A BI architecture can be deployed in an on-premises data center or the cloud. In either case, it contains a set of core components that collectively support the different stages of the BI process, from data collection, integration, storage and analysis to data visualization, information delivery and
Smart business application
A BI architecture can be deployed in an on-premises data center or the cloud. In either case, it contains a set of core components that collectively support the different stages of the BI process, from data collection, integration, storage and analysis to data visualization, information delivery and the use of BI data in business decision-making.

BI platforms are designed to do heavy-duty processing of data in the cloud or on your company’s servers. BI tools pull in data from multiple sources into a data warehouse, and then analyzes the data according to user queries, drag-and-drop reports, and dashboards.
BI platforms are designed to do heavy-duty processing of data in the cloud or on your company’s servers. BI tools pull in data from multiple sources into a data warehouse, and then analyzes the data according to user queries, drag-and-drop reports, and dashboards.
Having accurate data and faster reporting capability provides for better business decisions
Business intelligence can directly impact customer experience and customer satisfaction.
IT departments and analysts spend less time responding to business user requests. Departments who didn’t have access to their own data without contacting analysts or IT can now jump into data analysis with little training. BI is designed to be scalable, providing data solutions to departments who need it and for employees who crave data.
BI systems enhance data organization and analysis. In traditional data analysis, different departments’ data is siloed and users have to access several databases to answer their reporting questions. Now, modern BI platforms can combine all of these internal databases with external data sources such as customer data, social data, and even historical weather data into one data warehouse. Departments across an organization can access the same data at one time.
Organizations can be more competitive when they know the market and their performance within the market. Rosenblatt Securities analyzed data from hundreds of sources and was able to see the best possible time to enter and exit the market and position themselves strategically.
The Application Design should follow the Model-View-Controller (MVC) model for rendering and modeling data objects. The interface must be able to connect to a database to store XML schema defined using XSD and data streams. Source and destination formats for data must include XML and may also include: Extensible Style sheet Language Transformation (XSLT), JavaScript Object Notation (JSON), Comma Separated Value (CSV), and American Standard Code for Information Interchange (ASCII).

| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| API | Equipment | 1 | 15000 | 15000 |
| bindings | Miscellaneous | 3 | 700 | 2100 |
| Printing | Miscellaneous | 500 | 10 | 5000 |
| Cloud Account | Equipment | 1 | 14880 | 14880 |
| Total in (Rs) | 36980 |
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