Decision Framework, M. Knox Research Note 24 July 2003 Vertical Data Warehouse Solutions for Financial Services Packaged DW financial services solutions differ in degree of and approach to verticalization, components included, source and application neutrality, scope, scalability, modularity and range/depth of supported applications. Core Topics Business Intelligence and Data Warehousing: Business Intelligence and Data Warehousing Markets and Technologies; Financial Services: Financial Services Architectures and Emerging Technologies Key Issues Which traditional and emerging vendors and applications will meet FSPs' key operational and decision-support needs? What criteria should be used to evaluate business intelligence and data warehouse technologies? Several vendors are offering verticalized data warehouse (DW) solutions to the financial services industry. These packaged solutions are attractive because they promise more-rapid and sometimes less-expensive deployment than in-house development. They also promise lower cost than customized development services from a third party. Careful evaluation of the promised benefits is required, however, because substantial investment in implementation and customization may be needed (see "The Packaged Data Warehouse Myth"). These solutions may address all or only some of the primary components of a complete business intelligence solution (see Table 1). The DW itself provides the data infrastructure for supporting business intelligence applications, some of which may be included in the packaged offering. Central to the offerings, however, are data models that have been specifically developed or customized for the financial services industry or one of its sectors, such as retail banking or insurance. To meet the criteria of being a verticalized DW solution, in addition to the industryspecific data model, these solutions must also provide for the use and support of multiple, disparate: Data sources Analytic, reporting and access tools Applications, including those from different vendors or developed in-house This definition excludes application-specific data repositories that are not designed with the intent of supporting other applications and uses. Gartner Reproduction of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The reader assumes sole responsibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice.
Table 1 Components of a Packaged DW Business Intelligence Solution Component Processes Vendor Offerings Strategic Planning (Initial and Ongoing) Requirements Definition Initial Design Adjustment/Refinement Over Time Methodology Professional Services Design Tools Data Acquisition Data Management Data Analysis Data Access and Distribution Data Sourcing Internal Data Sourcing External Data Source Validation Data Movement Data Quality Assessment and Improvement Data Maintenance Data Security Documentation Governance Quality Preservation Segmentation Profitability Assessment Predictive Modeling Providing Access for Applications Providing Access for Direct Users Granting of Customized Views Allowing for Update of Data From Applications Proactively Providing Information to End Users and End Applications Providing Security and Audit Trails Extraction, Transformation and Loading tools Data Cleansing Tools Data Profiling Tools Metadata Management Tools Data Discovery Tools Third-Party Data Provision Database Management System Data Model Backup/Recovery/Archival Metadata Management Tools Statistical/Analytic Tools Householding Tools Data Extraction Templates Push Technologies Pull Technologies Query Tools Reporting Tools Online Analytical Processing Personalization Technologies Business Activity Monitoring/Enabling Technologies Hardware Usage Monitoring/Auditing Tools Authentication Tools Data Application Using Data for Numerous Functions, Including CRM (Sales, Service or Marketing), Risk Management, Corporate Performance Management Monitoring Usage Applications Repository Usage Monitoring Usage Analysis Technologies Source: Gartner Research (July 2003) Vendor Categorizations Vendors supplying verticalized DW solutions can fall into one of three categories, based on the primary or traditional focus of the company. Operational application system providers such as core banking and enterprise resource planning (ERP) system 24 July 2003 2
vendors that have extended their offerings to include data warehousing, leveraging their domain knowledge of operational data, which is a primary data source for business intelligence applications. Representative vendors that, in addition to their traditional operational solutions, offer verticalized DW solutions include FiServ, Metavante, PeopleSoft, Reveleus (a subsidiary of the core banking provider iflex) and SAP. Database management system (DBMS) providers essentially package expertise gained in custom services engagements for the design and development of DWs based on their DBMSs, or offer a warehousing solution that was acquired to round out product and service offerings. Representative vendors include IBM, Oracle, Sybase and Teradata. Analysis and business intelligence application providers have extended their solution stacks backward to include data foundation, making this foundation open to the support of analytics and applications beyond their own product sets. Representative vendors include Financial Architects, FRS, Harland, and SAS Institute. Evaluation Criteria In evaluating verticalized DW solutions, financial services providers (FSPs) must examine critical differences. These differences relate, in part, to the solution source. The key elements of the evaluation, beyond factors that are part of any vendor and solution evaluation such as viability, experience, proven performance and vision include: Degree of and approach to verticalization. FSPs must evaluate offerings to ensure they offer support that is deep and broad enough for the financial products and services they offer. Related issues are the sectors of the financial services industry addressed. In some instances, solutions are targeted at specific sectors, such as insurance or retail banking. If the need is to support multiple activities crossing multiple financial sectors (such as banking as well as insurance), there may be issues with extensibility, the ability to share information with other sector-specific solutions or comparability. In other instances, a broader, more-generic approach may be taken, which may require additional customization to meet sector-specific requirements. In some instances, compatible sector-specific extensions may be built on a sector-neutral (or in some cases industry-neutral) core, enabling data to be shared and aggregated across divergent lines of business. 24 July 2003 3
Some vendors, such as the core banking providers and some analysis and application providers, come from a financial-services-specific background, offering in-depth knowledge of the industry segment they cover. Knowledge of other financial services segments may be lacking, however, as may be in-depth knowledge of some of the business intelligence analytics and applications that the FSP intends to support. Knowledge of DW design and administration varies, and may be an issue for some vendors whose traditional focus has been on operational environments. Other providers, including the DBMS providers and business intelligence analysis and application providers, come from a nonvertical-specific tradition, and have enhanced their offerings with data sources, analytics and applications of particular interest to FSPs. The depth and extent to which these solutions have captured the required elements will vary, with verticalization representing an ongoing effort for many of these vendors. They do, however, bring experience in DW management or specific analytical and application techniques, and may have developed substantial domain expertise through their ongoing work. Business intelligence components included. FSPs must evaluate offerings to determine the extent to which they offer a holistic business intelligence environment, and the extent to which they will have to be paired with products from other vendors or in-house development to make a complete solution. In general, DBMS providers can be expected to most completely cover data management functionality. However, their experience with traditional financial services' data sources often mainframe, legacy-based may be weak. Core banking and application vendors will tend to show strength in data acquisition related to their own applications and data application, but may lack the experience and ability to access other data sources and the related metadata particularly if those sources are competitors' products. Source and application neutrality. FSPs must evaluate offerings to determine the extent to which they represent a true DW solution that is capable of supporting a broad range of data sources, subjects and applications, as opposed to being optimized for a specific data source or application. Source and application neutrality are most likely to be an issue for vendors that have developed their DW solutions as an extension of their particular applications. The ability to handle data sourced from other vendors' systems must be examined particularly closely for vendors, such as core banking and ERP system providers, that have built their DW 24 July 2003 4
solutions to capitalize on their knowledge and ownership of source data. Application neutrality supported by an application-neutral data model and broad inclusion of subject areas at a granular level must be examined particularly closely for vendors, such as analytical application providers, to determine if the required data elements are available with appropriate timing and frequency. Application neutrality is particularly important to ensure a consistent foundation that enables production of comparable metrics and analytics that can be aggregated at an enterprise level. Vendors whose packaged DW solution arise from service offerings may also have an issue with source and application neutrality, depending on the requirements of the engagements that formed the basis for their offerings. Scalability. FSPs must evaluate whether offerings will be able to support their current and future intended volume of users and concurrent queries, query complexity and data sources, as well as their desired frequency of refresh, which may include continuous updates. These issues are largely related to the underlying data acquisition and management technologies and database, as well as the reporting and analytic tools chosen. DWs that were originally designed for a limited set of applications and data sources, a specific set of technology products or for periodic batch reporting, will most likely be challenged in this area. Modularlity. FSPs that may already own some business intelligence components, or that may have preferences for specific tools or technologies, must determine the degree to which the DW offering is componentized and which elements they can substitute out. For example, some vendors require a particular underlying DBMS. In all cases where substitutions from the particular vendor solution are to be made, referenceable sites using the chosen technology or product with a similar environment including query throughput and concurrency and data volumes should be provided by the vendor, or a proof of concept should be tested; these are wise precautions under any scenario. Similar proof must also be sought for the use of specific tools or technologies where a vendor offers a less-than-complete DW solution (that is, does not cover all of the desired elements listed in Table 1). Range and depth of supplied applications and application support. FSPs must examine the range of supplied business intelligence applications, which differs significantly given the vendor's core area of competency and market focus, and compare them to the applications they desire to support. In 24 July 2003 5
some cases, vendors will not support applications themselves, but will have data extraction templates designed to provide data support through the creation of data marts or virtual data views for specific uses (such as customer profitability analysis). Where applications from other vendors will have to be used, references to sites using these applications or vendor proof of concept should be sought. Data subject areas and modeling must be reviewed and mapped to established applications and applications that will have to be developed in-house to determine coverage and suitability. Although supporting a broad array of applications, many DW solutions have traditionally focused on financial or customer relationship management (CRM) applications. Many of these solutions are being extended to support a full range of financial, CRM and risk management (see "Technology Suites Begin to Address ERM Processes") subject areas and support, as synergies are being sought across traditionally siloed subject areas. Acronym Key CRM DBMS DW ERP FSP customer relationship management database management system data warehouse enterprise resource planning financial services provider Bottom Line: Verticalized data warehouses may be viable solutions for budget- and time-pressed financial services providers. Careful mapping of requirements against the specifics of the particular offering is required to achieve a proper fit. Verticalized DW solutions vary widely; just because an offering has been targeted for financial services does not mean that it will cover a particular FSP's functional and subject area requirements, or that it will be extensible to meet future needs. 24 July 2003 6