Enterprise Data Management Service

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Enterprise Data Management Service >>>>>>>> Enterprise Data Management (EDM) Today s Demands on Data Management To process reference and pricing data in a timely, efficient and transparent manner while meeting consumer needs has always been the cornerstone of financial data management and the goal of the professional data manager. ACQUISITION MANUFACTURING DISTRIBUTION Data acquisition, manufacturing and distribution are the building blocks of the enterprise data supply chain. The force and pace of change applied to these processes demand our utmost attention at a time when expectations are high, budgets tightly managed and visibility into the workings and consequences of data management has never been so direct nor so keenly sought. CONTENTS 02 THE EVOLUTION OF EDM 03 BLOOMBERG POLARLAKE: THE NEXT GENERATION OF EDM 04 THE BLOOMBERG POLARLAKE EDM MODEL 06 MANAGED SERVICE COMPONENTS 08 THE BLOOMBERG POLARLAKE TECHNOLOGY PLATFORM 13 A COMPLETE EDM SOLUTION Three key drivers are defining the needs of EDM today: Regulatory Compliance Adapt to the introduction of new compliance regulations across financial markets Meet the continual demand for change as regulation implementations become clear Meet the high quality standards demanded by regulation in both quality of data and timeliness of delivery Risk Management Provide for increased visibility and transparency of the EDM process and data quality Decrease operational and reputational risk Meet shorter delivery schedules and increased reporting frequency Managing the Cost of Business Innovation and Operations Work ever more flexibly with both existing data sources and an increasing number of new sources Access sources swiftly to take business advantage Manufacture high-quality data and supply it to a growing number of internal and external data clients Improve audit through improved traceability and data lineage Improve quality and timeliness of reporting demanded by business, compliance and risk management All of the above are managed in an environment in which budgets are under extreme scrutiny and IT departments stretched.

EDM: Then and Now PROFESSIONAL SERVICES Support for Client-Specific Rules & Output Format Bloomberg PolarLake operates as an independent business unit to ensure that it can provide a fully vendor-agnostic solution. Bloomberg PolarLake has separate infrastructure facilities and operations staff, which enable it to provide the highest levels of security, privacy and permissioning of the data it receives from both commercial data vendors and clients own proprietary data and business rules. EDM VENDORS VENDOR Credible Large Scale Provider Multi-Vendor KEY Bloomberg PolarLake: THE NEXT GENERATION OF EDM Bloomberg PolarLake believes that a next generation managed service can uniquely meet the requirements of today s financial industry. The key is the ability to address the needs of both effective common processing and the preservation of a client s business DNA and intellectual property. Figure 1 CLIENTS PEAK IN-HOUSE 1ST GENERATION EDM FRAGMENTATION 2ND GENERATION EDM Proprietary Solutions Deployed Software Solutions High Complexity Managed Service The Evolution of EDM The need to provide solutions for enterprise data management is not new. Over a number of decades, many attempts have been made to utilize a combination of the available technology and human skills. As illustrated in Figure 1, the development of proprietary systems in which clients built everything in-house and utllized only minimal data and technology from outside vendors was the first step. Often these were very large projects attempting to map the entire organization. Many such projects failed because of their size, multiple requirements and the available technology, all of which led to prohibitive budgets and time lines. These projects also suffered from the inability to adapt quickly to change. Over time, software solutions became available in the marketplace. Each solution had its own heritage for example, pricing data for risk systems or reference data for security processing. These systems were successful in their own right but demanded high levels of maintenance and professional service resources to integrate with downstream systems and ever-changing vendor data. An industry of professional services businesses developed to support these environments. Some financial institutions took their first steps toward outsourcing sections of the data-management-solution effort by hiring professional services companies for IT integration and using third-party providers to host IT and network environments. However, the major problems of adaptability, speed of change and cost management remained. With the difficult conditions surrounding the financial markets came the understanding that a great deal of enterprise data management was not unique to a single organization. Added to this realization were ever-shrinking and harder-pressed IT divisions. People The result was that many firms began to look for a provider who could deliver not only industrial-strength technology solutions but also had the resources to manage the common elements of enterprise data-management. The ultimate would be the ability for a firm to add its intellectual property by connecting to the service through in-house rule definition/management and continuously resetting priorities in short, managing business change. Introducing Bloomberg PolarLake May 2012 saw Bloomberg s announcement of its acquisition of PolarLake, a global leader in applying semantic web and big data technologies to the data challenges of the financial industry. Recognized by industry analysts as a leader in reference data distribution and integration, PolarLake received the 2012 Inside Reference Data Award for Best Reference Data Initiative and gained admission to the Gartner Visionaries Quadrant. This acquisition enabled the newly formed Bloomberg PolarLake to build and offer a robust, customizable, open and inclusive managed data service to acquire, manage and distribute complex and wide-ranging datasets with speed, agility and control. This is a service that reduces the need for investment in a large technical infrastructure, provides mission-critical support in a very tight time frame and provides an ideal vehicle for significant future expansion of requirements. Data Technology Process The Founding Principles A service is more than a collection of technology components. The Bloomberg PolarLake EDM Service is built on the principles required to meet the force of market change: Open to all vendor and client data Transparent process control and access to lineage of the data supply chain Complete security and confidentiality for proprietary client data and rules High degree of automated processing This is achieved by Bloomberg PolarLake through the unique and complete provision of the following capabilities: Data Vendor Feeds On-boarding and processing of data vendor sources. Client Data On-boarding and processing of internal data sources (internal feeds, classification schema, cross-referencing mappings, etc.). Client-specific Processing Applying and executing client-defined rules in the operations process. Clients maintain control over their rules, including vendor hierarchy, tolerance, quality rules and output formats. Clients have access to workflow tools, an interactive rules manager and full data lineage back to the source. A Next-Gen Managed Service > On-boarding > Ongoing Feed Maintenance > Feed Processing > Vendor Interactions > Vendor Hierarchies > Price Tolerances > Exception Processes Flows > Distribution Services Figure 2 VENDOR FEEDS CLIENT RULES CLIENT S OPERATIONAL INTELLIGENCE High-speed reliable, change-tolerant data management supply chain processing based on the Bloomberg PolarLake platform Client-specific and common business rules supported Flexible client-directed exception management and data delivery definition Operational Intelligence Through access to proprietary data sources as well as high-quality technology and operators, Bloomberg PolarLake has the unique ability to govern, monitor, alert and predict problems in the data supply chain. The Bloomberg PolarLake processing model also enables the client to be the ultimate decision maker on exception resolution. > Internal Feeds > Internal Classifications > Internal Cross-referencing > Non-standard Feeds > Monitoring & Alerts > Industrial Strength Audit & Transparency > Intelligent Exception Assistance Enterprise Data Management Service 02 // 03

The Bloomberg PolarLake EDM MODEL Bloomberg PolarLake understands that in the data management industry, information management, data processing, storage and analysis needs can increase and change rapidly in short time frames. Many diverse data sources and data structures need to be managed, stored and modeled. Bloomberg PolarLake Edm Data Supply Chain COMMON FEED MANAGEMENT > Feed On-boarding > Protocols / Formats > Vendor Metadata > Feed Maintenance > Schedule Late Files > Feed Common Validation > File Structure, Header, Trailer > Record Count, File Date, Syntax Validation COMMON PROCESSING > Feed Common Validation > Single Vendor Business Validation > Day-On-Day Comparisons > Reasonability Tests > Exception Management > Audit and Archiving > Entitlements > Operational Intelligence: Suggestions, Smart Choices, Predictions, etc. This solution provides the data required by financial institutions researchers and financial analysts preformatted, mapped, enriched and giving a readily accessible holistic view of any market, instrument or financial entity. Sophisticated semantic data management underlies the managed service, thus providing full integration of multiple datasets from many sources, fully mapped and classified using both industry-standard classifications and financial institutions own classifications, where available. Both raw and processed data are archived and available for retrieval in various formats, e.g., direct to downstream applications and analytical tools delivered through various mechanisms based on a client s needs. A value-added service that the Data Operations team provides is the ongoing management of data input changes, regardless of source. Data change notifications sent by commercial vendors and other organizations providing data input, are analyzed by the Data Operations team, which reviews the impact and either directly implements the requested change or discusses with clients the likely impact of such change. The client and team then agree to a testing schedule before any changes are implemented. The financial institution continues to have the commercial relationship with its existing data vendors. CLIENT CONFIGURATION CLIENT MONITORING / EXCEPTION MANAGEMENT CLIENT SPECIFIC PROCESSING Bloomberg PolarLake Managed Service Case Study > Rules > Tolerances > Securities of Interest > Client Cross-reference > Calendar > Classification > Universe Management > Feed Status > Exceptions > Order Tracking > Rule Exception > Results Delivery and Management Vendor Hierarchy Automated Tolerances On-Request Basis Cross Vendor Business Validation > Exception Resolution Workflow Feed Cross-referencing > Ad Hoc Requests Waterfall Rules > Operations Management Ensure Rules Are Run Ensure Reports Are Issued Management of Review Procedure Application of 4 Eyes Audit SUPPLIER ORDERS SUPPLIERS BULK SOI, PULSE AD HOC PER SECURITY Figure 3 We believe that many consumers of financial data will be migrating to an EDM managed service model rather than implementing deployed software because such service provides a cost-effective solution with a seamless upgrade path for the challenges of managing a high volume and variety of datasets for constantly changing business requirements. Business users are able to focus solely on their specific business activities, confident that the underlying data required for decision making is accurate, comprehensive and provided to them in flexible ways. We also believe that a managed service model will provide clients with a quicker time-to-market than a traditional deployed EDM solution, with Bloomberg PolarLake taking over the burden of managing the growing data challenge and ensuring that relevant and timely data is provided to meet current and future needs. Our Data Operations team undertakes the client-determined level of data management by implementing sophisticated automated rule-management processing, coupled with exception-management processing thus ensuring full end-to-end data management and the production of cleansed and enriched datasets. This solution delivers common processing, which multiple clients require (e.g., single-vendor business validation), as well as client-specific processing managed by either Bloomberg PolarLake or the client through graphical user interfaces. The Bloomberg PolarLake EDM managed service is revolutionary in its use of semantic web and big data technologies to manage data complexity and volume. The platform underlying the managed service provides a rich infrastructure for information processing. It enables data to be acquired, manipulated and distributed in various paradigms and utilizes an open, flexible streaming technology. This innovative technical approach is coupled with a first-class data management organization and high-quality customer service that provides mission-critical support to the financial community. The Bloomberg PolarLake EDM Managed Service provides comprehensive, end-to-end life cycle management, as outlined in Figure 3. DAILY FTP SECURITIES OF INTEREST Figure 4 SECURITY MASTER SECURITY REPORT MONTHLY REPORTS (FTP) DMZ FTP VALIDATION RULES: HIERARCHY SUPPLIER ORDER GEN MATCHING ARBITRATION ORDER FULFILLMENT VENDOR HIERARCHY STRUCTURES EVENTS, ORDERS, DELIVERIES, DASHBOARD Enterprise Data Management Service 04 // 05

Managed Service Components Vendor Feeds All firms currently in the open market must source vendor data. However, the tasks of implementing, integrating and managing these feeds, while necessary, need not be repeated by all firms in the financial markets. The combination of a next-generation EDM technology platform with in-depth data feed knowledge enables Bloomberg PolarLake to offer access to vendor data while managing feed schedules, carrying out feed maintenance and delivering information on feed access. New feeds can be on-boarded rapidly and changes incorporated effectively without service interruption. Client Data Client-specific processing is one of the essential extra elements of the Bloomberg PolarLake EDM managed service. The power of our platform helps clients preserve their business DNA and take full advantage of their intellectual property. All of this is in addition to the common processing elements provided through the service. The on-boarding and processing of internal data sources (internal feeds, classification schema, cross-referencing mappings, etc.) is achieved using the same platform tools and Bloomberg PolarLake expertise as the on-boarding of vendor data feeds. As a result, on-boarding is swift and data content is managed and accessed with the same industrial strength as vendor data. Managed Service Process Flow VENDOR FEEDS COMMON PROCESSING service operations data operations Client Rules The application and execution of client-defined rules in the operations process enables clients to maintain control of vendor hierarchy, tolerance parameters, data quality and output formats. Clients have access to workflow tools and an interactive rules manager to control the processing of data in accordance to their rules and policies. Data lineage and audit trails for both raw and processed data is stored and readily available. Client-defined data output schedules, which make use of the standard platform data distribution components, enable the swift implementation and delivery of client-defined data to multiple business users. REAL Operational Intelligence Change is the single constant in the modern data supply chain. Whether it is change attributable to business drivers or from data vendors, these adaptations must be handled without interrupting the supply process. The Bloomberg PolarLake EDM platform follows a data store then model approach which puts the power into the hands of the data management professional. Through access to proprietary data sources in addition to high-quality technology and data operators, Bloomberg PolarLake has the unique ability to govern, monitor, alert CLIENT-SPECIFIC PROCESSING RULES CLIENT S SECURITIES OF INTEREST REQUEST/ REPLY EXCEPTIONS EXCEPTION RESOLUTION BULK DELIVERY CUSTOMER SITE APPS/ USERS and predict problems in the data supply chain. The Bloomberg PolarLake processing model enables clients to apply operational intelligence and be the ultimate decision maker on exception resolution. The result is an environment that delivers: Data flow monitoring and alerts to potential breaks in the data vendor supply chain Intelligent remediation of errors through the use of automatic rules Access to cross-market views and data intelligence for smart resolution of errors and the creation of new or adapted rules where required Fully transparent data lineage as data is added, changed or new knowledge is applied. This critical information is available for use by data operations staff and for application in automatic rules Industrial Strength Operations and Customer Service EDM is a supply chain accepting orders from clients (trading, risk, regulation), sourcing these orders from internal and external data feeds and delivering the orders as requested. It is critical that this chain be underpinned by an exceptionally strong operating environment. The Environment: High-speed, high-availability, next-generation EDM technology built by Bloomberg PolarLake and tested successfully in the most demanding of financial market environments Transparent visualization of the data management supply chain Highly qualified data operations team from the data vendor and banking communities who have access to every link in the chain Full audit trail and archiving (each change whether to data or rules, whether manual or through the application of rules is recorded) State-of-the-art technology environment designed and built by financial data management experts Controlling Enterprise Data A major goal of enterprise data management is control of a number of factors, including: Costs Data Quality Change Management The Decision Process Record Management Through intelligent operational efficiency, the next-generation Bloomberg PolarLake EDM Service delivers effective control without diminishing the ability to operate in a world of frequent and rapid change: Streamlined vendor data acquisition, management, cleansing and enrichment processes enable vendor data to be on-boarded swiftly, high levels of quality maintained and change professionally managed Sophisticated rule engine and workflow capabilities that underpin the provision of the EDM Service are extended to clients. This enables clients to maintain control of the decision process and apply their DNA and IPR. Full data lineage is provided for both vendor and client data that support control mechanisms for the decision process, change management and data quality Full audit trail provides the ability to record and control sourcing, change and delivery of data Cost control provided through the effective implementation of common processes, managed data delivery across client businesses and in-depth knowledge of data usage. Clients continue to have direct commercial relationships with data vendors Infrastructure and service control is provided by a highly skilled data operations team Figure 5 Enterprise Data Management Service 06 // 07

THE Bloomberg PolarLake EDM Technology Platform Philosophy Bloomberg PolarLake, through collaboration with and at the request of leading financial services firms, created a purpose-built solution to overcome the traditional complexity of RDBMS-based data model approaches to data management. The platform was specifically developed to address the complexities associated with pricing, reference, trade and entity data management and distribution in markets where rapid change is the norm and industrial strength a must. Managing Complexity SUPPLIERS CONSUMERS Data On-Boarding Process One of the strengths of the Bloomberg PolarLake solution is its ability to quickly import many different types of data. Our data management platform supports the acquisition, manufacturing and distribution of all data content types (e.g., reference data, trade data, settlement details, position data and account information). Our solution consists of an end-to-end data supply chain that matches data consumers with data providers (both internal and external). To achieve these matches, the solution implements fast data acquisition, management and distribution of data. This data supply chain configuration, as illustrated in Figure 7, supports incremental batch, end-of-day batch, real-time messaging or a combination of deliveries. At the core of the technology is a patented XML streaming technology that transforms source data into binary XML streams. These binary XML streams are either routed to the data store, sent directly to a data management process or sent directly to a distribution process. This streaming technology is the basis for our high performance and flexibility in data delivery. For satisfying incremental or end-of-day batch requirements, XML streams are cached and then recombined to create a batch file delivery in the required format. Data Supply Chain Concept VENDOR FEEDS BUSINESS ANALYST WORKBENCH MANAGEMENT WORKSTATION BACK OFFICE CLEARING & SETTLEMENT ACQUISITION / VENDOR FEEDS MANUFACTURING / COMMON PROCESSING CLIENT-SPECIFIC PROCESSING DISTRIBUTION / CLIENT-AUTOMATED DELIVERY INTERNAL SOURCES DYNAMIC LOADING POLICY ENGINE SOFT STORE DYNAMIC DISTRIBUTION MIDDLE OFFICE RISK MANAGEMENT Quality and Exception Workflows Order Management BUSINESS OPERATIONS DASHBOARD Rapid Onboarding Change-Tolerant Storage APPS: TRADE, SWIFT, FpML, CSV FRONT OFFICE TRADING Figure 6 Supplier Order Management Data Ops: Semantic Tagging / Search i Operational Intelligence Policy Engine Validation, Exceptions, Data Lineage, etc. Multi Channel, Multi-Protocol, Pub/Sub, Request/ Reply, Delta Detection Dynamic Storage Figure 7 Enterprise Data Management Service 08 // 09

LOADING A soft-parser tool loads data feeds of all formats from any data vendor or other source. For each feed, a set of configuration options defining basic metadata and file attributes, such as the feed s primary identifier, is defined and stored as a supplier delivery. When the vendor feed is available, the soft-parser tool applies the configuration options and loads the data on to the platform. We have experience of on-boarding a significant number of data sources and vendor data feeds for clients and quickly importing feed configurations, structured data and unstructured data. Figure 8 highlights a specific implementation where the Bloomberg PolarLake platform was able to achieve significant improvements over a leading EDM competitor. Key Metrics Compared Soft Data Store to archive complete and different content from all data sources, thus eliminating data loss resulting from rigid and limiting data models used in relational data stores. As a result, all types of data reference, index constituents, curves, positions from vendor or proprietary sources are completely stored and available. True relationships within and across feeds are established, all without any data loss, which is typical of traditional RDBMS approaches. Soft Data and Semantic Stores As outlined in Figure 9, the Bloomberg PolarLake approach separates the raw data from the semantical description of that data. This eliminates the need for massive amounts of modeling and storage of relationships as foreign keys in relational tables. This is one of the main causes of slow and delayed implementations both at the outset of a project and during the life of the program (as data sources and vendor feeds are added or changed). 11x FASTER LOAD TIME 17x MORE SECURITIES QUERIED PER SECOND 20x FASTER ON-BOARDING VENDOR & INTERNAL 30 25 20 29 300 250 200 255 40 35 30 25 40 VENDOR META 15 150 20 10 5 0 15 100 10 50 5 15 2.5 3 0 0 CUSTOM LINKS Figure 8 BLOOMBERG POLAR LAKE LEADING COMPETITOR Data Management Flexibility A key capability of the Bloomberg PolarLake Managed Service is tracking the provenance of data through its complete life cycle from source to derived datasets. Our data management solution has great flexibility in managing data without ever losing the provenance of the original dataset. We offer storage of any dataset without changes to the database schema. To ensure the ready availability of the data in the XML database, metadata that describes the dataset is required. In the data repository, the data is described using semantic models, instead of relationships being managed through foreign key relationships in a RDBMS runtime. The traditional approach to data management results in a rigid canonical model, a semantic mismatch between data sources where data needs to be discarded to fit the data model and nonstop changes in the data model that is trying to manage changes to multiple feeds. Conflicting data classifications are often left unsupported. In the Bloomberg PolarLake solution, data can be described and used in several models without any changes by altering an existing semantic model or introducing a new semantic model. These models are completely soft. They can be changed at any time, built up incrementally and many models of the same data are possible. They are also easily publishable to a relational model if required. This approach enables us to be data agnostic and facilitates the capture and modeling of very complex data structures and relationships. Data within the Soft Data Store is accessed directly using SQL. Reports are generated directly or with third-party reporting tools for distribution. Additionally, the data can be published using a number of different protocols and formats, such as, Pub/Sub, Request/Reply, SQL, JMS Messaging, XML, Flat File, Batch or Real-time. For data that needs to be cleansed, merged or normalized, we create a golden copy record (with manual intervention when required) in our own hosted Data Store or use a financial institution s repository/schema, enabling maximum reuse of existing assets and simplifying integration. We also support multiple consumer views and conflicting classification through use of semantic tagging for example, researchers and financial analysts can use different golden copy datasets. The Soft Data Store archives the raw data. The Semantic Store is used to describe the data (classifications, relationships, linkages) through semantic metadata. In this way, related and unrelated data can be stored in the same data store. The free-form XML technology enables the Figure 9 CHANGE-TOLERANT SOFT STORE The benefits of this approach: Change Tolerance The solution is fully tolerant of changes in incoming data sources. This could include fields added or renamed (a common occurrence). The Data Store requires reconstruction while the data model does not; data does not need to be reloaded. Changes to self-describing feeds are adopted with zero downtime or human intervention. Business Usage Future Proofing Anticipating what data feeds or data sources are required is not possible, but ongoing industry research confirms the need for any modern solution to be highly agile and flexible to manage CUSTOM MODELS SEMANTIC STORE data changes and ensure rapid delivery to meet business and regulatory requirements. Our Data Store is capable of storing various content:.pdf, FpML ISDA agreements, emails and other types of structured and semi-structured documents. This overcomes the frustration and business impact of the wait or downtime while preparing a data model upgrade to support a new feed or data type. Enterprise Data Management Service 10 // 11

Data Provenance The Bloomberg PolarLake Soft Data Store stores raw data with a time stamp at acquisition. The Semantic Store and the usage of semantic metadata enable bi-temporal management of data, with each piece of data being created with its own time stamp; any changes to that data are retained and a full history of raw and subsequent changes is available to view online and in a report. This information provides a full audit log of time-stamped changes from original raw data through the completed derived dataset. We store and retain this data throughout the entire life cycle, including retirement. The client is able to track and view information describing where data originated, where it flows, what rules were applied and how it was transformed at every stage. This information also can be retrieved via an audit log. The client is able to see the data as it appeared in the data management system at a specific point in time ( as at ) and at every stage in its transformation ( as of ). As the policy engine manufactures derived data items and structures, a bill of materials is created. This gives the provenance of the data used to produce derived data. Data Scalability The Bloomberg PolarLake EDM Managed Service is inherently scalable, with the underlying technology stack offering major improvements over traditional architectures. A combination of standard and proprietary technologies is used to implement a scalable, industrial-strength information processing platform. The primary mechanism for storage of data is based on XML binary storage and semantic triples. The platform is highly configurable and has been designed and deployed to support the demanding growth that organizations are experiencing in their data requirements. Unlike other approaches, this platform focuses on complex and changing data structures rather than on simple and rigid constructs. Clients are able to on-board new datasets easily, store raw and derived data and provide ad hoc request services to consumers. Platform implementation examples include a scalable data management environment that grew from 5 to 38 large data sources over months rather than a previously projected three-year time frame. In such environments, new unseen datasets were on-boarded and modifications made to existing feeds. The datasets included the entire universe of security master, pricing and ratings from data vendors. This data was made available for raw browsing, execution of rules, fulfillment of consumer requests and publication to downstream systems. The platform supports a mix of ad hoc and standing order requests. Ad hoc requests were fulfilled using a mix of ad hoc direct SQL, XML messaging, web services, file- and policy-controlled request formats. Features: Patented XML streaming technology facilitates very high message throughput on any given server (thousands of messages/per second/per server). Horizontal scaling is supported through the seamless addition of servers. Each server concurrently executes a variable number of multi-threaded processes, thereby facilitating vertical scalability. The number of concurrently executing processes, relative process priorities and number of concurrently executing threads are all configurable on a per-server basis. The underlying Data Store supports the storage of an arbitrary number of datasets through application data partitioning. Each dataset persists across a distinct set of physical tables (leveraging Oracle XML DB Compressed Binary Storage, structured and unstructured XML indexes and Oracle partitioning). Individual datasets containing hundreds of millions of XML records of varying sizes are commonplace. Record sizes typically vary from a small number of bytes to several KB. Arbitrary numbers of datasets are supported, with each being assigned to distinct sets of partitioned physical tables, thus leveraging SSD arrays to optimize database performance and reduce I/O contention. Both hash-based and sorted-map lookups are supported. Data is stored as XML Documents, with each document typically representing a delta to a logical entity. Physically, the XML is tokenized and compressed for efficient storage and transmission. Further Data Management Capabilities Our data management solution also supports historical data. Data records from each data source for a specific unique entity (security or issuer) are stored as record updates. New record updates for that unique entity are placed in the Soft Data Store as current record updates; the previous record updates remain in the Soft Data Store as historical data. The complete history of record updates, including every data field delivered for each update, is available for constructing historical time series outputs. We support full audit trails for data acquisition, manufacturing processes (validation, normalization, consolidation, etc.), distribution (incoming requests and outbound distribution) and data modification. Audit trails are viewable using our web application. In addition, Bloomberg PolarLake interacts with other APIs and web services to expose the audit trail data to other user-display applications. Our solution platform has an embedded ETL capability, the key to which is to provide transparency in the rules governing mapping, routing, transformation, validation or enrichment of data in the ETL process. This is done through an Excel-style Business Analyst Workbench that has embedded rule-overriding and rule-testing capabilities. A COMPLETE EDM SOLUTION Bloomberg PolarLake addresses the complex challenges facing financial services firms in a timely, efficient and transparent manner, delivering three key business benefits across your entire data supply chain: Comprehensive regulatory compliance Enhanced risk management Reduced cost of operations Bloomberg PolarLake offers a complete solution, combining the following capabilities: Vendor Feeds: Immediate access to a comprehensive range of third-party reference data feeds. Client-specific Processing: Customized processing rules to match your precise business requirements. Operational Intelligence: By combining next-generation technology with dedicated data operators, Bloomberg PolarLake EDM offers the unique ability to monitor, identify and predict potential issues. Industrial-strength Operations: A managed service that gives you the flexibility to meet regulatory demands and changes in future requirements. The Bloomberg PolarLake EDM service delivers effective control over your data 12 // 13 Enterprise Data Management Service

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