Project Management
System Analysis
Architecture Design
Software Development
Database Management
Business Intelligence


OLAP Architecture

We divide OLAP operational characteristics into three main modules:
  1. OLAP graphical user interface - GUI
  2. OLAP analytical processing logic
  3. OLAP data-processing logic

These three OLAP modules, residing in the client/server environment, make it possible to use OLAP' s three defining characteristics: multidimensional data analysis, advanced database support, and easy-to-use end user interface.


ETL - Extraction, Transformation and Load

We utilize ETL to build, deploy, and manage a series of linked, dimensional data marts to form an integrated business intelligence system. Dimensional data marts are organized by subject area such as sales, finance and marketing, and coordinated by data category such as customer, product and location. We deliver a panoramic view of the key business dimensions for our clients in terms of time, locations, customers, products, services, accounts and channels.

OLAP - Online Analytical Processing


Multidimensional Data Analysis Techniques
  1. Advanced data presentation functions: 3-D graphics, pivot tables, cross tabs, data rotation, three-dimensional cubes. Such data presentation facilities are compatible with desktop spreadsheets, statistical packages, query and report-writer packages.
  2. Advanced data aggregation, consolidation, and classification functions: allow the business data analyst to create multiple data aggregation levels, slice and dice data, drill down and roll up data across different dimensions and aggregation levels. For example, aggregating data across the time dimension (by week, month, quarter and year) allows the business data analyst to drill down and roll up across time dimensions.
  3. Advanced computational functions: business-oriented variables (market share, period comparisons, sales margins, product margins, percentage changes, etc.), financial and accounting ratios (profitability, overhead, cost allocations, returns, etc.), statistical and forecasting functions, and so on. These functions are provided automatically, and the end users do not need to redefine their components each time they are accessed.
  4. Advanced data modeling functions: support for " What-If " scenarios,  variable assessment, variable contributions to outcome, linear programming, and other modeling tools.
bulletAdvanced Database Support
  1. Access to many different kinds of DBMSs - Database Management System, flat files, and internal and external data sources.
  2. Access to aggregated data warehouse data as well as to the detail data found in operational databases.
  3. Advanced data navigation features such as drill-down and roll-up
  4. Rapid and consistent query response times.
  5. The ability to map end user requests, expressed in either business or model terms, to the appropriate data source and then to the proper data access language (SQL 2000 or Oracle 9i). The query code is optimized to match the data source, regardless of whether the source is operational or data warehouse.
  6. Support for very large databases. The data warehouse can easily and quickly grow to multiple gigabytes and even terabytes
bulletEasy-to-Use End User Interface
bulletClient/Server Architecture
  1. The Client/Server architecture provides a framework within which new systems can be designed, developed, and implemented.
  2. The Client/Server environment enables us to divide an OLAP system into several components that define its architecture. These components can then be placed on the same computer or they can be distributed among several computers. Our OLAP is designed to meet ease-of-use, as well as system flexibility, and requirements.



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