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3 budgeting, performance measurement and quality analysis (Hart and Porter, 2004). OLAP applications are typical examples of BI technology. OLAP applications are developed especially for information rich industries like marketing, finance and consumer goods production to gain competitive advantage in a global marketplace by converting available data into useful information (Beynon and Maad, 2002). In all these different types of industries and business functions, OLAP supports its users by letting them look at their data from different perspectives, performing complex calculations quickly, aggregating data from several sources and doing all these in a user friendly manner, generally providing visual tools. In order to utilize better and appreciate its advantages, one needs to understand the basic terminology and concepts related to OLAP. Data for OLAP is stored in special databases which have a different structure than OLTP databases. An OLAP database has a special schema suitable for OLAP operations. This schema, which is usually called as star schema, is not fully normalized as in OLTP databases and has a dimensional structure. In this dimensional modeling of the data, there are two basic types of tables which are fact tables and dimension tables. Fact tables are used to store numeric values called measures. Measures are fields like sales amount of a product, unit price for an item or cost of a purchased material which are numeric fields that can be used in calculations. Measures are the values that the user wants to see in summary or in detail during the analysis of the data. Dimension tables, on the other hand, are tables used to view the measures from different angles, like time, product category or sales region. Dimension tables are related to fact tables through some common attribute in a foreign key relationship forming a shape like a star as shown in Figure-1. Dimensions allow users to navigate the data in fact tables from different aspects, letting them either sum up the values or drill down into the detail. Because of the multi-dimensional structure of OLAP schema, in OLAP databases there is a special storage structure which is called a cube representing multidimensionality. Cubes are basically a combination of fact and dimension tables. Although cubes in geometry are three dimensional, OLAP cubes can have several dimensions. But the term cube is used symbolically to differentiate this multidimensional structure from two dimensional row-column structures of database tables and spreadsheets. The physical storage of OLAP databases is also different from OLTP databases. There are three different approaches. The first is relational OLAP (ROLAP) which uses a relational database engine to store data. Multidimensional OLAP (MOLAP) method, on the other hand, doesn t use relational engine but uses a distinct structure for dimensional modelling of the data. Finally, Hybrid OLAP (HOLAP) uses a combination of both techniques to store OLAP data (Riordan, 2005). Regardless the types of physical implementation, OLAP systems are expected to give fast responses to complex 231

4 queries involving millions of records and a lot of calculation. In order to give a fast response to such queries, OLAP systems make calculations and store the values before the queries are executed. Therefore, during query execution, the results are unexpectedly fast considering the number of records included in the calculations. OLAP users need a query language to retrieve values and make the calculations using several measures from fact tables. The standard language for querying relational databases has been Structured Query Language (SQL). SQL is so common that it is used by virtually any type of database system. Unfortunately, SQL is not suited for answering problems that are supposed to be solved by OLAP. Therefore OLAP tools have their own query languages. One such language is MDX, (Multidimensional Expressions). MDX is developed by Microsoft and now adopted by several other vendors in their OLAP products. MDX is standardized and has become a widely accepted OLAP query language. Although MDX is similar to SQL in some respects, it has special clauses and features making it easy to make multidimensional view and querying of the underlying data. MDX is of course very important for OLAP developers, but for users of OLAP applications the user interface of the applications usually have visual tools so that the users do not need to write the specific MDX queries. The application writes the query under the hood freeing the user from the complexities of writing an MDX query. On the other hand, more experienced users may benefit from learning this query language to perform customized and complex analyses. In this part of the paper, in order to clarify some of the OLAP concepts and how it can be used in a typical business, a sample OLAP application will be discussed for a fictitious company, which we call as company X. We will assume that Company X is a manufacturing company, producing several types of candies and chocolates. In a business, like company X, there are several functions like purchasing, inventory management, production planning and scheduling, production, sales and marketing, distribution of products, accounting and financial management. For all these functions and processes covering more than one function, there are several questions managers need to answer. For example, a manager may want to know how much of some type of raw material is purchased in the past for several time periods to see seasonal fluctuations and make better forecasts of future purchases. Another manager may need to look at the sales of different product groups across different store types, different sales regions and different time periods. Also, how much revenue gathered from the sales of different products can be of interest. Accounting department may want to find out the relative effects of several factors on correctly determining the costs of goods sold. Another concern can be determining the relative profitability of several products and based on that deciding whether to increase or decrease the production of some products. 232

6 The sales cube for company X consists of a sales fact table containing sales related measures and some dimensions to view the summary and detail data on these dimensions. The fact table contains sales quantity, sales amount, discount percentage for the item sold, cost of goods sold and profit amount as measures. It also has some key attributes to relate to dimension tables. The cube has product, store and time as dimensions. The product dimension table contains information about the products that the company sells to its customers. It has fields like product ID and product name and it has a relation to a product family table to allow analysis based on product groups. These product groups can be formed based on the several custom criteria defined by the users allowing the creation of several custom product families. Company X sells its products to stores of different types, so customers of the company are stores and so customers are represented in store dimension. The store dimension table has basic attributes about the store, like store ID, store name and store type, and connected to a region table to determine the sales region of the store and to allow further analysis based on sales regions. Finally, time dimension table holds information about the date the sales is done and related time information like the fiscal period information. Based on the data in time dimension, sales figures on a monthly basis or quarterly basis can be calculated easily. The dimension tables are related to fact table through common fields between fact and dimension tables. The data in dimension tables can be used to define hierarchies which allow looking at fact data at several levels. For example, product hierarchy can be used to look at the sales data at the product or product family level. In a similar fashion, a store hierarchy lets the user to navigate and drill down sales data at different region level and specific store level. Figure-3 shows a sample look at the sales cube. In addition to purchasing and sales cubes mentioned above, other type of cubes can be created for analysis purposes. For example, an inventory cube can be created to make inventory analysis, to understand the material flow into the company and to get a clear picture of stock levels. Another potential cube which can be useful for managers is an accounting cube. Accounting cube can be used to look at the accounting data by different fiscal years. The cube can contain several measures related to accounting, such as assets, liabilities, and income data. These measures can be viewed through several dimensions like an account dimension or a time dimension. A summary report can be easily generated to grasp the general situation of the company or the user can drill down into specific details. Accounting cube can also let managers do several types of custom analyses thereby enabling management accounting. In any business and especially those operating in information rich and turbulent markets, managers need timely, accurate and high quality information for effective decision making. Business Intelligence is an umbrella term that refers to a set of technologies to help 234

8 Beynon, M. and S. Maad (2002), Empirical Modelling of Real Life Financial Systems: The Need for Integration of Enabling Tools and Technologies, Transactions of the SDPS, Vol. 6, No.1, pp Buchanan, Leigh and Andrew O Connell (2006), A Brief History of Decision Making, Harvard Business Review, Jan. 2006, pp Davenport, Tomas H. (2006), Competing on Analytics, Harvard Business Review, Jan. 2006, pp Hancock, John C. and Roger Toren (2006), Practical Business Intelligence with SQL Server 2005, NJ: Addison Wesley Professional. Hart, Mike and Gabi Porter (2004), The Impact of Cognitive and Other Factors on the Perceived Usefulness of OLAP, The Journal of Computer Information Systems, Vol. 45, No.1, pp Olszak, Celina M. and Ewa Ziemba (2007), Approach to Building and Implementing Business Intelligence Systems, Interdisciplinary Journal of Information, Knowledge, and Management, Vol. 2, pp Riordan, Rebecca M. (2005), Designing Effective Database Systems, NJ: Addison Wesley Professional. 236

9 TIME Dimension CUSTOMER Dimension REGION Dimension SALES Fact PRODUCT Dimension SALESMAN Dimension 2005 Supplier 2 Category 1 A B C

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