Velocity v8 Data Warehousing Methodology

Velocity v8Data Warehousing Methodology

Data WarehousingExecutive SummaryData Warehousing, once dedicated to business intelligence and reporting, and usually at the departmental or business unit level, is today becoming a strategic corporate initiative supporting an entire enterprise across a multitude of business applications. This brisk pace of change, coupled with industry consolidation and regulatory requirements, demands that data warehouses step into a mission-critical, operational role. Information Technology (IT) plays a crucial role in delivering the data foundation for key performance indicators such as revenue growth, margin improvement and asset efficiency at the corporate, business unit and departmental levels. And IT now has the tools and methods to succeed at any of these levels. An enterprise-wide, integrated hub is the most effective approach to track and improve fundamental business measures. It is not only desirable, it is necessary and feasible. Here are the reasons why:

The traditional approach of managing information across divisions, geographies, and segments through manual consolidation and reconciliation is error -prone and cannot keep pace with the rapid changes and stricter mandates in the business. The data must be trustworthy. Executive officers are responsible for the accuracy of the data used to make management decisions, as well as for financial and regulatory reporting. Technologies have matured to the point where industry leaders are reaping the benefits of enterprise-wide data solutions, increasing their understanding of the market, and improving their agility

Organizations may choose to implement different levels of Data Warehouses from line of business level implementations to Enterprise Data Warehouses. As the size and scope of a Warehouse increases so does the complexity, risk and effort. For those that achieve an Enterprise Data Warehouse, the benefits are often the greatest. However, an organization must be committed to delivering an Enterprise Data Warehouse and must ensure the resources, budget and timeline are sufficient to overcome the organizational hurdles to having a single repository of corporate data assets.

INFORMATICA CONFIDENTIAL

Velocity v8 Methodology - Data Warehousing

2 of 1017

Business DriversThe primary business drivers responsible for a data warehouse project vary and can be very organization specific. However, a few generalities can be evidenced as trends across most organizations. Below are some of the key drivers typically responsible for driving data warehouse projects: Desire for a 360 degree view around customers, products, or other subject areas In order to make effective business decisions and have meaningful interactions with customers, suppliers and other partners it is important to gather information from a variety of systems to provide a 360 degree view of the entity. For example, consider a software company looking to provide a 360 degree view of their customers. To provide this view, it may require gathering and relating sales orders, prospective sales interactions, maintenance payments, support calls and services engagements. These items merged together paint a more complete picture of a particular customers value and interaction with the organization. The challenge is that in any organization this data might reside in numerous systems with different customer codes and structures across different technologies, making the creation of a single report nearly impossible programmatically. Thus a need arises for a centralized location to merge and rationalize this data for easy reporting - such as a Data Warehouse. Desire to provide intensive analytics reporting without impacting operational systems Operational systems are built and tuned for the best operational performance possible. A slowdown in an order entry system may cost a business lost sales and decreased customer satisfaction. Given that analytic reporting often requires summarizing and gathering large amounts of information, queries against operational systems for analytic purposes are usually discouraged and even outright prohibited for fear of impacting system performance. One key value of a data warehouse is the ability to access large data sets for analytic purposes while remaining physically separated from operational systems. This ensures that operational system performance is not adversely affected by analytic work and that business users are free to crunch large data sets and metrics without impacting daily operations. Maintaining or generating historical records In most cases, operational systems only store current state information on orders, transactions, customers, products and other data. Historical information has little use in the operational world. Point of sale transactions, for example, may be purged from



3 of 1017

operational systems after 30 days when the return policy expires. When organizations have a need for historical reporting it is often difficult or impossible to gather historical values from operational systems due to their very nature. By implementing a Data Warehouse where data is pulled in on a specified interval, historical values and information can be retained in the warehouse for any length of time an organization determines necessary. Data can also be stored and organized more efficiently for easy retrieval for analytical purposes. Standardizing on common definitions of corporate metrics across organizational boundaries As organizations grow, different areas of an organization may develop their own interpretation of business definitions and objects. To one group, a customer might mean anyone who purchased something from the web-site versus another group that believes any business or individual that received services is a customer. In order to standardize reporting and consolidation of these areas, organizations will embark on a data warehouse project to define and calculate these metrics in a common fashion across the organization. There are many other specific business drivers that can spur the need for a Data Warehouse. However, these are some of the most common seen across most organizations.

Key Success FactorsTo ensure success for a Data Warehouse implementation, there are key success factors that must be kept in mind throughout the project. Many times data warehouses are built by IT staff that have been pulled or moved from other implementation efforts such as system implementations and upgrades. In these cases, the process for implementing a Data Warehouse can be quite a change from past IT work. These Key Success Factors point out important topics to consider as you begin project planning.

Understanding Key Characteristics of a Data WarehouseWhen embarking on a Data Warehouse project it is important to recognize and keep in mind the key differentiators between a Data Warehouse project and a typical system implementation. Some of these differences are:

Data Sources are from many disparate systems, internal and external to the organization Data models are used to understand relationships and business rules within



4 of 1017

the data

Data volumes for both Data Integration and analytic reporting are high Historical data is maintained, often for periods of years Data is often stored as both detailed level data and summarized or aggregated data The underlying database system is tuned for querying large volumes of data rather than for inserting single transaction data Data Warehouse data supports tactical and strategic decision making, rather than operational processing A successful Data Warehouse is business driven. The goal of any Data Warehouse is to identify the business information needs Data Warehouse applications can have enterprisewide impact and visibility and often enable reporting at the highest levels within an organization As an enterprise level application, executive level support is vital to success

Typically data must be modeled, structured and populated in a relational database for it to be available for a Data Warehouse reporting project. Data Integration is designed based on available Operational Application sources to pull, cleanse, transform and populate an Enterprise Subject Area Database. Once the data is present in the Subject Area Database, projects can fulfill their requirements to provide Business Intelligence reporting to the Data Warehouse end users. This is done by identifying detailed reporting requirements and designing corresponding Business Intelligence Data Marts that capture all of the properly granulated facts and dimensions needed for reporting. These Data Marts are then populated using a Data Integration process and coupled to the Reporting components developed in the Business Intelligence tool.

Understanding Common Data Warehouse Project TypesNot every Data Warehouse project is a brand new implementation of a Data Warehouse. Often Warehouses are deployed in phases where subsequent implementations are simply adding new subject areas, new data sources or enhanced reporting to the existing solution. General categories of Data Warehouse projects have been defined below along with key considerations for each. New Business Data Project This type of project addresses the need to gather data from an area of the enterprise where no prior familiarity of the business data or requirements exists. All project components are required.



5 of 1017

Logical data modeling is a crucial step in this type of project, as there is a need to thoroughly understand and model the data requirements from a business perspective. The logical data model serves as the fundamental foundation and blueprint upon which all follow-on project work will be based. If not properly and sufficiently addressed, ultimate success for this type of project will be difficult to achieve; and re-work after-thefact will be costly from both a time and money perspective. Physical data modeling and data discovery components will drive out the identification and design of the new database requirements and the new data sources. New Data Integration processes must be created to bring new data into new Data Warehouse database structures. A set of history loads may be required to backload the data and bring it up to the current timeline. New Dimensional Data Mart and BI Reporting offerings must be modeled, designed and implemented to satisfy the user information and access needs. Enhanced Data Source Project This type of project addresses the need to add a new data source or to alter an existing data source, but always within the context of already established logical data structures and definitions. No logical data modeling is needed because no new business data requirements are being entertained. Minor adjustments to the physical model and database may be needed to accommodate changes in volume due to the new source or new or altered views may be needed to report on the new data instances that may now be available to the users. Data discovery analysis comprises a key portion of this type of project, as does the corresponding new or altered data integration processes that move the data to the database. Business intelligence reports and queries may need to change to incorporate new views or expanded drill-downs and data value relationships. Back loading historical data may also be required. When enhancing existing data, Metadata management efforts to track data from the physical data sources through the data integration process and to business intelligence data marts and reports can assist with impact analysis and scoping efforts. Enhanced Business Intelligence Requirements-Only Project This type of project is focused solely on the expansion or alteration of the business intelligence reporting and query capability using existing subject area data. This type of project does not entertain the introduction of any new or altered data (in structure or content) to the warehouse subject area database. New or altered dimensional data mart tables/views may be required to support the business intelligence enhancements; otherwise the majority, if not all, of the work is within the business intelligence



6 of 1017

development component.

Executive Support and Organizational Buy-InSuccessful Data Warehouse projects are usually characterized by strong organizational commitment to the delivery of enterprise analytics. The value and return on investment (ROI) must be clearly articulated early in the project and an acknowledgement of the cost and time to achieve results needs to be fully explored and understood. Typically Data Warehouse project efforts involve a steering committee of executives and business leads that drive the priority and overall vision for the organization. Executive commitment is not only needed for assigning appropriate resources and budget, but also to assist the data warehouse team in breaking down organizational barriers. It is not uncommon for a data warehouse team to encounter challenges in getting access to the data and systems necessary to build the data warehouse. Operational system owners are focused on their system and its primary function and have little interest in making their data available for warehousing efforts. At these times, the Data Warehouse steering committee can step in or rally executive support to break down these barriers in the organization. It is important to assess the business case and executive sponsorship early on in the Data Warehouse project. The project is at risk if the business value of the warehouse cannot be articulated at the executive level and on down through the organization. If executives do not have a clear picture of how the data warehouse will impact their business and the value it will provide, it wont be long before a decision is made to reduce or stop funding the effort.

Enterprise Vision and Milestone DeliveryThe Data Warehouse team should always keep the end goal in mind for an enterprise wide data warehouse. Often an enterprise data warehouse will strive to achieve a single source of truth across the entire enterprise and across all data stores. Delivering this in a big bang approach nearly always fails. By the time all of the enterprise modeling, data rationalization and data integration have taken place across all facets of the organization, the value of the project is called into question, and the project is either delayed or cancelled. In order to keep an Enterprise Data Warehouse on track, phases of deployment should be scheduled to provide value quickly and continuously throughout the lifecycle of the



7 of 1017

project. It is important for project teams to find areas of high business value that can be delivered quickly and then build upon that success as the enterprise vision is realized. The string of regular success milestones and business value keeps the executive sponsorship engaged and proves the value of the Data Warehouse to the organization early and often. The key to remember is that while these short term milestones are delivered, the Data Warehouse Team should not lose sight of the end goal of the enterprise vision. For example, when implementing customer retention metrics for two key systems - as an early win be sure to consider the 5 other systems in the organization and try to ensure that the model and process is flexible enough so that the current work will not need to be re-architected when this data is added in a later phase. Keep the final goal in mind when designing and building the incremental milestones.

Flexible and Adaptable ReportingEnd-user reporting must provide flexibility, offering straightforward reports for basic users, and for analytic users allowing drilling and roll-ups, views of both summary and detailed data and ad-hoc reporting. Report design that is too rigid may lead to clutter (as multiple structures are developed for reports that are very similar to each other) in the Business Intelligence Application and in the Data Integration and Data Warehouse Database contents. Providing flexible structures and reports allows data to be queried from the same reports and database structures without redundancy or the time required to develop new objects and Data Integration processes. Users can create reports from the common structures, thus removing the bottleneck of IT activities and the need to wait for development. Data modeling and physical database structures that reflect the business model (rather than the requirements for a single report) enable flexibility as a by-product.

Summary and Detail DataOften reporting requirements are defined for summary data. While summary data may be available from transaction and operational systems, it is best to bring the detailed data into the Data Warehouse and summarize based on that detail. This avoids potential problems due to different calculation methods, aggregating on different criteria and other ways in which the summary data brought in as a source might differ from rollups that begin with the raw detailed records. Because the summary offers smaller database table sizes, it may be tempting to bring this data in first, and then bring in the detailed data at a later stage in order to drill down to the details. Having standard sources of the raw data and using the same source for various summaries increases the quality of the data and avoids ending up with multiple versions of the truth.



8 of 1017

Engage Business Users EarlyBusiness Users must be engaged throughout the entire development process. The resulting reports and supporting data from the Data Warehouse project should address answers to business questions. If the users are not involved then it is probable that the end-result will not meet their needs for business data; and the overall success of the project is diminished. The more the business users feel that the solution is focused on solving their analytic needs the more likelihood there is of adoption.

Thorough Data Validation and MonitoringOnce lost, trust is difficult to regain. As a Data Warehouse is rolled out (and throughout its existence) it is important to thoroughly validate the data it contains in order it to maintain the end users trust in the data warehouse analytics. If a key metric is incorrect (i.e., the gross sales amount for a region in a particular month) end users may loose confidence in the system and all of its reports and metrics. If users lose faith in the analytics, this can hamper enterprise adoption and even spell the end of a data warehouse. Not only is thorough testing and validation required to ensure that data is loaded completely and accurately into the warehouse, but organizations will often create ongoing balancing and auditing procedures. These procedures are run on a regular basis to ensure metrics are accurate and that they tie out with source systems. Sometimes these procedures are manual and sometimes they are automated. If the warehouse is suspected to be inaccurate - or a daily load fails to run communications are initiated with end users to alert them to the problem. It is better to limit user reporting for a morning until the issues are addressed, than to risk that an executive makes a critical business decision with incorrect data.

Last updated: 27-May-08 23:05



9 of 1017

Roles

Velocity Roles and Responsibilities Application Specialist Business Analyst Business Project Manager Data Architect Data Integration Developer Data Quality Developer Data Steward/Data Quality Steward Data Warehouse Administrator Database Administrator (DBA) End User Metadata Manager PowerCenter Domain Administrator Presentation Layer Developer Production Supervisor Project Sponsor Quality Assurance Manager Repository Administrator Technical Architect Technical Project Manager Test Engineer Test Manager Training Coordinator User Acceptance Test Lead



10 of 1017

Velocity Roles and ResponsibilitiesThe following pages describe the roles used throughout this Guide, along with the responsibilities typically associated with each. Please note that the concept of a role is distinct from that of an employee or full time equivalent (FTE). A role encapsulates a set of responsibilities that may be fulfilled by a single person in a part-time or fulltime capacity, or may be accomplished by a number of people working together. The Velocity Guide refers to roles with an implicit assumption that there is a corresponding person in that role. For example, a task description may discuss the involvement of "the DBA" on a particular project, however, there may be one or more DBAs, or a person whose part-time responsibility is database administration. In addition, note that there is no assumption of staffing level for each role -- that is, a small project may have one individual filling the role of Data Integration Developer, Data Architect, and Database Administrator, while large projects may have multiple individuals assigned to each role. In cases where multiple people represent a given role, the singular role name is used, and project planners can specify the actual allocation of work among all relevant parties. For example, the methodology always refers to the Technical Architect, when in fact, there may be a team of two or more people developing the Technical Architecture for a very large development effort.

Data Integration Project - Sample Organization Chart




11 of 1017

Application SpecialistSuccessful data integration projects are built on a foundation of thorough understanding of the source and target applications. The Application Specialist is responsible for providing detailed information on data models, metadata, audit controls and processing controls to Business Analysts, Technical Architects and others regarding the source and/or target system. This role is normally filled by someone from a technical background who is able to query/analyze the data hands-on. The person filling this role should have a good business understanding of how the data is generated and maintained and good relationships with the Data Steward and the users of the data.

Reports to:

Technical Project Manager

Responsibilities:

Authority on application system data and process models Advises on known and anticipated data quality issues Supports the construction of representative test data sets

Qualifications/Certifications

Possesses excellent communication skills, both written and verbal Must be able to work effectively with both business and technical stakeholders Works independently with minimal supervision

Recommended Training

Informatica Data Explorer

Last updated: 09-Apr-07 15:38



12 of 1017

Business AnalystThe primary role of the Business Analyst (sometimes known as the Functional Analyst) is to represent the interests of the business in the development of the data integration solution. The secondary role is to function as an interpreter for business and technical staff, translating concepts and terminology and generally bridging gaps in understanding. Under normal circumstances, someone from the business community fills this role, since deep knowledge of the business requirement is indispensable. Ideally, familiarity with the technology and the development life-cycle allows the individual to function as the communications channel between technical and business users.

Reports to:

Business Project Manager

Responsibilities:

Ensures that the delivered solution fulfills the needs of the business (should be involved in decisions related to the business requirements) Assists in determining the data integration system project scope, time and required resources Provides support and analysis of data collection, mapping, aggregation and balancing functions Performs requirements analysis, documentation, testing, ad-hoc reporting, user support and project leadership Produces detailed business process flows, functional requirements specifications and data models and communicates these requirements to the design and build teams Conducts cost/benefit assessments of the functionality requested by end-users Prioritizes and balances competing priorities Plans and authors the user documentation set


Possesses excellent communication skills, both written and verbal



13 of 1017

Must be able to work effectively with both business and technical stakeholders Works independently with minimal supervision Has knowledge of the tools and technologies used in the data integration solution Holds certification in industry vertical knowledge (if applicable)


Interview/workshop techniques Project Management Data Analysis Structured analysis UML or other business design methodology Data Warehouse Development




14 of 1017

Business Project ManagerThe Business Project Manager has overall responsibility for the delivery of the data integration solution. As such, the Business Project Manager works with the project sponsor, technical project manager, user community, and development team to strike an appropriate balance of business needs, resource availability, project scope, schedule, and budget to deliver specified requirements and meet customer satisfaction.

Reports to:

Project Sponsor

Responsibilities:

Develops and manages the project work plan Manages project scope, time-line and budget Resolves budget issues Works with the Technical Project Manager to procure and assign the appropriate resources for the project Communicates project progress to Project Sponsor(s) Is responsible for ensuring delivery on commitments and ensuring that the delivered solution fulfills the needs of the business Performs requirements analysis, documentation, ad-hoc reporting and project leadership


Translates strategies into deliverables Prioritizes and balances competing priorities Possesses excellent communication skills, both written and verbal Results oriented team player Must be able to work effectively with both business and technical stakeholders Works independently with minimal supervision Has knowledge of the tools and technologies used in the data integration



15 of 1017

solution

Holds certification in industry vertical knowledge (if applicable)


Project Management




16 of 1017

Data ArchitectThe Data Architect is responsible for the delivery of a robust scalable data architecture that meets the business goals of the organization. The Data Architect develops the logical data models, and documents the models in Entity-Relationship Diagrams (ERD). The Data Architect must work with the Business Analysts and Data Integration Developers to translate the business requirements into a logical model. The logical model is captured in the ERD, which then feeds the work of the Database Administrator, who designs and implements the physical database. Depending on the specific structure of the development organization, the Data Architect may also be considered a Data Warehouse Architect, in cooperation with the Technical Architect. This role involves developing the overall Data Warehouse logical architecture, specifically the configuration of the data warehouse, data marts, and an operational data store or staging area if necessary. The physical implementation of the architecture is the responsibility of the Database Administrator.

Reports to:


Responsibilities:

Designs an information strategy that maximizes the value of data as an enterprise asset Maintains logical/physical data models Coordinates the metadata associated with the application Develops technical design documents Develops and communicates data standards Maintains Data Quality metrics Plans architectures and infrastructures in support of data management processes and procedures Supports the build out of the Data Warehouse, Data Marts and operational data store Effectively communicates with other technology and product team members




17 of 1017

Strong understanding of data integration concepts Understanding of multiple data architectures that can support a Data Warehouse Ability to translate functional requirements into technical design specifications Ability to develop technical design documents and test case documents Experience in optimizing data loads and data transformations Industry vertical experience is essential Project Solution experience is desired Has had some exposure to Project Management Has worked with Modeling Packages Has experience with at least one RDBMS Strong Business Analysis and problem solving skills Familiarity with Enterprise Architecture Structures (Zachman/TOGAF)


Modeling Packages Data Warehouse Development

Last updated: 01-Feb-07 18:51



18 of 1017

Data Integration DeveloperThe Data Integration Developer is responsible for the design, build, and deployment of the project's data integration component. A typical data integration effort usually involves multiple Data Integration Developers developing the Informatica mappings, executing sessions, and validating the results.

Reports to:


Responsibilities:

Uses the Informatica Data Integration platform to extract, transform, and load data Develops Informatica mapping designs Develops Data Integration Workflows and load processes Ensures adherence to locally defined standards for all developed components Performs data analysis for both Source and Target tables/columns Provides technical documentation of Source and Target mappings Supports the development and design of the internal data integration framework Participates in design and development reviews Works with System owners to resolve source data issues and refine transformation rules Ensures performance metrics are met and tracked Writes and maintains unit tests Conduct QA Reviews Performs production migrations


Understands data integration processes and how to tune for performance Has SQL experience



19 of 1017

Possesses excellent communications skills Has the ability to develop work plans and follow through on assignments with minimal guidance Has Informatica Data Integration Platform experience Is an Informatica Certified Designer Has RDBMS experience Has the ability to work with business and system owners to obtain requirements and manage expectations


Data Modeling PowerCenter Level I & II Developer PowerCenter - Performance Tuning PowerCenter - Team Based Development PowerCenter - Advanced Mapping Techniques PowerCenter - Advanced Workflow Techniques PowerCenter - XML Support PowerCenter - Data Profiling PowerExchange




20 of 1017

Data Quality DeveloperThe Data Quality Developer (DQ Developer) is responsible for designing, testing, deploying, and documenting the project's data quality procedures and their outputs. The DQ Developer provides the Data Integration Developer with all relevant outputs and results from the data quality procedures, including any ongoing procedures that will run in the Operate phase or after project-end. The DQ Developer must provide the Business Analyst with the summary results of data quality analysis as needed during the project. The DQ Developer must also document at a functional level how the procedures work within the data quality applications. The primary tasks associated with this role are to use Informatica Data Quality and Informatica Data Explorer to profile the project source data, define or confirm the definition of the metadata, cleanse and accuracy-check the project data, check for duplicate or redundant records, and provide the Data Integration Developer with concrete proposals on how to proceed with the ETL processes.

Reports to:


Responsibilities:

Profile source data and determine all source data and metadata characteristics Design and execute Data Quality Audit Present profiling/audit results, in summary and in detail, to the business analyst, the project manager, and the data steward Assist the business analyst/project manager/data steward in defining or modifying the project plan based on these results Assist the Data Integration Developer in designing source-to-target mappings Design and execute the data quality plans that will cleanse, de-duplicate, and otherwise prepare the project data for the Build phase Test Data Quality plans for accuracy and completeness Assist in deploying plans that will run in a scheduled or batch environment Document all plans in detail and hand-over documentation to the customer Assist in any other areas relating to the use of data quality processes, such as unit testing




21 of 1017

Has knowledge of the tools and technologies used in the data quality solution Results oriented team player Possesses excellent communication skills, both written and verbal Must be able to work effectively with both business and technical stakeholders


Data Quality Workbench I & II Data Explorer Level I PowerCenter Level I Developer Basic RDBMS Training Data Warehouse Development




22 of 1017

Data Steward/Data Quality StewardThe Data Steward owns the data and associated business and technical rules on behalf of the Project Sponsor. This role has responsibility for defining and maintaining business and technical rules, liaising with the business and technical communities, and resolving issues relating to the data. The Data Steward will be the primary contact for all questions relating to the data, its use, processing and quality. In essence, this role formalizes the accountability for the management of organizational data. Typically the Data Steward is a key member of a Data Stewardship Committee put into place by the Project Sponsor. This committee will include business users and technical staff such as Application Experts. There is often an arbitration element to the role where data is put to different uses by separate groups of users whose requirements have to be reconciled.

Reports to:


Responsibilities:

Records the business use for defined data Identifies opportunities to share and re-use data Decides upon the target data quality metrics Monitors the progress towards, and tuning of, data quality target metrics Oversees data quality strategy and remedial measures Participates in the enforcement of data quality standards Enters, maintains and verifies data changes Ensures the quality, completeness and accuracy of data definitions Communicates concerns, issues and problems with data to the individuals that can influence change Researches and resolves data issues




23 of 1017

Possesses strong analytical and problem solving skills Has experience in managing data standardization in a large organization, including setting and executing strategy Previous industry vertical experience is essential Possesses excellent communication skills, both written and verbal Exhibits effective negotiating skills Displays meticulous attention to detail Must be able to work effectively with both business and technical stakeholders Works independently with minimal supervision Project solution experience is desirable


Data Quality Workbench Level I Data Explorer Level I




24 of 1017

Data Warehouse AdministratorThe scope of the Data Warehouse Administrator role is similar to that of the DBA. A typical data integration solution however, involves more than a single target database and the Data Warehouse Administrator is responsible for coordinating the many facets of the solution, including operational considerations of the data warehouse, security, job scheduling and submission, and resolution of production failures.

Reports to:


Responsibilities:

Monitors and supports the Enterprise Data Warehouse environment Manages the data extraction, transformation, movement, loading, cleansing and updating processes into the DW environment Maintains the DW repository Implements database security Sets standards and procedures for the DW environment Implements technology improvements Works to resolve technical issues Contributes to technical and system architectural planning Tests and implements new technical solutions


Experience in supporting Data Warehouse environments Familiarity with database, integration and presentation technology Experience in developing and supporting real-time and batch-driven data movements Solid understanding of relational database models and dimensional data models Strategic planning and system analysis



25 of 1017

Able to work effectively with both business and technical stakeholders Works independently with minimal supervision


DBMS Administration Data Warehouse Development PowerCenter Administrator Level I & II PowerCenter Security and Migration PowerCenter Metadata Manager




26 of 1017

Database Administrator (DBA)The Database Administrator (DBA) in a Data Integration Solution is typically responsible for translating the logical model (i.e., the ERD) into a physical model for implementation in the chosen DBMS, implementing the model, developing volume and capacity estimates, performance tuning, and general administration of the DBMS. In many cases, the project DBA also has useful knowledge of existing source database systems. In most cases, a DBA's skills are tied to a particular DBMS, such as Oracle or Sybase. As a result, an analytic solution with heterogeneous sources/targets may require the involvement of several DBAs. The Project Manager and Data Warehouse Administrator are responsible for ensuring that the DBAs are working in concert toward a common solution.

Reports to:


Responsibilities:

Plans, implements and supports enterprise databases Establishes and maintains database security and integrity controls Delivers database services while managing to policies, procedures and standards Tests and implements new technical solutions Monitors and supports the database infrastructure (including clients) Develops volume and capacity estimates Proposes and implements enhancements to improve performance and reliability Provides operational support of databases, including backup and recovery Develops programs to migrate data between systems Works to resolve technical issues Contributes to technical and system architectural planning Supports data integration developers in troubleshooting performance issues Collaborates with other Departments (i.e., Network Administrators) to identify and resolve performance issues



27 of 1017


Experience in database administration, backup and recovery Expertise in database configuration and tuning Appreciation of DI tool-set and associated tools Experience in developing and supporting ETL real-time and batch processes Strategic planning and system analysis Strong analytical and communication skills Able to work effectively with both business and technical stakeholders Ability to work independently with minimal supervision


DBMS Administration




28 of 1017

End UserThe End User is the ultimate "consumer" of the data in the data warehouse and/or data marts. As such, the end user represents a key customer constituent (management is another), and must therefore be heavily involved in the development of a data integration solution. Specifically, a representative of the End User community must be involved in gathering and clarifying the business requirements, developing the solution and User Acceptance Testing (if applicable).

Reports to:


Responsibilities:

Gathers and clarifies business requirements Reviews technical design proposals Participates in User Acceptance testing Provides feedback on the user experience


Strong understanding of the business' processes Good communication skills


Data Analyzer - Quickstart Data Analyzer - Report Development




29 of 1017

Metadata ManagerThe Metadata Manager's primary role is to serve as the central point of contact for all corporate metadata management. This role involves setting the company's metadata strategy, developing standards with the data administration group, determining metadata points of integration between disparate systems, and ensuring the ability to deliver metadata to business and technical users. The Metadata Manager is required to work across business and technical groups to ensure that consistent metadata standards are followed in all existing applications as well as in new development. The Metadata Manager also monitors PowerCenter repositories for accuracy and metadata consistency.

Reports to:


Responsibilities:

Formulates and implements the metadata strategy Captures and integrates metadata from heterogeneous metadata sources Implements and governs best practices relating to enterprise metadata management standards Determines metadata points of integration between disparate systems Ensures the ability to deliver metadata to business and technical users Monitors development repositories for accuracy and metadata consistency Identifies and profiles data sources to populate the metadata repository Designs metadata repository models


Business sector experience is essential Experience in implementing and managing a repository environment Experience in data modeling (relational and dimensional) Experience in using repository tools Solid knowledge of general data architecture concepts, standards and best



30 of 1017

practices

Strong analytical skills Excellent communication skills, both written and verbal Proven ability to work effectively with both business users and technical stakeholders


DBMS Basics Data Modeling PowerCenter - Metadata Manager




31 of 1017

PowerCenter Domain AdministratorThe PowerCenter Domain Administrator is responsible for administering the Informatica Data Integration environment. This involves the management and administration of all components in the PowerCenter domain. The PowerCenter Domain Administrator works closely with the Technical Architect and other project personnel during the Architect, Build and Deploy phases to plan, configure, support and maintain the desired PowerCenter configuration. The PowerCenter Domain Administrator is reponsible for the domain security configuration, licensing and the physical linstall and location of the services and nodes that compose the domain.

Reports to:


Responsibilities:

Manages the PowerCenter Domain, Nodes, Service Manager and Application Services Develops Disaster recovery and failover strategies for the Data Integration Environment Responsible for High Availability and PowerCenter Grid configuration Creates new services as nodes as needed Ensures proper configuration of the PowerCenter Domain components Ensures proper application of the licensing files to nodes and services Manages user and user group access to the domain components Manages backup and recovery of the domain metadata and appropriate shared file directories Monitors domain services and troubleshoots any errors Applies software updates as required Tests and implements new technical solutions




32 of 1017

Informatica Certified Administrator Experience in supporting Data Warehouse environments Experience in developing and supporting ETL real-time and batch processes Solid understanding of relational database models and dimensional data models


PowerCenter Administrator Level I and Level II




33 of 1017

Presentation Layer DeveloperThe Presentation Layer Developer is responsible for the design, build, and deployment of the presentation layer component of the data integration solution. This component provides the user interface to the data warehouses, data marts and other products of the data integration effort. As the interface is highly visible to the enterprise, a person in this role must work closely with end users to gain a full understanding of their needs. The Presentation Layer Developer designs the application, ensuring that the end-user requirements gathered during the requirements definition phase are accurately met by the final build of the application. In most cases, the developer works with front-end Business Intelligence tools, such as Cognos, Business Objects and others. To be most effective, the Presentation Layer Developer should be familiar with metadata concepts and the Data Warehouse/Data Mart data model.

Reports to:


Responsibilities:

Collaborates with ends users and other stakeholders to define detailed requirements Designs business intelligence solutions that meet user requirements for accessing and analyzing data Works with front-end business intelligence tools to design the reporting environment Works with the DBA and Data Architect to optimize reporting performance Develops supporting documentation for the application Participates in the full testing cycle


Solid understanding of metadata concepts and the Data Warehouse/Data Mart model



34 of 1017

Aptitude with front-end business intelligence tools (i.e., Cognos, Business Objects, Informatica Data Analyzer) Excellent problem solving and trouble-shooting skills Solid interpersonal skills and ability to work with business and system owners to obtain requirements and manage expectations Capable of expressing technical concepts in business terms


Informatica Data Analyzer Data Warehouse Development




35 of 1017

Production SupervisorThe Production Supervisor has operational oversight for the production environment and the daily execution of workflows, sessions and other data integration processes. Responsibilities includes, but are not limited to - training and supervision of system operators, review of execution statistics, managing the scheduling for upgrades to the system and application software as well as the release of data integration processes.

Reports to:

Information Technology Lead

Responsibilities:

Manages the daily execution of workflows and sessions in the production environment Trains and supervises the work of system operators Reviews and audits execution logs and statistics and escalates issues appropriately Schedules the release of new sessions or workflows Schedules upgrades to the system and application software Ensures that work instructions are followed Monitors data integration processes for performance Monitors data integration components to ensure appropriate storage and capacity for daily volumes


Production supervisory experience Effective leadership skills Strong problem solving skills Excellent organizational and follow-up skills


PowerCenter Level I Developer



36 of 1017

PowerCenter Team Based Development PowerCenter Advanced Workflow Techniques PowerCenter Security and Migration




37 of 1017

Project SponsorThe Project Sponsor is typically a member of the business community rather than an IT/IS resource. This is important because the lack of business sponsorship is often a contributing cause of systems implementation failure. The Project Sponsor often initiates the effort, serves as project champion, guides the Project Managers in understanding business priorities, and reports status of the implementation to executive leadership. Once an implementation is complete, the Project Sponsor may also serve as "chief evangelist", bringing word of the successful implementation to other areas within the organization.

Reports to:

Executive Leadership

Responsibilities:

Provides the business sponsorship for the project Champions the project within the business Initiates the project effort Guides the Project Managers in understanding business requirements and priorities Assists in determining the data integration system project scope, time, budget and required resources Reports status of the implementation to executive leadership


Has industry vertical knowledge


N/A




38 of 1017

Quality Assurance ManagerThe Quality Assurance (QA) Manager ensures that the original intent of the business case is achieved in the actual implementation of the analytic solution. This involves leading the efforts to validate the integrity of the data throughout the data integration processes, and ensuring that the utlimate data target has been accurately derived from the source data. The QA Manager can be a member of the IT organization, but serve as a liaison to the business community (i.e., the Business Analysts and End Users). In situations where issues arise with regard to the quality of the solution, the QA Manager works with project management and the development team to resolve them. Depending upon the test approach taken by the project team, the QA Manager may also serve as the Test Manager.

Reports to:


Responsibilities:

Leads the effort to validate the integrity of the data through the data integration processes Ensures that the data contained in the data integration solution has been accurately derived from the source data Develops and maintains quality assurance plans and test requirements documentation Verifies compliance to commitments contained in quality plans Works with the project management and development teams to resolve issues Participates in the enforcement of data quality standards Communicates concerns, issues and problems with data Participates in the testing and post-production verification Together with the Technical Lead and the Repository Administrator, articulates the development standards Advises on the development methods to ensure that quality is built in Designs the QA and standards enforcement strategy Together with the Test Manager, coordinates the QA and Test strategies



39 of 1017

Manages the implementation of the QA strategy


Industry vertical knowledge Solid understanding of the Software Development Life Cycle Experience in quality assurance performance, auditing processes, best practices and procedures Experience with automated testing tools Knowledge of Data Warehouse and Data Integration enterprise environments Able to work effectively with both business and technical stakeholders


PowerCenter Level I Developer Infomatica Data Explorer Informatica Data Quality Workbench Project Management




40 of 1017

Repository AdministratorThe Repository Administrator is responsible for administering a PowerCenter or Data Analyzer Repository. This requires maintaining the organization and security of the objects contained in the repository. It entails developing and maintaining the folder and schema structures, managing users, groups, and roles, global/local repository relationships and backup and recovery. During the development effort, the Repository Administrator is responsible for coordinating migrations, maintaining database connections, establishing and promoting naming conventions and development standards, and developing back-up and restore procedures for the repositories. The Repository Administrator works closely with the Technical Architect and other project personnel during the Architect, Build and Deploy phases to plan, configure, support and maintain the desired PowerCenter and Data Analyzer configuration.

Reports to:


Responsibilities:

Develops and maintains the repository folder structure Manages user and user group access to objects in the repository Manages PowerCenter global/local repository relationships and security levels Coordinates migration of data during the development effort Establishes and promotes naming conventions and development standards Develops back-up and restore procedures for the repository Works to resolve technical issues Contributes to technical and system architectural planning Tests and implements new technical solutions


Informatica Certified Administrator Experience in supporting Data Warehouse environments



41 of 1017

Experience in developing and supporting ETL real-time and batch processes Solid understanding of relational database models and dimensional data models


PowerCenter Administrator Level I and Level II Data Analyzer Introduction




42 of 1017

Technical ArchitectThe Technical Architect is responsible for the conceptualization, design, and implementation of a sound technical architecture, which includes both hardware and software components. The Architect interacts with the Project Management and design teams early in the development effort in order to understand the scope of the business problem and its solution. The Technical Architect must always consider both current (stated) requirements and future (unstated) directions. Having this perspective helps to ensure that the architecture can expand to correspond with the growth of the data integration solution. This is particularly critical given the highly iterative nature of data integration solution development.

Reports to:


Responsibilities:

Develops the architectural design for a highly scalable, large volume enterprise solution Performs high-level architectural planning, proof-of-concept and software design Defines and implements standards, shared components and approaches Functions as the Design Authority in technical design reviews Contributes to development project estimates, scheduling and development reviews Approves code reviews and technical deliverables Assures architectural integrity Maintains compliance with change control, SDLC and development standards Develops and reviews implementation plans and contingency plans


Software development expertise (previous development experience of the application type) Deep understanding of all technical components of the application solution



43 of 1017

Understanding of industry standard data integration architectures Ability to translate functional requirements into technical design specifications Ability to develop technical design documents Strong Business Analysis and problem solving skills Familiarity with Enterprise Architecture Structures (Zachman/TOGAF) or equivalent Experience and/or training in appropriate platforms for the project Familiarity with appropriate modeling techniques such as UML and ER modeling as appropriate


Operating Systems DBMS PowerCenter Developer and Administrator - Level I PowerCenter New Features Basic and advanced XML




44 of 1017

Technical Project ManagerThe Technical Project Manager has overall responsibility for managing the technical resources within a project. As such, he/she works with the project sponsor, business project manager and development team to assign the appropriate resources for a project within the scope, schedule, and budget and to ensure that project deliverables are met.

Reports to:

Project Sponsor or Business Project Manager

Responsibilities:

Defines and implements the methodology adopted for the project Liaises with the Project Sponsor and Business Project Manager Manages project resources within the project scope, time-line and budget Ensures all business requirements are accurate Communicates project progress to Project Sponsor(s) Is responsible for ensuring delivery on commitments and ensuring that the delivered solution fulfills the needs of the business Performs requirements analysis, documentation, ad-hoc reporting and resource leadership


Translates strategies into deliverables Prioritizes and balances competing priorities Must be able to work effectively with both business and technical stakeholders Has knowledge of the tools and technologies used in the data integration solution Holds certification in industry vertical knowledge (if applicable)




45 of 1017

Project Management Techniques PowerCenter Developer Level I PowerCenter Administrator Level I Data Analyzer Introduction




46 of 1017

Test EngineerThe Test Engineer is responsible for completion of test plans and their execution. During test planning, the Test Engineer works with the Testing Manager/Quality Assurance Manager to finalize the test plans and to ensure that the requirements are testable. The Test Engineer is also responsible for complete execution including design and implementing test scripts, test suites of test cases, and test data. The Test Engineer should be able to demonstrate knowledge of testing techniques and to provide feedback to developers. He/She uses the procedures as defined in the test strategy to execute, report results and progress of test execution and to escalate testing issues as appropriate.

Reports to:

Test Manager (or Quality Assurance Manager)

Responsibilities:

Provides input to the test plan and executes it Carries out requested procedures to ensure that Data Integration systems and services meet organization standards and business requirements Develops and maintains test plans, test requirements documentation, test cases and test scripts Verifies compliance to commitments contained in the test plans Escalates issues and works to resolve them Participates in testing and post-production verification efforts Executes test scripts and documents and provides the results to the test manager Provides feedback to developers Investigates and resolves test failures


Solid understanding of the Software Development Life Cycle Experience with automated testing tools Strong knowledge of Data Warehouse and Data Integration enterprise



47 of 1017

environments

Experience in a quality assurance and testing environment Experience in developing and executing test cases and in setting up complex test environments Industry vertical knowledge


PowerCenter Developer Level I &II Data Analyzer Introduction SQL Basics Data Quality Workbench




48 of 1017

Test ManagerThe Test Manager is responsible for coordinating all aspects of test planning and execution. During test planning, the Test Manager becomes familiar with the business requirements in order to develop sufficient test coverage for all planned functionality. He/she also develops a test schedule that fits into the overall project plan. Typically, the Test Manager works with a development counterpart during test execution; the development manager schedules and oversees the completion of fixes for bugs found during testing. The test manager is also responsible for the creation of the test data set. An integrated test data set is a valuable project resource in its own right; apart from its obvious role in testing, the test data set is very useful to the developers of integration and presentation components. In general, separate functional and volume test data sets will be required. In most cases, these should be derived from the production environment. It may also be necessary to manufacture a data set which triggers all the business rules and transformations specified for the application. Finally, the Test Manager must continually advocate adherence to the Test Plans. Projects at risk of delayed completion often sacrifice testing at the expense of a highquality end result.

Reports to:

Technical Project Manager (or Quality Assurance Manager)

Responsibilities:

Coordinates all aspects of test planning and execution Carries out procedures to ensure that Data Integration systems and services meet organization standards and business requirements Develops and maintains test plans, test requirements documentation, test cases and test scripts Develops and maintains test data sets Verifies compliance to commitments contained in the test plans Works with the project management and development teams to resolve issues Communicates concerns, issues and problems with data



49 of 1017

Leads testing and post-production verification efforts Executes test scripts and documents and publishes the results Investigates and resolves test failures


Solid understanding of the Software Development Life Cycle Experience with automated testing tools Strong knowledge of Data Warehouse and Data Integration enterprise environments Experience in a quality assurance and testing environment Experience in developing and executing test cases and in setting up complex test environments Experience in classifying, tracking and verifying bug fixes Industry vertical knowledge Able to work effectively with both business and technical stakeholders Project management


PowerCenter Developer Level I Data Analyzer Introduction Data Explorer




50 of 1017

Training CoordinatorThe Training Coordinator is responsible for the design, development, and delivery of all requisite training materials. The deployment of a data integration solution can only be successful if the End Users fully understand the purpose of the solution, the data and metadata available to them, and the types of analysis they can perform using the application. The Training Coordinator will work the Project Management Team, the development team, and the End Users to ensure that he/she fully understands the training needs, and develops the appropriate training material and delivery approach. The Training Coordinator will also schedule and manage the delivery of the actual training material to the End Users.

Reports to:


Responsibilities:

Designs, develops and delivers training materials Schedules and manages logistical aspects of training for end users Performs training need analysis in conjunction with the Project Manager, development team and end users Interviews subject matter experts Ensures delivery on training commitments


Experience in the training field Ability to create training materials in multiple formats (i.e., written, computerbased, instructor-led, etc.) Possesses excellent communication skills, both written and verbal Results oriented team player Must be able to work effectively with both business and technical stakeholders Has knowledge of the tools and technologies used in the data integration solution



51 of 1017


Training Needs Analysis Data Analyzer Introduction Data Analyzer Report Creation




52 of 1017

User Acceptance Test LeadThe User Acceptance Test Lead is responsible for leading the final testing and gaining final approval from the business users. The User Acceptance Test Lead interacts with the End Users and the design team during the development effort to ensure the inclusion of all the user requirements within the original defined scope. He/ she then validates that the deployed solution meets the final user requirements.

Reports to:


Responsibilities:

Gathers and clarifies business requirements Interacts with the design team and end users during the development efforts to ensure inclusion of users requirements within the defined scope Reviews technical design proposals Schedules and leads the user acceptance test effort Provides test script/case training to the user acceptance test team Reports on test activities and results Validates that the deployed solution meets the final user requirements


Experience planning and executing user acceptance testing Strong understanding of the business' processes Knowledge of the project solution Excellent communication skills


N/A



53 of 1017

Last updated: 12-Jun-07 16:06



54 of 1017

Phase 1: Manage1 Manage

1.1 Define Projectr

1.1.1 Establish Business Project Scope 1.1.2 Build Business Case 1.1.3 Assess Centralized Resources

r

r

1.2 Plan and Manage Projectr

1.2.1 Establish Project Roles 1.2.2 Develop Project Estimate 1.2.3 Develop Project Plan 1.2.4 Manage Project

r

r

r

1.3 Perform Project Close



55 of 1017

Phase 1: ManageDescription

Managing the development of a data integration solution requires extensive planning. A well-defined, comprehensive plan provides the foundation from which to build a project solution. The goal of this phase is to address the key elements required for a solid project foundation. These elements include:

Scope - Clearly defined business objectives. The measurable, businessrelevant outcomes expected from the project should be established early in the development effort. Then, an estimate of the expected Return on Investment (ROI) can be developed to gauge the level of investment and anticipated return. The business objectives should also spell out a complete inventory of business processes to facilitate a collective understanding of these processes among project team members. Planning/Managing - The project plan should detail the project scope as well as its objectives, required work efforts, risks, and assumptions. A thorough, comprehensive scope can be used to develop a work breakdown structure (WBS) and establish project roles for summary task assignments. The plan should also spell out the change and control process that will be used for the project. Project Close/Wrap-Up - At the end of each project, the final step is to obtain project closure. Part of this closure is to ensure the completeness of the effort and obtain sign-off for the project. Additionally, a project evaluation will help in retaining lessons learned and assessing the success of the overall effort.

PrerequisitesNone

RolesBusiness Project Manager (Primary) Data Integration Developer (Secondary)



56 of 1017

Data Quality Developer (Secondary) Data Transformation Developer (Secondary) Presentation Layer Developer (Secondary) Production Supervisor (Approve) Project Sponsor (Primary) Quality Assurance Manager (Approve) Technical Architect (Primary) Technical Project Manager (Primary)

ConsiderationsNone

Best PracticesNone

Sample DeliverablesNoneLast updated: 20-May-08 18:53



57 of 1017

Phase 1: ManageTask 1.1 Define Project DescriptionThis task entails constructing the business context for the project, defining in business terms the purpose and scope of the project as well as the value to the business (i.e., the business case).

PrerequisitesNone

RolesBusiness Analyst (Primary) Business Project Manager (Primary) Project Sponsor (Primary)

ConsiderationsThere are no technical considerations during this task; in fact, any discussion of implementation specifics should be avoided at this time. The focus here is on defining the project deliverable in business terms with no regard for technical feasibility. Any discussion of technologies is likely to sidetrack the strategic thinking needed to develop the project objectives.

Best PracticesNone

Sample DeliverablesProject Definition



58 of 1017




59 of 1017

Phase 1: ManageSubtask 1.1.1 Establish Business Project Scope DescriptionIn many ways the potential for success of the development effort for a data integration solution correlates directly to the clarity and focus of its business scope. If the business purpose is unclear or the boundaries of the business objectives are poorly defined, there is a much higher risk of failure or, at least, of a less-than-direct path to limited success.

PrerequisitesNone

RolesBusiness Analyst (Primary) Business Project Manager (Review Only) Project Sponsor (Primary)

ConsiderationsThe primary consideration in developing the Business Project Scope is balancing the high-priority needs of the key beneficiaries with the need to provide results within the near-term. The Project Manager and Business Analysts need to determine the key business needs and determine the feasibility of meeting those needs to establish a scope that provides value, typically within a 60 to 120 day time-frame.



60 of 1017

Tip As a general rule, involve as many project beneficiaries as possible in the needs assessment and goal definition. A "forum" type of meeting may be the most efficient way to gather the necessary information since it minimizes the amount of time involved in individual interviews and often encourages useful dialog among the participants. However, it is often difficult to gather all of the project beneficiaries and the project sponsor together for any single meeting, so you may have to arrange multiple meetings and summarize the input for the various participants.

Best PracticesNone

Sample DeliverablesProject Charter




61 of 1017

Phase 1: ManageSubtask 1.1.2 Build Business Case DescriptionBuilding support and funding for a data integration solution nearly always requires convincing executive IT management of its value to the business. The best way to do this, if possible, is to actually calculate the project's estimated return on investment (ROI) through a business case that calculates ROI. ROI modeling is valuable because it:

Supplies a fundamental cost-justification framework for evaluating a data integration project. Mandates advance planning among all appropriate parties, including IT team members, business users, and executive management. Helps organizations clarify and agree on the benefits they expect, and in that process, helps them set realistic expectations for the data integration solution or the data quality initiative.

In addition to traditional ROI modeling on data integration initiatives, quantitative and qualitative ROI assessments should also include assessments of data quality. Poor data quality costs organizations vast sums in lost revenues. Defective data leads to breakdowns in the supply chain, poor business decisions, and inferior customer relationship management. Moreover, poor quality data can lead to failures in compliance with industry regulations and even to outright project failure at the IT level. It is vital to acknowledge data quality issues at an early stage in the project. Consider a data integration project that is planned and resourced meticulously but that is undertaken on a dataset where the data is of a poorer quality than anyone realized. This can lead to the classic code-load-explode scenario, wherein the data breaks down in the target system due to a poor understanding of the data and metadata. What is worse, a data integration project can succeed from an IT perspective but deliver little if any business value if the data within the system is faulty. For example, a CRM system containing a dataset with a large quantity of redundant or inaccurate records is likely to be of little value to the business. Often an organization does not realize it has data quality issues until it is too late. For this reason, data quality should be a consideration in ROI modeling for all data integration projects from the beginning. For more details on how to quantify business value and associated data integration project cost, please see Assessing the Business Case.

Prerequisites1.1.1 Establish Business Project Scope

RolesBusiness Project Manager (Secondary)

ConsiderationsThe Business Case must focus on business value and, as much as possible, quantify that value. The business beneficiaries are primarily responsible for assessing the project benefits, while technical considerations drive the cost assessments. These two assessments - benefits and costs - form the basis for determining overall ROI to the business.

Building the Business Case Step 1 - Business BenefitsWhen creating your ROI model, it is best to start by looking at the expected business benefit of implementing the data integration solution. Common business imperatives include:

Improving decision-making and ensuring regulatory compliance. Modernizing the business to reduce costs.



62 of 1017

Merging and acquiring other organizations. Increasing business profitability. Outsourcing non-core business functions to be able to focus on your companys core value proposition.

Each of these business imperatives requires support via substantial IT initiatives. Common IT initiatives include:

Business intelligence initiatives. Retirement of legacy systems. Application consolidation initiatives. Establishment of data hubs for customer, supplier, and/or product data. Business process outsourcing (BPO) and/or Software as a Service (SaaS).

For these IT initiatives to be successful, you must be able to integrate data from a variety of disparate systems. The form of those data integration projects may vary. You may have a:

Data Warehousing project, which enables new business insight usually through business intelligence. Data Migration project, where data sources are moved to enable a new application or system. Data Consolidation project, where certain data sources or applications are retired in favor of another. Master Data Management project, where multiple data sources come together to form a more complex, master view of the data. Data Synchronization project, where data between two source systems need to stay perfectly consistent to enable different applications or systems. B2B Data Transformation project, where data from external partners is transformed to internal formats for processing by internal systems and responses are transformed back to partner appropriate formats. Data Quality project, where the goals are to cleanse data and to correct errors such as duplicates, missing information, mistyped information and other data deficiencies.

Once you have established the heritage of your data integration project back to its origins in the business imperatives, it is important to estimate the value derived from the data integration project. You can estimate the value by asking questions such as:

What is the business goal of this project? Is this relevant? What are the business metrics or key performance indicators associated with this goal? How will the business measure the success of this initiative? How does data accessibility affect the business initiative? Does having access to all of your data improve the business initiative? How does data availability affect the business initiative? Does having data available when its needed improve the business initiative? How does data quality affect the business initiative? Does having good data quality improve the business initiative? Conversely, what is the potential negative impact of having poor data quality on the business initiative? How does data auditability affect the business? Does having an audit trail of your data improve the business initiative from a compliance perspective? How does data security affect the business? Does ensuring secure data improve the business initiative?

After asking the questions above, youll start to be able to equate business value, in a monetary number, with the data integration project. Remember to not only estimate the business value over the first year after implementation, but also over the course of time. Most business cases and associated ROI models factor in expected business value for at least three years. If you are still struggling with estimating business value with the data integration initiative, see the table below that outlines common business value categories and how they relate to various data integration initiatives:

Business Value CategoryINCREASE REVENUE

Explanation

Typical Metrics

Data Integration Examples



63 of 1017

New Customer Acquisition

Lower the costs of acquiring new customers

- cost per new customer acquisition - cost per lead - # new customers acquired/month per sales rep or per office/store

- Marketing analytics - Integration of third party data (from credit bureaus, directory services, salesforce.com, etc.)

Cross-Sell / Up-Sell Increase penetration and sales - % cross-sell rate within existing customers - # products/customer - % share of wallet - customer lifetime value

- Single view of customer across all products, channels - Marketing analytics & customer segmentation - Customer lifetime value analysis - Sales/agent productivity dashboard - Sales & demand analytics - Customer master data integration - Demand chain synchronization - Data sharing across design, development, production and marketing/sales teams - Data sharing with third parties e. g. contract manufacturers, channels, marketing agencies - Cross-geography/cross-channel pricing visibility - Differential pricing analysis and tracking - Promotions effectiveness analysis

Sales and Channel Increase sales productivity, Management and improve visibility into demand

- sales per rep or per employee - close rate - revenue per transaction

New Product / Service Delivery

Accelerate new product/service - # new products launched/year introductions, and improve "hit - new product/service launch time rate" of new offerings - new product/service adoption rate

Pricing / Promotions

Set pricing and promotions to stimulate demand while improving margins

- margins - profitability per segment - cost-per-impression, cost-per-action

LOWER COSTS Supply Chain Management Lower procurement costs, increase supply chain visibility, and improve inventory management - purchasing discounts - inventory turns - quote-to-cash cycle time - demand forecast accuracy - product master data integration - demand analysis - cross-supplier purchasing history - cross-enterprise inventory rollup - scheduling and production synchronization

Production & Service Delivery

Lower the costs to manufacture - production cycle times products and/or deliver services - cost per unit (product) - cost per transaction (service) - straight-through-processing rate Lower distribution costs and improve visibility into distribution chain - distribution costs per unit - average delivery times - delivery date reliability

Logistics & Distribution

- integration with third party logistics management and distribution partners

Invoicing, Collections and Fraud Prevention

Improve invoicing and collections efficiency, and detect/prevent fraud

- # invoicing errors - DSO (days sales outstanding) - % uncollectible - % fraudulent transactions - End-of-quarter days to close - Financial reporting efficiency - Asset utilization rates

- invoicing/collections reconciliation - fraud detection

Financial Management

Streamline financial management and reporting

- Financial data warehouse/ reporting - Financial reconciliation - Asset management/tracking



64 of 1017

MANAGE RISK Compliance Risk(e. Prevent compliance outages to -# negative audit/inspection findings g. SEC/SOX/Basel avoid investigations, penalties, - probability of compliance lapse II/PCI) and negative impact on brand - cost of compliance lapses (fines, recovery costs, lost business) - audit/oversight costs - Financial reporting - Compliance monitoring & reporting

Financial/Asset Risk Management

Improve risk management of key assets, including financial, commodity, energy or capital assets

- errors & omissions - probability of loss - expected loss - safeguard and control costs

- Risk management data warehouse - Reference data integration - Scenario analysis - Corporate performance management - Resiliency and automatic failover/recovery for all data integration processes

Business Reduce downtime and lost Continuity/ business, prevent loss of key Disaster Recovery data, and lower recovery costs Risk

- mean time between failure (MTBF) - mean time to recover (MTTR) - recovery time objective (RTO) - recover point objective (RPO -- data loss)

Step 2 Calculating the CostsNow that you have estimated the monetary business value from the data integration project in Step 1, you will need to calculate the associated costs with that project in Step 2. In most cases, the data integration project is inevitable one way or another the business initiative is going to be accomplished so it is best to compare two alternative cost scenarios. One scenario would be implementing that data integration with tools from Informatica, while the other scenario would be implementing the data integration project without Informaticas toolset. Some examples of benchmarks to support the case for Informatica lowering the total cost of ownership (TCO) on data integration and data quality projects are outlined below:

Benchmarks from Industry Analysts, Consultants, and Authors Forrester Research, "The Total Economic Impact of Deploying Informatica PowerCenter", 2004 The average savings of using a data integration/ETL tool vs. hand coding: 31% in development costs 32% in operations costs 32% in maintenance costs 35% in overall project life-cycle costs

Gartner, "Integration Competency Center: Where Are Companies Today?", 2005 The top-performing third of Integration Competency Centers (ICCs) will save an average of: 30% in data interface development time and costs 20% in maintenance costs



65 of 1017

The top-performing third of ICCs will achieve 25% reuse of integration components

Larry English, Improving Data Warehouse and Business Information Quality, Wiley Computer Publishing, 1999. "The business costs of non-quality data, including irrecoverable costs, rework of products and services, workarounds, and lost and missed revenue may be as high as 10 to 25 percent of revenue or total budget of an organization." "Invalid data values in the typical customer database averages around 15 to 20 percent Actual data errors, even though the values may be valid, may be 25 to 30 percent or more in those same databases." "Large organizations often have data redundantly stored 10 times or more."

Ponemon Institute-- Study of costs incurred by 14 companies that had security breaches affecting between 1,500 to 900,000 consumer records Total costs to recover from a breach averaged $14 million per company, or $140 per lost customer record Direct costs for incremental, out-of-pocket, unbudgeted spending averaged $5 million per company, or $50 per lost customer for o

Documents

Velocity v8 Data Warehousing Methodology