V4 is an Enterprise Data Integration Solution (EDIS) that enables business users to perform robust forecasting and strategic data analyses on enterprise data, independent of platforms, database systems or other data sources. Using business intelligence, multidimensional processing and knowledge management functionality, V4 analyzes data in an unlimited number of dimensions, creating "what ifs" and performing "drill downs" for more specific information. Operating as a single point of connectivity, V4 seamlessly integrates large volumes of data throughout the enterprise, including ERP systems, customized legacy applications, data warehouses, e-commerce processes and other data sources. The features of V4 are described in further detail below: Multidimensional Functionality The V4 product is based on the concept of multiple dimensions. A dimension is any type of concept or view that you may wish to represent. For example, some typical enterprise dimensions are time, measures, products, geographical regions, sales channels, etc. Unlike traditional two-dimensional views of data, V4 operates in a virtually unlimited number of dimensions. Multidimensional views provide an underlying foundation for analytical processing and flexible access to information. V4 offers the ability to analyze data across any dimension, at any level of aggregation quickly and easily while insulating business users from complex query syntax.   Contextual Processing V4 is based on the concept of researching data, similar to the way people think and reason about that data. The role of contextual processing in conjunction with multidimensional functionality allows users of V4 to elegantly solve many complex problems. V4 interprets requests and data according to the requestor. For example, year-to-date might mean seasonal to a marketing manager, and fiscal to a financial person.   The diagram above represents two disparate systems. Each references a data field named "year-to-date. " For system A, "year-to-date " may be defined seasonal, and for system B it may be defined as fiscal. V4 can reside between these two systems and translate the value appropriately for each system's context.   The contextual processing capability inherent in V4 enables users to think conceptually about the problem, not about how data is represented and stored. V4 incorporates facts, rules, and procedures in a seamless fashion in order to provide the high level of integration needed to answer real-world inquires. It is no longer sufficient to simply report back on data or sums of data; financial, statistical, and industry specific models. V4 employs human reasoning supported by a rules-based model for complex and strategic data analysis.   Statistics/Financials V4 is the ideal solution for complex financial analysis and reporting. Multiple dimensions (variables) can be defined for company, department, profit center, general ledger account, forecast, budget, currency, etc. Reports for any combination of dimensions can be easily generated. New budgets can be quickly formulated with general rules and filled in by department heads with specific facts. Different revisions can be maintained. "What if ' analyses can be developed and then run on any subset of the data. Enterprise data from any number of sources can be evaluated and V4's financial analysis functions can be applied for comparison and optimization. All statistical formulas and algorithms are included within the V4 internal modules..   Microsoft Excel, the V4 user interface V4 operates at the desktop level, with Excel from Microsoft, a tool that is familiar to most of today's business users. Microsoft Excel add-ins in the form of drop down windows, are available, giving Excel users the ability to directly access V4. Macros can be used to guide users through any necessary setups. Specific routines can be easily incorporated into the Excel menu structure. Users can perform what-if analysis and drill-down operations directly from within Excel.   V4 Security The V4 solution allows general access rules and more specific access rules as the environment and situation warrants. General rules can either permit access or deny access to data and as such can be added over time without interfering with existing security policies and procedures.   Metadata Repository and Quality of the Data V4 can be utilized to summarize, correlate, tabulate, and point out trends in large amounts of data helping to ensure its quality. Metadata is literally data about data" and describes what we know about the components of a database. Necessary requirements for a state-of-the-art metadata repository include being able to represent different points of view about a database, how the database has changed over time, and ; how different systems reference the data. V4, a contextual multidimensional model provides these different points of view quickly and easily to assist users in navigating through the complexities of voluminous data warehouses.   Linking to External Data V4 supports a standard set of routines (Open DataBase Connectivity, ODBC) which have been developed to allow access to multiple database platforms. Operating as a single point of connectivity, V4 seamlessly integrates large volumes of data throughout the enterprise, including ERP systems, customized legacy applications, third-party data, data warehouses, e-commerce processes and other data sources.   Fast Access to Voluminous Data With V4, a small set of six to ten rules has the power to collect and process large volumes of data from many data sources throughout the enterprise. Operating as a single point of connectivity, V4 seamlessly integrates large volumes of data throughout the enterprise, including ERP systems, customized legacy applications, data warehouses, e-commerce processes and other data sources. For example, V4 can access 50,000 records per second, stored in a multidimensional V4 database, on an average-sized PC, which represents a billion records in less than 6 hours.   V4 / OLAP / Relational Comparison of Capabilities – End User Perspective   Topic Description / Example V4 OLAP Relational Multiple Cubes Access data from several different concept cubes for an integrated analysis. For instance comparing values from a sales cube with those of a customer satisfaction survey model. Yes No or Difficult n/a Aggregate & Detail Access to high level totals and all of the supporting detail that went into the totals. Yes limited n/a Access to Diverse Data Be able to utilize data not only from your well defined data warehouse but also from external sources such as the web and end-user Excel and Access databases. Yes No limited Hot Links To be able to hot link from high level result to one or more detailed data analyses or reports. This is not a drill down to supporting detail but hot linking to what would normally be considered different sub-systems within an ERP. For example to link from a "Cost of Sales" number on a Profit & Loss statement to a detailed cost of sales breakdown by customer, product type, or raw material commodity code. Yes No No Very Large Databases To be able to efficiently handle and analyze very large databases (millions of records, gigabytes of data) Yes Yes Yes Very Complex Databases A complex database is one with many different types of data. A good example of a complex databases are those which support an ERP. Data warehouses for large corporations are also typically complex. Yes No Yes Modeling Represent data as algorithms, build an entire "database" on calculations and integrate in with other databases. Yes No No One Product Provide the means to define, load, clean, and optimize data from various sources. Generate ad hoc simple reports to complex reports. Perform simple to complex calculations. Integrate metadata and security. All in one product! Yes No No What-Ifs The very nature of a "what-if" requires change. A basic assumption with data warehouses is that the data does not change. A "full featured" what-if facility must be able to "change" any subset of the data warehouse and/or any of the supporting analyses without actually changing anything. Yes No No Multiple Data Formats Data changes over time. Sales data captured today is not necessarily in the same format as sales data captured two years ago. A data analysis product must be able to integrate data that has varied in structure without having to explicitly load and convert it. Yes No No Data Element Linked to Multiple Sources Not all databases are complete- data may be missing or unknown for various periods represented by the database. A database tool must be able to handle missing data and support errors, defaults, or interpolations. Yes No No   V4 / OLAP / Relational Comparison of Capabilities – Technical Perspective   Topic Description / Example V4 OLAP Relational Multiple Cubes Access data from several different concept cubes for an integrated analysis. For instance comparing values from a financial cube (i.e. general ledger) with those of a customer satisfaction survey model. Yes No or Difficult n/a Integrated Metadata Integrate descriptive information about your data into the database and be able to access that data along with actual data. Yes No No Context Provide contextual access to all aspects of the data and analyses built using the data. Use the contextual access to enhance re-use, support many types of what-if’s and present results to different users in different appropriate formats. Yes No No Object Oriented Be able to model data using object oriented concepts such as hierarchies, inheritance, and polymorphism. Yes No No Integrated Security Most databases implement a simple security paradigm which is fine as long as it matches your needs. A truly integrated security model must be able to handle any number of security mechanisms from simple passwording to more complex data hiding schemes. Yes limited limited Rapid Prototyping To be able to rapidly build a working prototype with limited data and then grow into larger production database – without having to restructure prototypes. Yes sometimes sometimes Incremental Updates Update database with periodic new data without having to rebuild/refresh/re-index. Yes sometimes Yes XML Support The ability to import and export data using the XML standards. Yes maybe maybe Single Paradigm The number of different products and tools required to manage and support a data warehouse is rapidly overwhelming most IS managers and technicians. What is needed is a single paradigm capable of describing data, loading and cleaning data, storing in flat and multi-dimensional structures, building models, generating ad hoc and complex reports, and finally growing with the growth of your company. Yes No No   For more information about V4 the enterprise integration and data analysis solutions, please Contact Us. To better understand the power of V4 over SQL and OLAP, take the V4 CHALLENGE! Then, examine the true cost of generating a report  the old way and the V4 way!

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