along with any in-house developed systems. On a predetermined or on-demand basis 朴槿惠拒不认罪 司机冲进晨练队伍

Enterprise Performance Management (EPM) solutions are getting a lot of attention these days. Planning cycles are no longer stagnant pieces of corporate information; rather they now require constant vigilance and flexibility for corrections as dictated by the changing market climate. Organizations are always looking for improved and enhanced EPM solutions to stay current and stay competitive. Oftentimes it is hard to uncover the one tool in the array of tools that actually delivers on the promises and guaranteed results. The best way to illustrate a proven solution is to provide an example. The goal of every EPM solution is to provide management with the best possible set of data for finding key performance indicators and determining how to best manage these to make sure they remain a constant and reliable source of feedback on enterprise performance. Multiple companies in different industries have implemented this solution. And feedback shows that it works especially well in environments that have multiple financial productse.g., Essbase, Planning and HFM. Todays business environments are typically made up of a variety of operational systems. These systems hold the data necessary for business assessment, planning purposes and any necessary midcourse corrections. The solution example will look specifically at financial monitoring. The example will also show how the solution, combined with the included tools, can extend measurements to personnel management, warehousing, manufacturing and other areas of the business. SOLUTION COMPONENTS Relational Database The relational database is used to create a financial data repository (FDR) of information that can be used to recreate all of the reporting environments in the event of a disaster. As such, the FDR will contain all data records (FACTS counts, dollars or any other type measure) and the structure (Metadata) used to construct the hierarchies used by the Oracle Hyperion tool set. The FDR can best be characterized as an operational data mart storing financial information, both metadata and transactional in nature, which will be used on some predetermined cycle (nightly, weekly, monthly) to create fact tables and dimension tables that will be used to update the Oracle Hyperion BI and reporting environments. For this particular example, the two most common relational databases are Oracle and Microsoft SQL Server. Essbase Essbase is referred to as a Multidimensional Online Analytical Processing (MOLAP) tool. It allows data to be viewed through the use of a multidimensional data model. Reports that query information from Essbase typically use a cross data that intersects various sets of data points. For example, if I wanted to perform a sales analysis, I might want to see Sales Dollars by region, by office, by sales managers, and by sales personnel assigned to each of the sales managers. The by is how I define each intersecting data point. The multidimensional model is what allows me to create the set of multidimensional points of interest and stores data (in this case dollars) at the intersection points of each dimension (I am defining a dimension each time I use the word by) in an optimized storage array capable of doing near real-time retrievals. Oracle Hyperion Planning Oracle Hyperion Planning is part of the EPM suite of tools. It creates an enterprise wide budgeting and forecasting solution that marries the financial and operational planning processes for an organization. It also creates a set of planning models that delve into the operational portions of a business to provide information about the operational impact on the financial well-being of an organization. Planning couples the organizations financial and operational planning models. It helps the organization satisfy its immediate need for producing a financial planning model while also creating a cross functional platform that can be used in operational models. Because Planning is centralized within the organization, the planning process for the entire organization is created, viewed and scrutinized through this, creating a single sourced set of models that the entire organization uses. Oracle Hyperion HFM Oracle Hyperion HFM (Hyperion Financial Management) is part of the EPM suite of tools. It is a finance department maintainable tool that creates business rules to perform consolidations, reporting and analysis in one scalable solution. Oracle Hyperion DRM Oracle Hyperion DRM (Data Relationship Management) is part of the EPM suite of tools. It is a data management tool that allows the finance group to manage the pertinent dimensions and metadata. It provides support for extensive validation and enforcement of business rules, and when used across the enterprise, will enable and enforce data governance and compliance across the enterprise. The enterprise can also use the tool to build a consistent set of terms and definitions across all systems using like metadata. SOLUTION COMPONENTS The solution components for the EPM solution example include: Relational Database Essbase Oracle Hyperion Planning Oracle Hyperion HFM Oracle Hyperion DRM Solution Description An enterprise maintains the majority of its financial informationand any other required information for reportingin the operational systems. Using the schematic above, those operational systems can be any combination of Oracle Financials, PeopleSoft, SAP, and JD Edwards, along with any in-house developed systems. On a predetermined or on-demand basis, you pull extracts from the operational systems and push them to the FDR. From there, a routine strips the metadata (dimensions and attributes) from the files and creates a file that can then be compared against the existing dimension and attribute values currently in the FDR. Differences will range from new values coming in to changes in parent-child relationships. The result of these comparisons is the creation of an automator file which is used to update the information contained in the DRM hierarchies. Individuals responsible for maintaining the correct relationships within DRM will receive alerts about the new members for which they will need to go into each of the dimensions and validate, correct or modify the new entries, ensuring they have been positioned correctly within the appropriate hierarchies. Upon completion of the validation process, exports of all the appropriate metadata (dimensions and attributes) are then spun out of DRM after which the appropriate FDR processes receive the new information and rebuild the dimension and attribute tables. These new tables then form the backbone for the loads that are used by the reporting layer. The FDR is now in synch with the new fact data that was read into the FDR. The loads and processing of the new data can commence, and then the new data can be loaded into the Essbase cubes, sent to Planning via ODI, or prepped for HFM with a new load file. This ongoing cycle allows: metadata to be in synch with the new fact tables. all of the data necessary to rebuild the financial reporting environment to reside in one location. If any Lock and Send spreadsheets are used to make modifications or incorporate outlying data, then these spreadsheets should also be maintained for historical purposes in the database. The FDR should become an organizations sole source for rebuilding data and, if fed properly, will ensure that should anything happen to all or any part of the reporting environment, a rebuild using the data and metadata from the FDR will create a consistent set of information. FDR Design The sample FDR design shown below is presented as a twist on a traditional method because it provides an excellent mechanism to easily incorporate new dimensions and attributes without disrupting the existing design. It is a basic tenet of BI tools that work in conjunction with a relational database to incorporate a Star schema as an integral part of the design. Stars Components In a typical Star, the fact table resides at the center of the design. The fact table consists of records within a table, which for each row in the table contains the dimension keys along with the measure value used to create the desired intersection point within the reporting environments. The key to each dimension contains the value that points to the dimension table and provides the necessary descriptors to give meaning to each row in the fact table. For each dimension member referenced in the fact table, there is a corresponding row in the dimension table. The corresponding row in the dimension table has the attribute values associated for that dimension. Consider the following Fact table: Account IDRegion IDOffice IDSales Person IDCurrency IDAmount 1000-011RG_2110OF_1200SP_34CU_CA1250.34 This row entry represents the revenue booked against all the dimensions contained in the row. Consider now the associated dimension tables: Dimension NameValueDescription Account Dimension1000-011Revenue for Product Line A Region DimensionRG_2110Quebec, Canada Office_IDOF_1200Downtown Quebec Sales Office Sales Person IDSP_34Mark Thompson Currency IDCU_CACanadian Dollar AmountMeasure1250.34 Canadian Dollars Consider also that associated with the Office Dimension is a Sales Outlet Type. For this particular OF_1200, the Sales Outlet Type is Store Front. The row record in the Office Dimension would look like: Office KeyOffice DescriptionSales Outlet Type OF_1200Downtown Quebec Sales OfficeStore Front FDR Deployment Once the conceptual FDR has been designed, there are a number of ways to physically deploy the FDR. This case study is going to document only one of the methods. Figure 3 Physical FDR Deployment In this deployment, all of the attributes have been added to one physical database table. The structure for that table is built to support DRM maintaining each of the attributes in separate tables. The minimum information for the Attribute Table would be: Sequential Key Computer generated DRM Dimension Name (i.e. Asset Type) Parent Node Name Child Node Name Each attribute is associated to the appropriate dimension table and a cross-reference record is created that lists at a minimum: Dimension ID Attribute Sequential ID Attribute Child Name Once these associations have been set up, the Oracle Hyperion tools are free to build the required models using the appropriate dimensions and the associated attribute cross references as appropriate to process the correct fact tables. Benefits of Using this Design There are a number of benefits to implementing the FDR using this physical deployment. THE FDR can be built and deployed without knowing all of the dimensions and attributes beforehand. This design is built as a framework and foundation. DRM provides the framework to maintain all of the hierarchies for both dimensions and attributes. Adding new dimensions and attributes can be proceduralized allowing for additions without disruption. Adding new dimensions and attributes does not disrupt existing models. This framework is easy to maintain and works well with all of the Oracle Hyperion BI tools used to build models. New models can easily be built to accommodate new fact tables as required. About the Author: Learn more about TopDown Consulting, the preferred Hyperion/EPM solution partner for many of the largest and best performing Global 2000 companies, at . For additional perspectives, please visit and subscribe to the TopDown Consulting RSS blog feed at and follow the company on Twitter at @TopDownInc. Please feel free to publish the above blog in full or in part with attribution according to the Creative Common license. Article Published On: – Business 相关的主题文章: