SimCorp Solution Guide

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1 SimCorp Solution Guide Data Warehouse Manager For all your reporting and analytics tasks, you need a central data repository regardless of source. SimCorp s Data Warehouse Manager gives you a comprehensive, industry-standard investment data warehouse model along with all of the tools you need to facilitate timely, automated, and accurate reporting. The Data Warehouse Manager gives you access to high quality data at your fingertips, and helps you answer important questions without assistance of your IT department. Mitigate risk Reduce cost Enable growth simcorp.com

2 2 Why a Data Warehouse? Data warehouses are typically implemented to consolidate data from disparate systems into a single database, giving a single version of the truth and exposure to crucial business information via common reporting or business-intelligence tools. New regulatory reporting requirements are forcing many financial institutions to implement data warehouses. With SimCorp Dimension at the core of your enterprise architecture, your organization has made a decision towards an integrated platform, rather than having different operating systems storing information separately. Your company already has all investments in a single book of records and perhaps even uses SimCorp Dimension as the master for security master and market data master, with or without a data-scrubbing engine in front. However, SimCorp Dimension is rarely the only operating system in investment management firms, so some data integration is still required. The need to present business information to end users in a user-friendly format is becoming increasingly important as the need for self-service business intelligence is becoming the industry standard. Key benefits for your business A key benefit for business users is easy access to data, enabling them to deliver reports and perform in-depth analysis for decision-support. SimCorp s Data Warehouse Manager provides an industry standard solution and tools to extend and operate the data warehouse within your business. The Data Warehouse Manager integrates data from disparate systems, and provides easy access for end-users to information for business analytics, reporting and decision-support. With SimCorp Dimension as the core operating system, you probably already store around 80 percent of your operating data in SimCorp Dimension s database, structured however, in a data model that serves transactional processing rather than reporting and analytics. The common perception in the market is that a data warehouse project on average takes two years i and involves full time employees. Yet such projects are still prone to high failure rates. ii Market intelligence also shows that the annual maintenance cost of a production data warehouse is between 40 and 60 percent of the initial cost iii simply to keep up with market trends and the evolution of source systems. What SimCorp Clients Gain Implementation costs reduced by up to 80% Implementation time reduced by 75% Reduced risk of failure The proven market standard for SimCorp Dimension clients Reduced operational cost and risk (automated upgrades & enhancements) A solution owned by business with minimal IT footprint i ii M. Demarest. (1997) The politics of data warehousing. Adelman, S (2014): Measuring Data Warehouse Return on Investment - A Whitepaper, PhxDAMADay2012_Measuringhttp://cms2.dama-phoenix.org/wp-content/uploads/2013/07/PhxDAMADay2012_Measuring-Data-WarehouseROI.pdfData- WarehouseROI.pdf iii Watson, H. J., and Haley, B. J. (2004): Data Warehousing: A Framework and Survey of Current Practices, Journal of Data Warehousing,2(1), pp and Inmon 2001.

3 3 Investment Management Warehouse Together with clients, SimCorp has developed an industry standard data model for investment management, we call it the Investment Management Warehouse (IMW). It has been designed to follow modelling concepts from the Kimball methodology that for many years have been considered the best practice for implementing data warehouses. SimCorp monitors the new analytic trends and regulatory requirements to ensure that the Data Warehouse Manager evolves with every new SimCorp Dimension release. The Investment Management Warehouse covers the most relevant areas of SimCorp Dimension from a reporting and analytics perspective and consists of the following major components: The SimCorp Business Information Model Ensures that the business terminology is consistent between SimCorp Dimension and other SimCorp components, including the IMW. The SimCorp IMW Multidimensional Data Model The core and all subject areas included in the IMW overtime are available as multidimensional data models, ready for business intelligence tools and OLAP cubes of the customers choice. Since Release 5.6, the IMW model has more than 90 tables covering more than 700 fields from SimCorp Dimension. Predefined installable database schema Helps clients achieve a fast implementation of the Data Warehouse. Predefined ETL jobs Loads data from SimCorp Dimension to the Data Warehouse, and since Release 5.6, includes more than 130 data extracts. Our 10 guiding design principles In order to follow Data Warehouse Manager s best practices while leveraging the most from the tight integration with SimCorp Dimension, our solution is carefully designed according to the 10 guiding principles below: 1. Business-friendly, multidimensional solution engineered for intuitive usability and based on Ralph Kimball s design guidelines 2. Deliver data to the business in a reporting-friendly format where data can be used as it is, without further transformation needed when general industry best practices apply 3. Presented to business users in business terminology based on the same vocabulary as in SimCorp Dimension 4. Incorporate the accumulated investment management knowledge of SimCorp 5. Support data warehouse requirements of investment management firms 6. Analytical and reporting components to support business intelligence tools and OLAP technologies 7. Capture and promote data and metadata, enabling detailed lineage back to SimCorp Dimension including the Internal Keys 8. Ready-to-use data that is out-of-the-box and based on extracts from SimCorp Dimension that can be easily reconciled against SimCorp Dimension 9. Customer-specific extensions in SimCorp Dimension (such as free codes etc.) and integration of data residing outside of SimCorp Dimension 10. Automatic upgrades with SimCorp Dimension to ensure a stable operation

4 4 Data Analytics The analytics layer is where end users log on using a reporting or analytic tool of choice to produce reports or gain insight for decision support in their daily work. These users may or may not be users of SimCorp Dimension, but could also be operational analysts or senior management. According to business needs, a number of different options are available. Reporting tools SimCorp Dimension is integrated with SAP Crystal Reports for operational reporting, and can be used on data warehouse content. SimCorp Coric is the preferred client reporting system, available for client communications and web reporting including self-service options. Business Analytics Any third party business intelligence or reporting tool, which can access Oracle, will work with the SimCorp Dimension solution. Data Mart Layer Multidimensional Data Marts The Data Warehouse Toolbox may be used to build data marts based on sub-sets of the central base tables in the Data Warehouse Manager. SimCorp provides foundation packages of ETL and tables for sample data marts. SimCorp strongly advises against granting users direct access to the base data warehouse, but instead publish data to consumers through data marts. According to business needs, a number of different options are available. Custom data marts should be created along these lines: Subsetting the base data warehouse tables to provide targeted, local, simpler data models for specific business purposes and often only include current versions of versioned facts Governing access control and authorizations in easy and fail-safe ways Optimize the query performance according to business needs using relational tables with relevant indexing and/or cube technologies (OLAP) Reporting Data Marts Reporting data marts are data structures tailored for a specific reporting purpose and are available for a range of solutions in SimCorp Dimension. Sourcing the data warehouse client, fund and web reporting can be set up to either source data from the data warehouse, or directly from the operational database. Solvency II, Trade repository and NAIC Reporting have dedicated functionality, with the reporting pools in SimCorp Dimension structuring and preparing the data before writing them into the data marts.

5 5 The standard data warehouse solution includes some fact tables designed for reporting purposes. The variations include: Fact tables with high-level aggregations of non-additive measures in the risk and performance area Fact tables that feed calculation results (e.g. Fund Figures) in a form, which may be transposed into custom data marts for reporting purposes Data Mart Storage Since SimCorp does not supply specific data mart solutions, there are no restrictions on the choice of platform for the data mart layer. This may be on Oracle, SQL Server or any other technology. However, the SimCorp Data Warehouse Toolbox currently only supports loading to Oracle tables, so the loading to cubes on a MS SQL Server for analysis services must be specified in the MS SQL Server tools. SimCorp has expertise to support in this area. Solution overview The Data Warehouse Manager consists of two key components. Data Warehouse Toolbox Investment Management Warehouse An ETL tool for the business Consolidate data from SimCorp Dimension with other data sources Use the SimCorp data dictionary to select the right data and include meta-data and lineage Apply data quality screens and schedule data warehouse load plans Pre-built data model for investment managers Industry standards and full instrument coverage Maintained by SimCorp works across upgrades Data Warehouse Tool Box A set of tools to create, run and manage ETL (Extract, Transform, Load) processes Consolidates data with other sources Uses the data dictionary to select the correct data and include metadata and lineage Applies data quality rules and specifies data warehouse load plans Investment Management Warehouse (SimCorp IMW) A market standard data warehouse solution and data model Multi-dimensional model following the Kimball methodology designed with end users in mind Full financial instrument coverage that follows market trends Maintained by SimCorp and works across upgrades The solution is tightly integrated with SimCorp Dimension and as a business user familiar with SimCorp s reporting and connectivity tools, you can use the Data Warehouse Manager to. Consolidate data from external sources Build marts and reports based on specialized data models Specify data loads Turn disparate data into business value Control and monitor data quality

6 6 Using the Data Warehouse Toolbox Data model extensions and ETL jobs are designed, scheduled, and monitored from within SimCorp Dimension. This reduces the need to hire external specialists unfamiliar with the new tools. Additionally, there is little need to involve a database administrator for reporting queries, empowering business users to manage and find the data they need. These users are free to schedule data loads as they see fit, ensuring that the right data is delivered at the right time. The workflow also facilitates custom processes, such as data quality-breach thresholds or ad-hoc manual checks. The Data Warehouse Tool Box is based on proven technologies familiar to SimCorp Dimension users such as extensions to the Data Extractor, Formula environment, calculation engines and batch jobs. The Data Warehouse Tool Box also offers functionality for supporting specification of business rules for data quality, custom measures and transformations. As well as best practice handling of referential integrity using default dimension members and intelligent null handling.

7 7 Data Dictionary The Data Warehouse Manager incorporates a simple Data Dictionary, documenting the source tables and fields (in SimCorp Dimension) covered by the data warehouse. It also includes cross-references between source tables and fields and destination tables and fields and the extraction setups and extraction definitions used in the ETL processes. The content of the data dictionary is maintained by SimCorp. Data from External Sources The SimCorp Data Warehouse Toolbox enables users to load data from other systems using underlying Oracle technology with a user interface inside SimCorp Dimension. You can use the following data sources in the Data Warehouse Toolbox: Databases (via Oracle external tables) CSV and other structured files via the External File Definitions window XML, Web services, message queues, middleware via the Communication Server Preservation of History The Investment Management Warehouse is designed for detailed preservation of history. There are several levels of support for preservation of history: Surrogate key handling by way of mapping to the business keys is always enforced and in use Time series-style fact modelling of changes over time for prices, ratings etc. Support for slowly changing dimensions (type 2) by way of specification of version-creation criteria, which is applied to the SimCorp Dimension extracts, and of surrogate key usage in the staging processes for selected dimensions Versioned storage structures for selected facts, such as daily positions and transaction lifecycle. Most fact tables are loaded using simple append strategies, but for some facts a concept of versioning has been applied to view certain measures as they were at a specific point in time. The versioning property has been implemented using versions on the fact table records and a corresponding version dimension. User-defined data models, which is certainly available in SimCorp Dimension (in the form of free codes, tree node names, formula and extra fields etc.) is handled by adding custom extension tables to the DWH data model using the Data Warehouse Toolbox. Please note that non-linear, non-additive measures and key ratios (such as TWR and VaR) can only be represented with difficulty in multi-dimensional analytical tools (such as OLAPtools). For reporting environments, this can be dealt with in flat, aggregated data structures, but not for user- driven, interactive business analytics where SimCorp Dimension s internal analytic facilities are recommended. Selecting SimCorp s data warehouse solution has been a shortcut for Jyske Invest to getting a modern and upto-date data warehouse and professionalizing our reporting capabilities. Finn Beck Senior Director, Head of Fund Administration, Jyske Invest Fund Management A/S Designed for usage of Update or append to allow for updates of the data warehouse fact tables, if so desired

8 8 Ready, Set, Grow Data Warehouse Manager belongs to SimCorp s portfolio of integrated front-to-back solutions for business process automation in investment management. Efficient workflows seamlessly integrate your organization and provide accurate and up-to-date information when you need it, empowering SimCorp s flexible and scalable solutions allow you to capitalize on opportunities as they arise and swiftly adapt to changes in business requirements. Leading investment management firms worldwide rely on SimCorp solutions to provide optimal business conditions and secure competitive advantage. Get ready to grow with SimCorp. Learn more about Data Warehouse Manager at

9 SimCorp Solutions Since 1971, SimCorp has been providing investment and portfolio management software and services to the world s leading investment managers, asset managers, fund managers, fund administrators, pension funds, insurance funds, and wealth managers. Based on its world-class software platforms, SimCorp Dimension and SimCorp Coric, SimCorp provides global financial organizations with the tools they need to mitigate risk, reduce cost, and enable growth. Listed on the NASDAQ OMX Copenhagen, SimCorp is a global company, regionally covering all of Europe, North America, and Asia Pacific. For more information, please visit Legal Notice The contents of this publication are for general information and illustrative purposes only and are used at the reader s own risk. SimCorp uses all reasonable endeavors to ensure the accuracy of the information. However, SimCorp does not guarantee or warrant the accuracy, completeness, factual correctness, or reliability of any information in this publication and does not accept liability for errors, omissions, inaccuracies, or typographical errors. The views and opinions expressed in this publication are not necessarily those of SimCorp SimCorp A/S. All rights reserved. Without limiting rights under copyright, no part of this document may be reproduced, stored in, or introduced into a retrieval system, or transmitted in any form, by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose without the express written permission of SimCorp A/S. SimCorp, the SimCorp logo, SimCorp Dimension, and SimCorp Services are either registered trademarks or trademarks of SimCorp A/S in Denmark and/or other countries. Refer to for a full list of SimCorp A/S trademarks. Other trademarks referred to in this document are the property of their respective owners. simcorp.com