CA2331478A1 - System and method for assisting decision making - Google Patents

Abstract

Most organizations lack the time and resources to build their own integrated warehouse solutions from scratch. The present invention provides enterprise-wide business intelligence out of the box, and is the first and only solution to provide true end-to-end integrated warehousing analysis and reporting in one comprehensive package.

Description

SYSTEM AND METHOD FOR ASSISTING DECISION MAKING
FIELD OF THE INVENTION
S This invention relates to a system and method for assisting decision making, and more particularly to assist making business and organizational decisions.
BACKGROUND OF THE INVENTION
The hyper-competitive e-business economy has redefined relationships between companies and their customers, suppliers, and partners. With competitors but a click away, long-cultivated customer loyalty can evaporate in a keystroke and the emphasis on speed to market and cost containment has transformed suppliers from distant third parties into integral corporate allies.
Successfully managing customer and supplier relationships in this digitally driven environment means successfullymanaging two-way information flows. Organizations-both dot-corns and brick-and-mortar enterprises-have responded by redefining processes and harnessing speed and information, the pass codes to e-business competitive advantage.
These organizations realize that their futures depend on extracting important information from the mounting sources of data around them and then leveraging this information to make better and faster business decisions. Coming to grips with the analysis and reporting limitations of their enterprise resource planning (ERP) systems is an important part of this process, and many organizations are building data warehouses and data marts to optimize data to deliver the business insight they require to compete.

Unlocking the Power of Data Assets In the e-business economy, how quickly and successfully companies manage and apply information dictates how well they accelerate top-line business growth, improve operations, and sustain competitive advantage. Transforming data into meaningful information allows them to see and react to business drivers, propelling them toward achieving their goals.
Where do organizations get this data? Ideally, it comes from back office ERP
systems, front office, and e-business sources. In essence, this data allows companies to accomplish two important obj ectives: First, by capturing information from all customer and supplier touchpoints, organizations can build the foundation for effective CRM (customer relationship management) analytics, and SCM
(supply chain management) analytics. Second, augmenting the data with a robust analysis and reporting infrastructure enables them to measure business performance and improve strategic decision-making.
Armed with a 360° view of their businesses, companies can quickly match cause and effect and improve decision-making. For instance, an inventory manager will be equipped to answer a wealth of business questions: How much money have we invested in stock? How effectively are we managing and forecasting our requirements? How does stock move through our organization? How effectively are we allocating our resources?
Capturing, sharing, and using this type of information strategically equips organizations to compete in the e-business world. They gain the insight to quickly discern what is important and then act decisively before competitors lead customers away or supply chain problems siphon profits from the bottom line.
Business analysis and reporting solutions transform data into information that people can use. It turns volumes of data into key performance indicators, company-wide scorecards, pattern and trend analyses, and status reports. Sharing this information via intranets, extranets, and the Internet with everyone in the e-business value chain-decision makers, employees, customers, suppliers, and partners-allows companies to build competitive advantage and effectively manage customer and supply chain relationships and processes.
For instance, organizations can give customers Web-based 24 X 7 year-round self service access to their accounts, shipping, support, and other related business data. By making it easier for customers to deal with their organizations, companies are much more likely to keep clients coming back.
Likewise, by gaining access to customer inventory, shipping, and scorecard information, suppliers can see how to better serve their clients.
AddressingLthe Limitation of ERP-Based Reporting Systems In the information-savvy e-business world, rapidly deriving business insight from vast amounts of data is key to success. Companies must offer solutions that integrate accurate, current information into a consistent view-an important source of value delivered by business analysis and reporting solutions. This way, everyone works with the same data and business measurements.
Most large organizations use ERP systems to consolidate day-to-day transaction data and streamline business functions such as manufacturing. With their predefined, standard reporting capabilities, however, these ERP systems are not optimized to support the flexible, ad hoc business analysis and reporting businesses need today to make strategic decisions and improve business performance.
Basically, ERP systems are not intended to serve as e-business analysis and reporting infrastructures.
For example, generating a report from an ERP system that shows product line sales by region by sales person for the past five years would typically be quite time-consuming.
With their multitude of tables, fields, and column names, ERP systems are not well suited to end-user navigation.
Without easy information access, and the means to quickly analyze and report on findings, users can easily overlook important business correlations or veer off track completely.
Ultimately, the quality and speed of decision-making suffer.
In addition, if hundreds or thousands of users were to submit queries directly, ERP system performance would be impacted, jeopardizing important production system functions. This, along with the risks associated with giving the extended e-business enterprise direct access to ERP
systems, necessitates placing ERP data into an environment that is not only optimized for business analysis and reporting, but also for secure broad access. Seeking predictable performance and desiring to give users all the information they need quickly, many companies opt to build either data warehouses or data marts.
Enterprise Data Warehouses-The "Big Bang" Approach Created by extracting data from operational or transactional systems (like ERP
sources) and e-commerce systems and installing it in a more analysis- and reporting-friendly database, data warehouses are repositories of data that support management decision making.
However, data warehouses are expensive to build and they can take 18 to 24 months to create-an eternity in Internet time. Consequently, with enterprise information requirements evolving so fast today, data warehouses often fail to meet requirements when they are finally completed. Moreover, they require specialized skills and experience to build successfully.
Because of their sheer scope, data warehouses seldom produce the finely tuned analysis and reporting that e-business decision-making depends upon. Intended to be all things to all people, these warehouses focus on breadth of content, rather than the depth of vital information sweet spots users need.

Data Marts-The Stovepipe Approach Unlike data warehouses that combine and make all corporate data available across an enterprise, data marts focus more narrowly, serving specific business areas or departments.
Data marts also take less time and money to build and can therefore generate quicker payback than data warehouses.
Sound in principle, data mart creation can stumble in practice. While data marts can be built incrementally, they do not provide a holistic view of the enterprise.
Companies will build a data mart for Sales, another for Inventory, another for Finance, and so on. Unless these marts are coordinated, they act as stovepipes and prevent users from sharing information across the enterprise.
They also duplicate data and lead to lengthy updates because each mart must be refreshed individually. If companies update the marts at different times-even just a couple of hours apart-some users will have more current information than others. This lack of synchronization can lead to inconsistent analysis across the enterprise and cause users to question the integrity of the analysis and reporting solution.
For instance, users of one mart might define a "large" customer as one that generates more than $50,000 in revenue a month. Users of another might define a large customer as one that orders more than 100 units a month, which may only represent $10,000. In these cases, people can mistakenly think that they are discussing common ground. Not only may different marts define dimensions differently, they can calculate measures differently as well. For example, one department might compute "profit" by including bad debts and another may exclude them.
These types of inconsistencies not only create mis-understandings, they can delay schedules and increase costs, jeopardizing customer satisfaction and profits.
An enterprise's ability to advance its competitive position will soon be based in large part on how well it is able to achieve and maintain a "360 degree" view of its operational and financial effectiveness, customer relationships, and supply-side activities. Employees will need facts to S

measure and respond to business conditions immediately. Although current technologies are strong enough to address these concerns most of today's proffered solutions demand deep pockets and tons of implementation time-and they often leave the enterprise vulnerable.
Here are some of the areas of exposure. When an enterprise engages a consultant to build an analytic architecture, a knowledge gap can remain when the consultant finishes the project. Shortcuts often worsen matters. Deploying two or more data marts independently using typical methods can help the enterprise labor at "Internet speed." The problem? Different constituencies may not be working with the same data and metrics. Given the potential for quickly proliferating "misinformation"
throughout the enterprise, organizations should be grateful that their expensive analytic environments are often underused.
SUMMARY OF THE INVENTION
The Integrated Data Warehouse-The Ideal Solution An integrated data warehouse solution gives you both the enterprise-wide perspectives that traditional data warehouses offer and the incremental development that data marts provide. It enables you to address the unique business analysis and reporting needs of each functional area-Sales, Finance, Inventory, and so on-while integrating and coordinating these groups by using shared dimensions.
In essence, an integrated data warehouse offers the best of both worlds: the breadth of an enterprise-wide data warehouse and the luxury of incremental data mart implementation.
This structure enables an organization to maximize the return on its ERP, e-commerce, and other source data system investments. Released from the analysis and reporting confines of ERP systems, users can now creatively explore business problems and make equally creative and effective business decisions.

Moreover, users can incrementally add data marts over time, expanding the integrated data warehouse at their own pace. Each new mart fits seamlessly with its predecessors, extending the scope of the data warehouse to produce effective cross-functional business content-the fundamental information users need to understand their business drivers.
For example, if the inventory turnover rate suddenly dropped, users would want to know why. With an integrated data warehouse system comprised of several subj ect-specific marts, users could explore whether the root of the problem lies in Sales or in Inventory, perhaps the result of a change in the company sales compensation plan or a tightening of credit policy. By sharing the same conforming dimensions (for instance, "product") in both the Sales and Inventory marts, users could generate these types of revealing cross-functional views. The result: enterprise-wide decision-making is vastly improved.
Assessing Key Success Factors Creating and implementing a successful integrated data warehouse involves a lengthy series of complex steps and activities, and requires expertise in numerous highly specialized areas.
Despite the substantial hurdles, some IT departments elect to build data warehouses themselves. It is not unusual for these projects to end up over budget, miss major milestones, or even fail due to the unanticipated complexity of extracting, transforming, and loading the right data.
Before attempting to build an integrated data ware-house, IT departments need to fully assess the obstacles and risks involved.
The Data Warehouse Skills Inventory An integrated data warehouse proj ect requires a diverse array of skills and experience. The following six "make or break" skill-sets are important to a successful implementation.

Business Requirements Analyst Acts as liaison between the data warehouse proj ect team and the warehouse's end users. This person identifies and documents the needs of the business and produces a plan for addressing these needs using the data warehouse. The Business Requirements Analyst must have excellent communications skills and an ability to assess business information needs.
Sub,~ect Matter Experts Typically end users who are familiar with the information and business needs of the internal groups or areas that they represent and who have significant knowledge of the data.
These people help standardize on different aspects related to the data and work to resolve issues across business areas.
Source Systems Experts Identifies source fields based on the requirements specified for the warehouse. Also identifies the source hurdles that will need to be overcome in order to implement.
Data Architect The Data Architect develops and maintains the logical and physical data models of the warehouse, and is able to identify the most valuable data, integrate it, and develop the correlating data model.
Also responsible for recommending the optimal system of record, the Data Architect must ensure the company's business needs are incorporated into a technical solution.

Data Acquisition Developer and Architect Responsible for extracting data from a source system, performing associated transformations, and making the data available for loading into the data warehouse. The Data Acquisition Developer and Architect must understand extraction and transformation, identify transformations, and define source-to-target mappings.
Business Intelligence BI) Developer Develops solutions that allow end users to easily and consistently access the data warehouse. The BI Developer must clearly understand the business needs, be able to incorporate these into technical solutions, and be skilled in end-user access, reporting, and analysis tools.
The Steps to Build an Integrated Data Warehouse Assembling the necessary skills and expertise is the first step of many involved in the process of successfully developing an integrated data warehouse.
Process Involved in Building an Integrated Data Warehouse 1. Establishing End-User Needs - Business requirements analysis 2. Data Mart Design - Logical data model - Physical data model 3. Source System Analysis - Source system analysis and mappings 4. Data Mart Creation - Data acquisition process design - Data acquisition construction 5. Target System and Configuration Environment - Technical architecture design 6. Data Mart Operation - Maintenance and administration 7. Business Analysis and Reporting (Business Intelligence) - Data access design - Data access construction Assessing business requirements can take up to 50% of the entire effort of building a warehouse.
Establishing End-User Needs An IT department has to know its users' business requirements from A to Z. How will people use information? What questions do they need answered? Do they want high-level views or transaction details? Will they use this information in their offices or on the road? Only by exploring users' business requirements-and fully understanding how the departments of your enterprise interact-will a user be ready to create the appropriate metrics and business rules an effective analysis and reporting solution requires. Including the content in the warehouse that effectively supports business goals is the key to achieving maximum return on investment.
Data Mart Design Designing data marts involves turning the business needs you have identified into useful data. The process requires designing the data mart logical data model and the subsequent physical data model.
Users will need to answer many questions at this stage: Which end users should be involved during the design sessions? Do data sources exist for some or all of the intended data? Have they chosen an ETL tool? Will the initial design include metadata? If so, will it comprise technical metadata, business metadata, or both?

Once these questions are addressed, to optimize the solution for business analysis and reporting, users need to design a high-speed star schema data marts that logically arranges data and allow for cross- functional views of business operations. Simply put, the star schema data marts, based on relational data, uses shared, conformed dimensions to achieve a unified view of traditional bricks-and-mortar and e-business processes. In effect, a Sales data mart would define "Product X" the same way that the Inventory data mart does. These marts should also be scalable and contain embedded knowledge of the business analysis and reporting applications they will serve.
Source Stem Analysis The next step, source system analysis, needs to be undertaken by someone who is familiar with the user's ERP, e-commerce, and other source systems as well as any modifications that they have made to them. This expertise is necessary to identify which data to extract and how to extract it.
The source system expert needs to understand the unique parameters, fields, hierarchies, and technical approaches that characterize each ERP solution. Many organizations outsource the initial design of their ERP and e-commerce systems to consultants who take their source expertise with them once the contract is completed. This, coupled with the high rate of movement of in-house IT
resources leaves companies with a knowledge gap regarding these complex source systems. The solution is typically to retain consulting expertise, which can become prohibitively costly and, depending on a consultant's availability, even delay the solution delivery date.
Data Mart Creation Once users know where to look for data in the source systems, their next step is to develop source to target mappings and ensure that they extract, transform, and load ERP and other data into their data marts. Poor source data quality, missing source data, and redundant source data, among other challenges, can complicate this process.

Ultimately, the ETL system should flag errors during the ETL process, minimize computing resources, maximize automation, and incorporate best warehousing practices such as slowly changing dimensions, history preservation, and changed-data capture.
Delivering these capabilities will ensure that the process runs as smoothly as possible and that the data generated is accurate.
Users also need to know how to incrementally add data marts. For instance, if a user adds an Inventory mart to their existing Sales and Finance marts, the user must be careful to avoid creating data definition conflicts between the marts. Synchronization and coordination are key because problems at this stage can sabotage data integrity.
Target System and Configuration Environment Is the user using an NT application server to run your ETL code and populating an Oracle database on a Unix platform? Or is the user running their ETL code on Unix and populating a Microsoft SQL
server on NT? Depending on the platform and database, the user will have to vary the way that they install and configure their solution.
Data Mart Operation Tasks associated with operating, managing, and maintaining the integrated data warehouse include loading data marts from operational systems, troubleshooting the system, restarting failed jobs, and scheduling jobs so that they minimize impact on source systems.
Business Analysis and Reporting To derive full value from the business analysis and reporting solution, users must be able to answer in-depth questions such as "Which customers in the western sales region have increased their purchases by more than 30 percent in the past three years?" or "How much revenue did we generate from international sales of Product X last November?" These types of complex queries-involving time, geography, product lines, revenues, and other business variables-require that multiple dimensions and levels of detail be examined.
The business analysis and reporting solution must allow users to make connections between these cross-functional variables, connections that will provide insight into what is driving the business.
Building the Solution From Scratch: The Impact on It Building an integrated data warehouse from scratch requires substantial IT
expertise, not to mention equally substantial time and money. Fortunately, IT departments have an option that puts robust decision-making solutions in the hands of users quickly and cost-effectively.
This invention offers an out-of the-box integrated analytic solution called e-Applications that allows IT departments to provide users with high quality cross-functional business analysis and reporting in only a matter of weeks, freeing up specialized IT resources for immediate impact.
Out-of the-box e-Applications save users a complete business cycle in deploying and extending their integrated data warehouse solution. (See Figure 1).
BRIEF DESCRIPTIONS OF THE DRAWINGS
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
Figure 1 is a diagram showing an example of results of timing.
Figure 2 is a diagram showing an example of a holistic view of an enterprise.
Figure 3 is a diagram showing an example of a screen-shot of the Financial Analysis e-Application.
Figure 4 is a diagram showing an example of a screen-shot of the Inventory Analysis Suite of e-Application.
Figure 5 is a diagram showing an example of a screen-shot of the Sales Analysis e-Application.

Figure 6 is a diagram showing an example of interrelations of tables.
Figure 7 is a diagram showing an example of Sales Analysis Schema.
Figure 8 is a diagram showing an example of Inventory Analysis Suite Schema.
Figure 9 is a diagram showing an example of Financial Analysis Schema.
S Figure 10 is a diagram showing an example of Slowly Changing Dimensions.
Figure 11 is a diagram showing an example of Changed-Data Capture.
Figure 12 is a diagram showing an example of a screen-shot of the e-Applications console.
Figure 13 is a diagram showing another example of a screen-shot of the e-Applicaitons console.
Figure 14 is a diagram depicting an example of the e-Application.
Figure 15a to Figure 15y show an example of a data model.
DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
This invention relates to the challenges that organizations face when implementing data warehouses and traditional "stove pipe" data marts. It provides a solution-the integrated data warehouse-which comprises a series of coordinated data marts. These coordinated data marts allow companies to deliver value-laden enterprise-wide business analysis and reporting solutions that are important to competitive advantage in the e-business economy.
This invention also relates to building an integrated data warehouse from scratch, and describes a packaged solution. This invention comprises of complete end-to-end analytic applications for business analysis and reporting that include defined extractions and data models, proven business content, and best practices displayed through captured business metrics, and a full suite of key performance indicators (KPIs), reports, and analyses.
Built upon Cognos' unique operational framework and robust production environment, Cognos e-Applications help decision-makers rapidly derive business value from their enterprise data. By using Cognos e-Applications, organizations receive a complete, cross-functional view of their ERP and e-business data, which provides a strategic perspective on KPIs. And they dramatically reduce implementation costs and effort, which accelerates time to results.
e-Applications An Out-of the-Box Integrated Data Warehousing Solution By packaging e-Applications into a series of departmentally specific, coordinated analytic applications, this invention enables users to build an enterprise view of their organization incrementally and strategically. The benefits? Users promptly derive the business insight they need from the packaged reports and analyses provided, and IT departments escape the high-labor, high-cost, high-risk of many data ware-house and data mart practices, freeing time to refocus on other priorities.
Co~nos e-Applications Profile - Business-driven extractions and source-to-target mappings incorporate business rules that unravel major ERP systems such as SAP R/3, Oracle Applications, and J.D. Edwards, and are open to alternative sources.
- Optimized for reporting, pre-defined star schema data models and packaged reports and analyses reflect the analytical requirements for important areas such as Sales, Finance, and Inventory.
- The e-Applications Console provides intelligent ETL job control for ad hoc or scheduled data loads, sequences extraction jobs, defines extract dates, includes stop-recover strategy, and handles exceptions during data mart updates. It lets you set configuration parameters so that the data warehouse reflects ERP site-specific configurations.
- By implementing shared dimensions that can be used across numerous data marts-and implementing them only once-IT reduces its workload, and provides consistent data and dimensions throughout the enterprise and cross-functional business reporting and analysis for end users.
- Packaged and fully customizable business reports and multidimensional data models reflect the information and KPIs needed to manage, measure, and improve business performance in each functional area.
With e-Applications, users can build an enterprise view of their organization.
The outcome: users quickly derive the business insight they need from packaged reports and analyses provided, and IT
departments are freed from the high labor and high costs associated with many data warehouse and data mart initiatives. (See Figure 2).
Examples of e-Applications e-Applications contain a number of packaged reports that reflect the business requirements for important areas such as Finance, Sales, and Inventory.
Financial Analysis e-Application (See Figure 3) speeds reconciliations, period-end closings, and financial reporting and distribution by giving managers the information they need to analyze income statements, balance sheets, cash flows, key financial ratios, or currency rate conversions.
Types of financial reports available to end users include:
- Overview reports, such as income statement and balance sheet - Income statement analysis - Balance sheet analysis - Budget analysis - Analysis by legal entity - Analysis by management entity - Operational reports, such as cost center and general ledger analysis Sales Analysis e-Application (See Figure 5) allows users to analyze forecast accuracy and sales volume, calculate average deal size, and examine revenues and profitability, and so on.
Types of sales reports available to end users include:
- Reports by customer, such as customer sales ranking or customer sales by region - Reports by product, such as order summary, or product sales ranking - Reports by sales organization, such as orders by reps or by country - Reports by profit - Reports by quantity sold Inventory Analysis Suite of e-Applications (See Figure 4) provides inventory managers with the information they need to understand supply chains and assess demand forecasting accuracy, inventory carrying costs, supplier performance, and warehouse performance, and so on.
1 S Types of inventory reports available to end users include:
- Inventory performance, such as stock level overview or profile of plants by stock level - Demand analysis, such as stock usage comparisons, or materials profile of demand - Material tracking - Vendor analysis by stock movements - Resource activity, such as activity comparisons or plant/employee analysis Establishing_Business Content The heart of e-Applications lies in the quality of its business content. It is the business content that gives end users the ability to answer complicated questions involving numerous business dimensions and immediately gain the insight required to make strategic decisions. The basis of this content combines Cognos' well-established business intelligence expertise and proven best practices-strategies which have helped many of the world's leading companies generate maximum decision-making value from their data.

To ensure that e-Applications are business-ready out of the box, Cognos gathered comprehensive information about the business questions that users in specific functional areas face today. And Cognos extensively interviewed clients, professional associations, industry consultants, analysts, and subject matter experts-people who understand the challenges and the opportunities prevalent in each functional area.
From the research, Cognos identified hundreds of function-specific questions common to business people in virtually all industries. In other words, someone who manages a sales force for a pharmaceutical company will face many of the same business challenges as someone who manages a sales force at a textile company or a semiconductor company. After assembling and validating these questions, Cognos deconstructed each into business measures, dimensions, and attributes-the building blocks of a star schema data mart. Cognos also established business rules that govern how to derive measures such as "net profit margin" or "inventory balances"-measures that do not appear in ERP systems and must be created.
Cognos also explored the processes underlying each e-Application and determined how companies managed their workflows within each functional area. Cognos categorized questions as strategic, tactical, or operational and then established the information needs associated with each category.
What level of data granularity do users require? How much history do they need? Five years?
Three years? How often do they need to refresh data? Do they have to know what happened yesterday to answer a given business question or can they wait until the end of the week?
Tailored to End-Users A key aspect of establishing business content involved determining how to present information to different types of users. For instance, sales people who spend considerable time on the road with their laptops need OLAP cubes that will give them sales history information about their clients and others and allow them to compare performance and purchasing patterns. Senior executives, on the other hand, need high-level, visual perspectives of corporate performance.
Understanding these distinctions permitted us to build a solution that delivers business analysis and reporting value to users across the enterprise.
Using an iterative design-and-build process, Cognos tested the solutions in joint application development sessions, workshops, and client beta projects, continually refining the e-Applications until they delivered optimum value. Ultimately, Cognos devoted 50 per cent of development effort to collecting and refining the business content and ensuring that e-Applications give users the analysis and reporting capabilities they need to excel.
Building Coordinated Data Marts Using best warehousing practices to construct the e-Applications data marts, Cognos e-Applications are designed for the enterprise and deployable by department, an approach that delivers value to end users and achieves enterprise-wide decision-making cohesion as quickly as possible. Cognos created e- Applications to serve as the information backbone of an organization. By building this backbone section by section and tying functional areas together with common shared dimensions, organizations generate a powerful decision-making support infrastructure that can grow as they grow.
Common Dimensions Traditional stovepipe data marts may serve certain departmental decision-making needs, but they fail to offer all-important enterprise-wide views. By incorporating common dimensions, e-Applications allow knowledge workers to share information across departments and gain important decision-making synergies. Based on common terms and common information, common dimensions ensure that users in all departments or functional areas approach business issues using the same references.
Each e-Application has been designed from careful consideration of the business dimensions or measures that are common to each functional area of the business. Based on common terms and common information, these dimensions ensure that users in all departments approach business issues using the same references.
Each e-Application has been designed from careful consideration of the business dimensions or measures that are common to each functional area of the business. Based on common terms and common information, these dimensions ensure that users in all departments approach business issues using the same references. (See Figure 6).
For example, the dimension "customer" would mean precisely the same thing to a sales manager as it would to an inventory warehouse manager or a finance vice president.
Without conforming dimensions, each department would likely develop different definitions, hierarchies, terms, and dimensions for many of the same business measures, an inefficiency that can sidetrack productivity and hamper decision-making.
Incorporating common dimensions means IT builds the tables only once, and less redundancy because data is stored once, and shorter time to update because data is loaded once. Moreover, multiple star schemas can leverage the shared dimensions to reduce update time and resources.
Updates occur once, not five times, which speeds the update process. In addition, common dimensions save disk space, reduce redundancy, and ensure that data is consistent from one mart to the next.
Integration and Extendibility Building a data mart from scratch requires evaluating every component of the end-to-end solution-the extraction process, transformation process, data models, data marts, multidimensional components, and user reports-and then integrating them into a high-performance analysis and reporting system based on your specific ERP system. With e-Applications, this evaluation and integration work is done for a user. All the pieces work together out of the box, making lengthy evaluations unnecessary.

In addition, although e- Applications come ready to deliver instant analysis and reporting value, users have the option to extend the application to suit their specific needs.
Perhaps users want to add additional source system data, incorporate external data, rework the packaged reports, or incorporate special graphics. Cognos offers them this flexibility and enables them to integrate these extensions with other e-Applications. By making provisions for the unique way that a company operates, users can augment the analysis and reporting value that the solution offers.
As well, the e-Applications Console, a production control environment, can integrate and manage all the extensions. This capacity saves users from dealing with extensions on their own; users can easily tie them into the e-Applications Console and gain the same production control and management benefits that accrue to the out-of the-box applications themselves.
In addition, users can deploy e-Applications as required-on the Web, in a client/server configuration- whatever is most effective for their environment. And by taking advantage of Cognos' sophisticated and market-leading OLAP tools and visual reports, organizations will be well equipped to meet the information needs of all their users.
Data Granularitv To solve a business problem, sometimes decision-makers want to see transaction details, not just higher level summaries. For this reason, e-Applications, which contain both relational and OLAP
data, extract the most granular data from the source ERP systems and use it to populate the data marts. Decision-makers can therefore easily access transaction-level detail and gain a micro view of the business issues at hand.
Offering detailed granularity takes pressure off the source ERP system as well. Rather than query the production system every time they need to perform detailed analysis, decision makers simply query the e-Applications and glean the insight they want.

Star Schema Desi ng-SSpeeds Queries e-Application data marts perform business analysis and reporting much faster than ERP systems, which distribute data fields among thousands of tables. Finding the fields that describe a given query in an ERP system often requires joining copious tables, a time-consuming step that slows analysis and drains database processing power. Optimized for high-speed analysis and reporting, e-Applications incorporate a star schema architecture that accelerates query performance and produces fast business insight.
The Star Schema Star schema architectures contain two types of tables-fact tables and dimension tables. A fact table comprises the transaction history associated with each activity being modeled.
These fact tables store the numerical measurements of the business and include an >D field for each dimension that they represent. For instance, a Sales fact table might include fields for Customer m, Sales-person >D, Product >D, Quantity Sold, Discount, and Total Amount, etc. The fact table is linked to several dimension tables that qualitatively describe the fact table fields in more detail. For instance, the Salesperson >I7 dimension table might include Salesperson ID, Salesperson Name, Phone Number, Sales Office, and Employee Number, and so on.
This star structure, with the fact tables surrounded by satellite dimension tables, allows users to drill down quickly into the data to uncover cor-relations between dimensions and elements in the fact table. Forming queries involves a set of simple one-way joins, from the fact table to each dimension, rather than complex multi-step joins through multiple levels of tables. Users get the in-formation they need quickly, allowing them to solve business problems, spot trends, or act on opportunities.
e-Applications evaluate every component of the end-to-end solution you need and translate this information into data models that reflect the analysis and reporting capabilities you need based on the business information needs. (See Figures 7, 9 and 8).

Data AccLuisition A very complex part of building a data mart involves extracting the right data from the source system, transforming it into the desired form, and loading it into the data marts. To facilitate and expedite this process, Cognos built a repository for the e-Applications ETL
tool that both understands the source ERP system and the targets. This repository uses business rules to transform data from the ERP system to the targets.
e-Applications greatly simplify the complex process of extracting data from specific source systems such as J.D. Edwards, SAP R/3, and Oracle. Overcoming the technical hurdles and addressing the unique characteristics involved in each system, e-Applications identify the reporting data needed and supply the methods to extract it.
Extraction To extract data properly requires in-depth knowledge about the underlying source system.
Developers need to know where the relevant data comes from and what the specific data structures look like. They also need to know about the technical hurdles specific to their source systems.
Using its extensive experience with SAP, Oracle, and J.D. Edwards ERP systems, Cognos identified the relevant functional reporting data in each and developed methodologies to extract this data. For example, Cognos knows that SAP uses pooled and clustered table structures, that Oracle provides "flex" fields, and that J.D. Edwards maintains address books in a special way.
Each system contains unique characteristics that affect data mart building. e-Applications addresses them all. This inherent source system intelligence spares users from having to spend potentially hundreds of hours analyzing complex ERP and e-business systems.
1n addition to speeding the extraction process, the ETL tool incorporates safeguards to protect data integrity. As data comes across from the source system, the ETL tool looks for specific conditions.

If these conditions are absent, the tool generates an error log and lists the missing data, simplifying system administration and trouble-shooting.
Transformation Missing data, incomplete data, or inaccurate data can degrade the quality of a business analysis and reporting solution and substantially hinder the business results.
To generate consistently high data quality, the e-Applications ETL tool contains transformation functions that format and integrate source data before it is stored in the data mart. This process might involve any number of functions: restructuring data files, records, and fields; removing superfluous data; decoding and translating field values to enhance data;
improving data readability;
validating data; calculating new values from one or more source columns;
simplifying data; and changing data types. The transformation process will also reject records that don't satisfy business rules.
Once set up, the ETL process can run automatically according to the desired schedule. As part of the transformation process, e-Applications employ surrogate keys-that substitute for natural keys-to improve processing performance and reduce the volume of data required to describe a particular data element. For instance, e-Applications save space by converting text to integers and by generating composite keys, which combine several keys into one.
Data Loading Once the source data has been transformed, e-Applications load it into the destination data marts and make the data available to users for analysis and reporting.
e-Applications apply different updating rules to different tables depending on the nature of the component data. By tailoring the data-loading process to the data, e-Applications update information faster with less demand on the source system. For instance, tables defined as "static" contain data that changes infrequently and therefore needs refreshing only on an ad hoc basis. Tables that require more frequent refreshing can be treated differently as well, according to the characteristics of their data. Users can do a complete refresh, a changed-data capture, or a slowly changing dimension.
S
e-Applications also include stop-recover strategy, which allows extraction jobs that have been interrupted to be restarted. This feature saves administrators time and helps ensure data integrity.
Slowly Changing Dimensions and History Preservation To ensure that an integrated data warehouse accurately captures changes to dimensions that vary infrequently-product hierarchies, sales regions, and so on-e-Applications accommodate slowly changing dimensions.
This feature is one example of Cognos' best ware-housing practices, and offers two primary bene-fits. First, it allows users to go back and find out what was going on at a point in corporate history.
In other words, although employees may have moved or sales terntories may have been redrawn, the system will accurately present information about these slowly changing dimensions as they existed at the time of interest. This allows users to derive consistent, repeatable results, solidifying the value of their decision support system by preserving history.
Second, users can see all values or changes over time. This capability furnishes the insight to uncover longer-term trends and business impacts. If users have incomplete historical information, they can end up making improper assumptions and compromising the quality of their decisions.
Whereas ERP systems typically archive all but the most recent year or two's worth of data without access to supporting details, e-Applications allow users to dig into an issue's past several years or more to gain revealing perspectives about its present. This trend-analysis capability is an e-business imperative, allowing companies to track the impact of decisions over time.

An Example of Slowly Chan~in~-Dimensions If a sales person transfers to a different region in mid year, the e-Applications data marts will allow an organization to record the move and reflect the change in the database.
Without record of this slowly changing dimension, a year-end revenue summaryby region would allocate their entire year's sales to the new regional manager, overstating their accomplishments and understating the previous manager's performance. Companies that make decisions based on this type of misleading information can end up making incorrect assumptions and that can result in costly mistakes.
With slowly changing dimensions, the revenue that the sales person generated before their departure will properly accrue to the previous regional sales manager, and the revenue that they generate after the move will be credited to the new manager. Over time, certain dimensions-employees, products, and customers-will change, and e-Applications, by creating another dimension record, have the flexibility to accommodate these changes and produce an accurate view of business performance.
e-Applications handle slowly changing dimensions so that the integrated data warehouse accurately captures infrequent but important data changes. So users can rely on the data's integrity at all times.
(See Figure 10).
Changed-Data Capture e-Applications also include changed-data capture, the capacity to periodically update the data marts with current information without rebuilding them from the ground up. Another best warehousing practice, changed-data capture detects new, modified, or deleted records in source systems and up-dates the e-Application data marts with those changes.

An Approach to Changed-Data Capture To vastly improve updating speed, e-Applications split the changed-data capture function into two.
One inserts new data incrementally in bulk, a quick and efficient approach that eases the pressure on processing resources. The other step updates changes to existing data, a process that involves going into the database, finding the modified row, updating it, and then saving the change. Given that changes are less voluminous than new data, e-Applications handle the maj ority of updating with the more efficient and speedier process. Updating can therefore be conducted successfully even in the face of continually shrinking update windows.
To further its efficiency, e-Applications look only at the data that has changed in the ERP system.
Recognizing the date and time of the last update, the e-Applications ETL tool requests only records from that update forward. Asking what records have changed and determining whether the changed records are of interest filters this subset further. This approach demands far fewer CPU resources than would be required to extract all the ERP data, to compare it to the data mart, and to load the difference-an unwieldy process that would involve examining every row in the ERP system.
Consequently, changed-data capture improves system performance and speeds updates.
Changed-data capture allows users to periodically update data marts without reloading them from scratch. (See Figure 11 ).
The Command Center: The e-Applications Console The engine behind e-Applications resides within the e-Applications Console, an easy-to-use production control environment that simplifies the up front installation, configuration, and loading of e-Applications. It also makes maintaining the marts easier once they are up and running.

Administrators can use the Console to set extraction sequences, and establish dependencies and priorities. It also enables organizations to implement co-ordinated analytic applications incrementally and manage them centrally.
The e-Applications console manages ETL processes automatically. (See Figure 12).
Simplifying Confi ra anon The e-Applications Console employs easy-to-use configuration parameters to help users tailor e-Applications to their environment.
If the user's company is like most, the user likely customized their SAP, Oracle, or J.D. Edwards source system. If so, their hierarchies, hierarchy types, status codes, charts of accounts, exchange rates types, and other fields may differ from the source system defaults. The e-Applications Console has parameters which help users configure your e-Applications solution to reflect these changes.
This out-of the-box convenience saves a user effort, speeds configuration, and delivers business analysis and reporting value that much faster.
The e-Applications console enables users to augment the e-Application to reflect their particular implementation of the ERP system through configuration parameters. (See Figure 13) The e-Applications Console guides users through adding new e-Applications and ensures that the new ones are synchronized properly. As well, the e-Applications Console matches the configuration to the user's target database and equipment. Whether the user uses Oracle RBDMS or Microsoft SQL Server on NT or Unix platforms, the e-Applications Console will tailor its implementation to the user's physical environment.

Enhanced Production Control The e-Applications Console enables users to import historical ERP data at a pace convenient to their business. This initial load job can take a long time, a potential problem if users attempt to import all this data during a single extended window. Using the e-Applications Console, however, users can schedule the loading to occur in phases-which users set- and populate the marts during slow network activity periods. This convenience avoids saddling users with degraded network performance while the loading occurs.
Users can also use the e-Applications Console to simplify the ongoing extraction, transformation, and loading processes. It will help users sequence jobs and determine which are to run, what data they are to extract, and when they are to run (i.e. date ranges).
The Console will also enable users to run ad hoc jobs or put scheduled jobs on hold.
Moreover, the e-Applications Console equips users to maintain their system in top form.
Administrative tables within the e-Applications relational database store information pertaining to the system's operation. The e-Applications Console uses this information to generate job status reports and error reports, giving users a firm handle on their system at all times.
Administrative Reporting In addition to the functionality afforded by the e-Applications console, e-Applications administrative reporting lets IT:
Track extraction history, including:
- tables - start & end date, and elapsed time - extraction from & to date - row counts - errors Track errors, including:
- extraction object - error count - error type - severity - date - time Business-Ready e-Applications Not only do e-Applications capture the right business content, they come with packaged reports, OLAP cubes, and catalogues that offer out-of the-box business insight. Using Cognos business intelligence, users can also generate an array of reports-OLAP, relational, standard, ad hoc, time trend-to meet all information requirements, for all positions in the organization. Moreover, these reports are also easy to change. Decision makers can easily adapt them to manage, measure, and improve business performance in their functional areas, greatly reducing the burden on IT. Either way, knowledge workers gain key business insight and derive immediate productivity gains.
Furthermore, e-Applications, which can be extended to include scorecarding and visualizations, provide the right report for the right users on the client platform of choice:
Windows, Excel, or Web browser, whether users are LAN-based or working remotely.
Scalability e-Applications are also designed for maximum scalability. Users can add new functional area data marts to further enhance their enterprise analysis and reporting. Users can broaden the source data collection points beyond their ERP system to gain a more complete view of the user's enterprise and customer relationships. Organizations can also increase the number of users that the system supports, accommodating corporate expansion without the growing pains.
e-Applications: An Applications Framework for Maintaining an Analytical View of the Entire Enterprise Using business-ready, packaged analytical and reporting environments, called e-Applications (Figure 14), Cognos already has woven sales finance, and inventory applications into an enterprise business backbone. The e-Applications combine the best principles of business and "data warehousing" in an operational framework that is aimed at giving business users the wherewithal to answer pressing questions while allowing technologists to escape the high-labor, low-yield cycle of many data warehouse and data mart practices. Cognos has delivered the ability to quickly implement and centrally manage data marts that have common metrics and conforming dimensions, and thereby help enterprises to "snap in" Cognos' current and emerging applications and use them as piece parts to create other advanced applications. With e-Applications, Cognos has become a one-stop supplier that has delivered a method that can keep the entire enterprise and extended-enterprise team working in concert.
Cognos has designed these applications to draw and transform data from a variety of sources; the company pays particular attention to easing the burden of working with SAP, Oracle Applications, and J.D. Edwards Enterprise Resource Planning (ERP) systems. Cognos officials say that applications for other operational and e-Business sources will soon follow, including an e-Commerce Analysis e-Application (currently in development).
The system delivers packaged, extensible applications that will enable enterprises to maintain a 360 degree analytical view of the business, including operations, finance, customer relations, and supply side activities. Enterprises and value added resellers (VARs) can extend these applications in numerous ways using the Cognos e-Application Development Kit (ADK) to evolve an ever-expanding view. Using common dimensions (such as customer and location) and a shared Operational Framework as the primary integration points between applications, an enterprise can create a crossfunctional view of the business as well as underpin additional Cognos analytic applications.
The strategy is well matched to current and emerging e-Business pain-points.
Enterprises today must compete on the traditional-but changing-business front while trying to add new e-Business differentiators. Moreover, many of these enterprises are in the midst of buying or creating an increasing number of back and front-office applications, not only to run the business but also to capture the organization's data assets. Enterprises want to tap the information within all of these systems-to get a clearer understanding of the entire business and for making better decisions.
Enterprises also are now grappling with understanding how "getting closer to customers" affects the supply chain, and they can expect the continual introduction of other requirements. Cognos shows every indication that it can keep its customers ahead of the e-Business demand curve with powerful applications that reduce technologists' implementation and maintenance burden and the time to success for corporate and departmental decision-makers.
Thinkin~out of the Box Using its multidimensional analysis software, PowerPlay, and its query tool, Impromptu, in conjunction with relational schemes and online analytical processing (OLAP) models, Cognos has created out-of the-box data mart applications. These applications contain defined extractions, data models, and proven (best practices) business content. Built upon Cognos' unique Operational Framework and robust production environment, e-Applications display business content through captured business metrics, as well as a full suite of key performance indicators (KPIs), reports, and analyses. Other viewing options include scorecard and visualization techniques. The numerous reports included with the e-Applications can sene as a departure point for in-house customization.
Enterprises also can extend the system's underlying data structure and multidimensional cubes.

Of particular note is the Operational Framework, which runs on Windows NT and major Unix platforms and is designed to combine ease-of use and technical rigor. Creating a production control environment, the framework functions as a hub for administrators to extend Cognos' data models, data transformations, and KPIs; it also schedules updates to either relational or multidimensional data.
With the framework, administrators can schedule extractions and cube creation and dictate dependencies and priorities. The framework supports best warehousing practices, including changed data capture, a technique that allows the enterprise to refresh the analytical structure with discrete changes-rather than forcing the enterprise into a time-consuming, batch-oriented refreshment cycle.
The framework also supports slowly changing dimensions, a common occurrence in decision support because many systems track and analyze history. Dimensions such as sales regions shift across time;
if handled incorrectly, the changes will produce analytic inaccuracies. Slowly changing dimensions can also undermine performance-related aggregation strategies. The framework also permits the enterprise to adopt a strategy of building data marts (whether based on a relational or multidimensional database model) independently and managing them centrally. It is the enterprise, not the tool, that dictates the strategy.
This infrastructure flexibility should not be underestimated; it reduces time and considerable expense in both implementation and administration. Enterprises deliberating over the build-versus-buy issue for data marts finally have a very persuasive technological/business argument for buying a packaged application. Moreover, Cognos underscores its commitment to flexibility on the distribution side, as well; it permits enterprises to reach employees, partners, customers, and suppliers via client-saner and across the Internet, intranets, and extranets.
Where e-Applications Will Be Most Useful Numerous factors are driving enterprises of all sizes to implement various types of analysis and reporting systems. From a "macro perspective," Cognos' e-Applications and their Operational Framework will be found valuable at enterprises charged with:
- Reducing the guesswork needed to understand, attract, manage, and keep best customers;
- Attributing costs of revenue-enhancing initiatives across products, services, customers, and assets;
- Engaging with partners on the extranet at an analytically enhanced speed and level; and - Increasing the speed at which the enterprise is able to meet these challenges via analytic applications.
Cognos e-Applications provide options for organizations to implement analytical solutions for individual departments, as an incremental approach to an enterprise solution, or as an integrated whole.
The ability to garner consistency across the applications is particularly important at this time because many enterprises now realize that they need to grasp the business implications contained within third-party applications such as ERP and Sales Force Automation (SFA).
For organizations facing these challenges, e-Applications introduce two immediate benefits. First, many operations managers acknowledge that they need something akin to a multidimensional view of information from their ERP and e-Business systems-but admit they are not quite certain where to begin. e-Applications contain this information. Second, e-Applications also contain a very clear method for moving forward to complement an enterprise's existing operational systems. Using the same methods, the enterprise can add functionality incrementally to extend earlier e-Applications.

From another perspective, many enterprises have built mufti-million-dollar data warehouses, but these online repositories of historical information typically supply information to only a relatively few members of the enterprise. Cognos e-Applications provide a complementary solution to existing data warehousing projects. With its non-intrusive environment, Cognos can increase the population of business users benefiting from the data warehouse investment and increase the value of that investment. As an extension to a data warehouse, e-Applications will protect the integrity of the data warehouse for its specialist users.
Cognos e-Applications eliminate the many synchronization, consistency, and misinformation problems that have plagued enterprises trying to build data marts independently across various business units. Moreover, e-Applications combat "scope creep," a common and expensive problem when an enterprise tries to use a methodology to understand business requirements and map these needs to a data warehouse or data mart. If done poorly, the enterprise frequently has to revisit "the solution" and consequently takes more time and pays more money than anticipated.
Equally important, e-Applications eliminate the need to evaluate numerous technology piece-parts, substituting instead software parts that Cognos, a trusted supplier, has "calibrated" to work together.
Thus, e-Applications help eliminate the lengthy evaluation and testing cycles for many products and provide a single vendor for support and interaction.
In addition to ensuring that all of the technology components work together, Cognos has taken many steps out of the development process for designing and building an analytical application/data mart/warehouse. Rather than focusing on early development steps such as data modeling and source-to-target mappings, the enterprise or business unit can concentrate on strategic requirements.
These difficulties will become more pronounced as e-Business quickens the pace at which enterprises fail or succeed. With traditional needs-assessment methodologies, the business may evolve before the project reaches completion. Preparation time can often exceed the life span (and value) of the application. 1n this context, one of e-Applications' benefits is the ability to incorporate changes to the analytical infrastructure as the business changes. In other words, Cognos is delivering the ability to get rolling quickly-similar to changing the tires of a car while it is moving.
An Anal~rtical Framework To qualify its offering as an analytical application, not just a tool with an applications veneer, a supplier should aim to reduce the enterprise experience of complexity without diluting the power of the solution. Analytical applications should deliver the majority of the following requirements:
- Vertical or horizontal focus via business logic, calculations, models, and transformations, etc.;
- Extensibility, allowing the enterprise to add, for example, its own logic to increase the value of the application;
1 S - Internet support, for both analysis (where appropriate) and distribution;
- Upgradeability, placing the burden on the supplier for making sure that the customer is never forced to change applications to take advantage of new functionality; and - KPIs that distill complex information into a simple metric.
Although Cognos clearly satisfies all of the above conditions and has aimed e-Applications at delivering ease-of use in the context of business users, it has one other differentiator in the analytical applications space. Cognos supplies an administrator cockpit, called the e-Applications Console, which simplifies the administration and maintenance of numerous processes.
These processes include managing tasks that are normally the province of extraction, transformation, and loading (ETL) environments found in the best, generally data-driven, data mart and data warehouse environments.

Development Environment Although the e-Applications for sales, finance, and inventory applications will dramatically speed up implementation, enterprises will always have unique blends of source data that the applications must absorb. The e-Applications contain the extractions and source-to-target mappings for SAP, Oracle, and J.D. Edwards ERP systems. The pre-configured ETL transformations alone will spare the enterprise from having to conduct in-depth source system analysis of complex ERP and e-Business systems. Using codeless ETL processes, enterprise can extend these extractions to other ERP data and external or non-ERP data.
Moreover, e-Applications leverage Cognos data mart creation technology, a source and target-agnostic environment that translates combinations of source data into optimized dimensional structures. Administrators can prompt the tool to build star schema (a multidimensional model in a relational database), Microsoft SQL Server OLAP Services-based dimensional models, or PowerPlay PowerCubes- Cognos' OLAP cubes.
Cognos has integrated e-Applications with many of its other BI technologies, thus increasing the value of the applications by extending access to them. Because e-Applications contain PowerCubes, business users will be able to leverage predefined and customized business analysis and reports that are based on Cognos BI software. The tight integration between the Cognos tools allows business users to examine summary-level data in PowerPlay and drill for the details in relational data.
The e-Applications support the major relational database management systems (RDBMSs), ODBC
sources, fixed record files, and text files as sources-and Oracle and SQL
Server as targets.
Beyond ETL: Maintaining Quality Analytically oriented production environments require precision scrutiny as a way of guaranteeing that decision-makers are guiding the enterprise with accurate and current information. The automatic administration functions of the e-Applications Console watch the system for errors, validate restarts and job completions, and track the history of every job. Unlike many other ETL/management environments, the e-Applications Console gives the enterprise an exceptionally detailed level of control. For example, many environments recognize whether or not a specific data refreshment has S completed, but few allow the enterprise to forgo the rerunning of an update with the expectation that the system will intelligently "catch up" on its next scheduled run.
In addition to many bread-and-butter ETL functions, the e-Applications identify what tables must be up-dated and the sequence in which the system should perform the updates.
The e-Applications also identify which tables are static-i.e., do not need to change; which need complete refreshing;
and which should be updated incrementally. As noted previously, the automatic handling of slowly changing dimensions-e.g., alterations to the sales organization-can spare enterprises from analytic inaccuracy.
e-Applications Sales Analysis e-Application The Sales Analysis e-Application is designed to help executives, senior managers, line managers, and operational personnel assess the organization's sales performance, as well as examine the deeply related issues of customer trends and satisfaction, product performance, and organization effectiveness. The software, for example, can help the enterprise discern the characteristics of success by locating customers that have the highest margin contributions and pinning down their pre-ferred distribution channel. As such, the Sales Analysis e-Application can serve as the linchpin of Customer Relationship Management (CRM) applications. From the perspective of product sales performance, the system can zero-in on purchase quantity details, and it can help with supplier comparisons.

The Sales Analysis e-Application can also help to measure and maximize the use of various channels to reduce costs. Business users can drill into the details of various channels, and they can examine performance drivers such as seasonality and quantities.
A sampler of the KPIs included in the Sales Analysis e-Application includes the size of the customer base and changes affecting it over time; average revenue per customer; and customers grouped according to their revenue, volume, and margin contribution. Customer satisfaction Is target return patterns by region, across time, correlated with on-time delivery.
Financial Anal, sib s e-Application A predominate characteristic of e-Business is that it places organizations under increased pressure to grasp the financial implications of each business practice. Because these businesses face thinning margins, they should be able to understand the true cost of interacting with their customers, not only 1 S on a single channel but also on all channels. Moreover, they should also factor expenses across the supply chain and through the distribution channel.
From a tactical perspective, Financial Analysis e-Application shortens the reconciliation cycle and helps overcome one of the most serious difficulties in most organizations: the prompt close. In addition to speeding up the reporting and distribution cycle, the software is aimed at deep examinations of what might be called seven strategic keys to an organization's financial health:
Income Statement, Profit, Balance Sheet, Financial Ratios, Budget Reviews, Business Unit Per-formance, and Overview Reporting. Given the nature of the Financial Analysis e-Application, it contains KPIs aimed at these areas of strategic measurement.
While space prohibits a detailed exploration of the entire Financial Analysis e-Application, the Income Statement can serve as an exemplar. Devised to help examine trends and time comparisons as well as budget variances, the software can help trace shifts in cost structures at an operational level over time. Moreover, it can measure performance against plan by division, geography, and business unit.
Inventory Analysis Suite e-Applications With the emergence of e-Business as a driving competitive force, organizations should optimize their understanding of the deep relationship among customer relationships, financial health, and the supply chain. Toward that goal, the Inventory Analysis Suite e-Applications analyze warehouse performance, material movement, material classes, physical inventory, and forecasts to actuals.
These supply chain measures and the detail behind them contribute to customer satisfaction by, for example, helping the enterprise meet demand; and they contribute to cash flow optimization via a thorough grasp of inventory investments.
The Inventory Analysis Suite e-Applications can answer many detailed questions, including the average corporate investment in stock, broken down by location and compared to previous periods.
EIs include overviews of stock levels, valuations, and fluctuations; stock movements; and stock coverage

Claims (18)

1. A data model for representing an organization having a plurality of groups of functions, the data model comprising:
a plurality of preset groups of tables, each group of tables representing each group of functions; and preset joins connecting the tables indicating interrelation of the tables to represent the relationship among the functions.

2. The data model as claimed in claim 1, wherein each group of tables having one or more tables, each table describing one or more preset attributes of the respective function.

3. The data model as claimed in claim 2, wherein the present joins are provided based on the attributes.

4. The data model as claimed in claim 1, wherein each group of tables is associated with a data mart of the respective function.

5. A method for creating a data model for representing an organization having a plurality of groups of functions, the method comprising steps of:
obtaining attributes of the functions from the organization by presenting a predetermined set of questions;
analysing the attributes of the functions; and creating a data model based on the analysis.

6. The method as claimed in claim 5, wherein the analysing step comprises a step of identifying relationship among the attributes.

7. The method as claimed in claim 6, wherein the creating step comprises steps of:

grouping the attributes based on the identified relationship into tables; and joining the tables to represent the identified relationship.

8. The method as claimed in claim 5 further comprising steps of:
extracting data from multiple data marts; and loading the extracted data into the data model.

9. The method as claimed in claim 8 further comprising a step of:
transforming the extracted data into a form loadable into the data model.

10. A console for managing a data model for representing an organization having a plurality of groups of functions, the console comprising:
means for installing a predefined data model; and means for setting a sequence of extraction of information from each function to load the data model.

11. The console as claimed in claim 10 further comprising means for querying the data model to obtain a report of the organization.

12. The console as claimed in claim 10 further comprising means for modifying the data model.

13. The console as claimed in claim 10 further comprising means for tracking histories of changes of data model.

14. A method for analysing an organization having a plurality of groups of functions, the method comprising steps of:
preparing a data model representing interrelation of the groups of functions;
and obtaining information from the data model using the interrelation among the groups of functions.

15. The method as claimed in claim 14, wherein the preparing step comprises a steps of:
obtaining attributes of each group of functions;

analysing interrelation of the attributes of the functions; and creating the data model based on the analysis.

16. A method for creating a report for use by an organization having a plurality of groups of functions, the method comprising steps of:
accessing a data model representing interrelation of the functions;
obtaining information from the data model; and compiling a report based on the obtained information.

17. The method as claimed in claim 16, wherein the accessing step comprises a step of using a predefined form of a report.

18. The method as claimed in claim 16, wherein the obtaining step comprises a step of combining information from different groups of functions.