go to Condition Explorer >
create Boolean Variable and paste below function on expression definition:
mod (RowNumber (),2)
go to Page Explorer >
select cell on crosstab
on properties, define Style Variable >
chose Variable Boolean1
chose 'Yes' on Boolean1 when you click Condition Explorer
and change background color to red
chose 'No' on Boolean1 when you click Condition Explorer
and change background color to pink
finish.
createdBy. @aankdavid
Wednesday, May 16, 2012
Sunday, February 26, 2012
BI Reporting
Business Intelligence Reports
Data is collected from various sources and cleansed or transformed into a form required by data warehouse or data mart decision support systems. BI tools provide a means of taking advantage of these data contained within a data warehouse or data mart by providing answer to critical queries related to the business.
The power of business intelligence actually relies on the knowledge acquired through the analysis of various reports taken using business intelligence reporting tools. Business Intelligence Reporting Tools or OLAP Tools provide different views of data by pivotting or rotating the data across several dimensions. BI tools are capable of displaying data in several formats like, tables, pivots, charts, query results or reports.
Zero Footprint Technology
Earlier, with client/server architecture, business intelligence reporting tools were installed on each user's desktop in order to connect to the database and to take reports. Nowadays, with the revolution of web advancement, Zero Footprint technology allows users to connect to the database and see the reports through web browsers which doesn't require them to install any software to do so. With the help of this Zero Footprint technology, the reports that were only availabale to top executives, now can be seen by any employee making them to participate and contribute towards the decision making.
source: learnbi.com
ETL Process & Data Warehouse
ETL, an acronym for 'Extraction, Transformation and Loading' is a collection of processes associated with extracting the source data, transforming that data and finally loading that data into a data warehouse. Before loading the required into data warehouse, it should be transformed in order to meet the needs of the data warehouse. This transformation involves several processes like data cleansing, data profiling, data type conversion, validating for referential integrity, performing aggregation if needed, denormalization and normalization.
Data Warehouse
Data warehouse is a centralized repository where all the information for analysis is kept in an organization. This is the data collected from variant sources for the purpose of analytical processing and reporting. This data is non-volatile and a data warehouse is built on a dimensional data model. From this data warehouse, data can be extracted for reporting needs with the help of query tools or many data marts can be built based on subject area requirements.
Data Mart
Data Mart is subject oriented, basically a sub-set of data warehouse, built for the purpose of analyzing a particular line of business or department. It holds the data specific to a particular subject area like sales, purchase etc. Data marts can be of derived from a data warehouse or built for the sole purpose of BI directly from the source and like data warehouse, data marts are also constructed from dimensional data models.
source: learnbi.com
Star Schema
In a Dimensional Data Model, a fact table is the centralized table which is connected to multiple dimensions related to that fact. This type of approach is known as the Star Schema design based on which data warehouse and data marts are built. Since BI takes advantage of data displayed in the form of mutli-dimensional cubes, this star schema approach helps analyze and produce complex reports very easy by slicing and dicing the dimensions of interest.
From the sample diagram shown below, the required Fact 'Loan Amount' can be calculated across various dimensions like state, branch, time, product, loan officer and investor dimensions.
Sample Star Schema Diagram
source: learnbi.com
Dimensional Modeling
Dimensional Model comprises a fact table and many dimension tables and is used for calculating summarized data. Since Business Intelligence reports are used in measuring the facts(aggregates) across multiple dimensions, dimensional data modeling is the prefered modeling technique in a BI environment. A Fact table contains various measures or facts like sales amount, loan amount etc., whereas a Dimension table describes the particular entity like time, state etc., based on which the required facts are measured.
source: learnbi.com
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