The times of today are seeing Business Intelligence emerge as playing a key role in business decisions by providing proven solutions to help leverage best business practices to deliver the timely granular intelligence, needed to create increasingly effective business strategies, to continuously strengthen the competitive position, and steadily improve the revenue streams”.
BI is a term that encompasses a broad range of analytical software and solutions for gathering, consolidating, analyzing and providing access to information in a way that is supposed to let an enterprise’s users make better business decisions. The term BI encompasses software for extraction, transformation and loading (ETL), data warehousing, database query and reporting, multidimensional / on-line analytical processing (OLAP) data analysis, data mining and visualization.
Benefits of BI Implementation
- Help organization make better decisions – by provision of customized applications or tools aimed at users of various cadres.
- Allow business users to analyze and better understand their organization’s plans
- Reduced operating costs
- Reallocate resources to low cost channels
- Alert organizations to potential issues when exceptional conditions occur – such as sales dropping below forecast or inventory falling below a threshold value.
- Lower operating costs
- Gain Competitive advantage
- Enhance product offerings and improve customer service
- Greater share of customers mind and wallet by identification of key customer needs and service opportunities.
- Enhanced Revenue through new customer acquisitions & cross-selling / up-selling to existing customers
- Improve the overall efficiency and effectiveness of your organization
Based on the complexity of the solution and the requirements, appropriate BI technologies are selected. Analysis for the functional deliverables is best done through prototyping. This gives them an opportunity to adjust their delivery requirements and their expectations.
Requirement analysis is performed including a predefined set of the key performance indicators (KPIs) that are required by the end users. The analysis phase produces a high level design of the various components of the solution with the sources of relevant information. Because of dynamic nature of BI projects, modifications in objective, people, estimate, technology, users and sponsors can severely impact the success of the project.
Post designing phase, the development of the BI Implementation model is carried out in a step wise manner,
- The full process flow of information across the organization should be modeled.
- The requirements for what type of meta data to capture and store must be documented in a meta model. In addition, the requirements for delivering meta data to the users have to be analyzed.
- If a meta data repository is purchased, it will most likely have to be extended with features that are required by BI applications. If a meta data repository is built, the database has to be designed based on the meta model developed during the previous step.
- The database design schema must match the access requirements of the business.
- Depending on the data cleansing and data transformation requirements developed during analysis, an ETL tool may or may not be the best solution. In either case, preprocessing the data and writing extensions to the tool capabilities are frequently required.
- The real payback for BI applications comes from the business intelligence hidden in the organization’s data, which can only be discovered with data mining tools. Developing Meta Data Repository becomes a subproject of the overall BI project.
Once all components of the BI application are thoroughly tested, the application is deployed at the user ends.
The success of BI project primarily lies on the quality of end-user training and support. This phase requires an interactive approach, with extensive user training and adjustments to meet the user needs. This phase includes the development of predefined reports and analyses for the business users, and laying the groundwork for more advanced analytics in the future.
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