The more relevant, useful intelligence you have at your fingertips about your business, your customers, your partners and your operations—the more your organization can make better decisions and increase competitive advantage. If you understand your customers better, you can satisfy them in new, focused and more consistent ways. Information gives you an opportunity to control costs, maximize profits, and make better business decisions, faster. 

What is Business Intelligence Reporting?

Standard reports are always helpful, however they must be delivered in near real-time and in an online format that allows drill-down and analysis. Business Intelligence Reporting provides Decision support with the help of Expert Systems build in Reporting with the help of OLAP - Online Analytical Processing Tools which works with Data Warehouses to provide you right data at right time.

To enable organizations to gain competitive advantage, knowledge workers need to have access to relevant comprehensive information and analysis tools to make better decisions, faster. 
Business Intelligence Reporting solutions are implemented to help organizations understand customer buying patterns, identify sales and profit growth opportunities, and improve overall decision making. Business intelligence solutions enable decision makers to be more effective by: 

• Minimizing the time required to collect all relevant business information. 
• Automating the assimilation of the information into personalized intelligence. 
• Providing analysis tools for making comparisons and making intelligent decisions. 
• Closing the loop from decision to action.

Value of BI Reporting to Business 
Business intelligence solutions differ from—and add value to—standard operational systems and Internet applications in three ways. These solutions: 

1. Provide the ability to extract, cleanse, and aggregate data from multiple operational systems into a separate data mart or warehouse called Data Warehouse.
2. Store data often in a star-schema or multi-dimensional cube (OLAP) format to enable rapid delivery of summarized information and drill-down to detail.
3. Deliver personalized, relevant informational views, querying, reporting and analysis capabilities that go beyond the standard reporting capabilities of transaction based systems—a requirement for gaining better business understanding and making better decisions, faster.
   

eReports is OLAP System : Online Analytical Processing System

Systems designed to handle the queries required to discover trends and critical factors are called online analytical processing (OLAP) systems. OLAP queries typically require large amounts of data. For example, the head of a government motor vehicle licensing department could ask for a report that shows the number of each make and model of vehicle registered by the department each year for the past 20 years. Running this type of query against the original detail data in an OLTP system has two effects:

• The query takes a long time to aggregate (sum) all of the detail records for the last 20 years, so the report is not ready in a timely manner.
  
• The query generates a very heavy workload that at least slows down the normal users of the system from recording transactions at their normal pace.

Another issue is that many large enterprises do not have only one OLTP system that records all the transaction data. Most large enterprises have multiple OLTP systems, many of which were developed at different times and use different software and hardware. In many cases, the codes and names used to identify items in one system are different from the codes and names used in another. Managers running OLAP queries generally need to be able to reference the data from several of these OLTP systems.

OLAP data is organized into multidimensional cubes. The structure of data in multidimensional cubes gives better performance for OLAP queries than data organized in relational tables. The basic unit of a multidimensional cube is called a measure. Measures are the units of data that are being analyzed. For example, a corporation that operates hardware stores wants to analyze revenue and discounts for the different products it sells. The measures are the number of units sold, revenue, and the sum of any discounts. The measures are organized along dimensions. In this example, a three dimensional cube could have these dimensions: time, store, and products. Think of these dimensions as forming the logical x, y, and z axis of a three-dimensional, virtual cube.

Each dimension is divided into units called members. The members of a dimension are typically organized into a hierarchy. Similar members are grouped together as a level of the hierarchy. For example, the top hierarchy level of a time dimension can be years, with months at the next level, then weeks, days, and finally hours at the bottom level of the hierarchy. At each intersection of the three dimensions, the values for the measures that match those three dimension values are recorded. For example, suppose that the hour starting at 1:00 P.M. Saturday, Feb. 19, 2000 is a time dimension member, Store #2 of Albany, New York is a store dimension member, and Easy-Clean Mops are a product dimension member. Where these three dimensions meet, the cell records that 10 mops were sold for revenues of $90.00 and an average discount of $1.00.

The specific dimensions and measures defined for the cubes in any particular OLAP system depend on the kinds of analysis important to the enterprise. Transforming OLTP data from relational tables into OLAP cubes, and the design of the cubes, is a complex area that is the subject of many third-party books.

OLAP systems operate on OLAP data in data warehouses or data marts. A data warehouse stores enterprise-level OLAP data, while a data mart is smaller and typically covers a single function in an organization.

Transforming OLTP Data to OLAP Data Warehouses

The transformation of OLTP data so that it gives acceptable performance in an OLAP system requires these processes:

Merge Data

You must be able to merge all the data related to specific items (products, customers, employees) from multiple OLTP systems into a single OLAP system. The merge process must resolve differences in encoding between the different OLTP systems. For example, one system may assign an ID to each employee, and the other systems have no employee IDs. The merge process must be able to match common employee data from both systems, perhaps by comparing employee names and addresses. The merge process must also be able to convert data stored using different data types in each OLTP system to a single data type used in the OLAP system. You must also select which columns in the OLTP system are not relevant to an OLAP system, and exclude these columns from the merge process.

The systems providing input data for an OLAP system are not strictly limited to traditional, centrally located OLTP systems. Valuable information may be stored in various legacy locations, even in some cases including relatively small sources such as Microsoft® Excel spreadsheets stored on a file share.

Scrub Data

Merging the OLTP data into a data warehouse gives you an opportunity to scrub data. You may find that various OLTP systems spell items differently, or the merge process may uncover previously unknown spelling errors. You may find other inconsistencies, such as having different addresses for the same store, employee, or customer. These inconsistencies have to be addressed before the data can be loaded into the data warehouse for use by the OLAP system.

Aggregate Data

OLTP data records all transaction details. OLAP queries typically need summary data, or data aggregated in some fashion. For example, a query to retrieve the monthly sales totals for each product over the last year runs much faster if the database only has summary rows showing the daily or hourly sales for each product, than if the query must scan every transaction detail record for the last year.

The degree to which you aggregate the data in a data warehouse depends on a number of design factors, such as the speed requirements of your OLAP queries and the level of granularity required for your analysis. For example, if you aggregate sales details into daily summaries instead of hourly summaries, your OLAP queries would run faster, but you could only do this if you had no need to analyze sales on an hourly basis.

Organize Data in Cubes

Relational OLTP data is organized in a way that makes some analysis processing difficult and time-consuming. When OLTP data is moved into a data warehouse, it must be transformed into an organization that better supports decision support analysis. The process of building a data warehouse involves reorganizing OLTP data stored in relational tables into OLAP data stored in multidimensional cubes.

Transformation Stages and Data Warehousing Components

The process of making data available through OLAP applications typically goes through three phases:

1. Extract the data from OLTP or legacy data sources into a staging area.
2. Transform the data into a form usable in an OLAP system. This involves actions such as data scrubbing and aggregation.
3. Load the data into a data warehouse or data mart.

The process of extracting the data from the OLTP and legacy data sources and transforming it into the warehouse servers is called the ETL process, and is typically run on a periodic basis, such as once a week or once a month.

Once the data is loaded into a data warehouse, an important part of an OLAP system is to provide facilities for decision makers to access and analyze the data in the data warehouses and data marts.

The illustration shows the general categories of components that OLAP systems use to provide these services.

Data Sources

The OLTP databases and other legacy sources of data that contain the data that must be transformed into the OLAP data in data warehouses and data marts.

Intermediate Data Stores

The combined data storage areas and processes that stage, cleanse, and transform the OLTP data into useful OLAP data.

Warehouse Servers

Warehouse servers are the computers running the relational databases that contain the data for data warehouses and data marts, and the servers that manage the OLAP data.

Business Intelligence

The sets of tools and applications that query the OLAP data and provide reports and information to the enterprise decision makers.

Meta Data

Models the organization of data and applications in the different OLAP components. Meta data describes objects such as tables in OLTP databases, cubes in data warehouses and data marts, and also records which applications reference the various pieces of data.