EMC to bring Analytics Labs and Services into the firm. Our Data Scientists and Analysts, joined by EMC Financial Services Industry experts, have been solving for business challenges such as fraud detection, customer churn, social marketing and others. EMC Consulting is now leveraging these same skills, tools, and principles to help companies analyze the contents of their data feeds for quality, accuracy, and duplication in order to make more strategic data consumption investments as well as to drive the business processes in procurement required to adopt more modern data purchasing behaviors and contracts. 11