Business Intelligence BI in WPF OLAP Common
What is BI?
Business Intelligence (BI) simplifies information to enable all decision makers of an organization to access information easily. This helps the decision makers at all level to understand, analyze, collaborate, and act on information anytime, anywhere.
Here is how Wikipedia defines BI.
Why to use BI?
It is hard to over emphasize the importance of Business Intelligence (BI) in today’s world. It is impossible to make strategic business decisions without analyzing the past business performance. Businesses are increasingly investing heavily in tools and services that help decision makers to visually analyze the data in myriad ways.
Several products and solutions have emerged to cater to the Business Intelligence market; Lesson have been learn, and currently Online Analytical Processing (OLAP) is the de facto standard for persisting and accessing BI data. Applications built using OLAP include sales reports, executive reports, forecasting, and so on.
What’s new in BI?
While BI has been an expensive affair in the past, requiring several thousands of dollars in investment for integrating a solution into an enterprise, recent technological developments have considerably reduced the cost to implement and own an OLAP-based BI solution.
Microsoft’s SQL Server Analysis Services (SSAS) is one such solution, which is a set of OLAP services provided as part of Microsoft SQL Server. The SSAS is available for almost a decade. It is a mature and very cost-effective solution for maintaining multidimensional (also called cube) data. With an efficient OLAP storage mechanism, you only need another efficient OLAP visualization tool set to complete your BI needs. This is where the Syncfusion OLAP controls come into place.
Multi-dimensional structure is defined as “a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data”. The structure is broken into cubes which are able to store and access data within the confines of each cube. Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions. Even when data is manipulated, it is still easy to access as well as be a compact type of database. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use OLAP applications. Analytical databases use these databases because of their ability to deliver answers quickly to complex business queries. Data can be seen through different ways, which gives a broader picture of a problem unlike other models.
Multi-dimensional database products were commercially popularized as Online Analytical Processing (OLAP) systems to help analysts do decision support on large historical data. They expose a multidimensional view of the data with categorical attributes like products and stores forming the dimensions and numeric attributes like sales and revenue forming the measures or cells of the multidimensional cube. Dimensions are usually associated with the hierarchies that specify aggregation levels. For instance, store-name ->city ->state is a hierarchy on the store dimension. The measure attributes are aggregated to various levels of detail about the combination of dimension attributes using functions like sum, average, count, and variance. OLAP products provide convenient tools for exploring the data cubes through navigational operators like select, drill-down, roll-up, and pivot conforming to the multidimensional view of data. An analyst can interactively invoke sequences of these simple operations to visualize the measures along various combinations of dimensions and at various levels of aggregation. Our OLAP Architecture provides you the easiest way to the extreme analysis of data.