Testing of Business Intelligence (BI) Applications

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  Business intelligence (BI)  Is a process of gathering, analyzing, and transforming raw data into accurate, efficient, and meaningful information which can be used to make wise business decisions and refine business strategy. As part of the BI process, organizations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data and create data visualizations, BI dashboards and reports to make the analytics results available to business users for operational decision-making and strategic planning.  Business Intelligence Testing (BI Testing)  initiatives help companies gain deeper and better insights so they can manage or make decisions based on hard facts or data. What is Business Intelligence Testing: Business Intelligence testing helps the business users to validate and verify the accuracy of these BI Reports and Dashboards and ensures data credibility and accuracy of insights derived from the BI process. Rep...
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What are the Business Intelligence (BI) Techniques?

 Business Intelligence (BI) Techniques

Here we will discuss the different techniques we use in Business Intelligence process. 

Business Intelligence & Analytics BI

Business Intelligence Techniques

1. Analytics

Analytics is a business intelligence technique that involves the study of available data to extract meaningful insights and trends. This is a popular BI technique since it lets businesses deeply understand the data they have and drive ultimate value with data-driven decisions.

2. Predictive Modeling

Predictive modeling is a BI technique that utilizes statistical techniques to create models that could be used in forecasting probabilities and trends. With predictive modeling, it is possible to predict the value for a particular data item as well as the attributes using multiple statistical models.

3. OLAP (Online Analytical Processing)

Online analytical processing is a technique for solving analytical problems with different dimensions. The most important value in OLAP is its multidimensional aspect that lets users identify problems from different perspectives. OLAP could be used to complete tasks such as budgeting, CRM data analysis, and financial forecasting.

4. Characterization and descriptive data mining

Characterization provides a concise summarization of the given collection of data. Descriptive data mining is based on data and analysis, define models for the database, and forecast the trend used in Segmentation, cluster analysis.

5. Data Mining

Data mining is a technique for discovering patterns in huge datasets and often incorporates database systems, statistics, and machine learning to find these patterns. Data mining is an integral process for data management as well as the pre-processing of data since it ensures appropriate data structuring. 

6. Model Visualization

The model visualization technique is used to transform the discovered facts into histograms, plots, charts, and other visuals that aid in proper interpretation of the insights.

7. Reporting

Reports effectively collect and present information to support management, planning, and decision-making processes. Once the report is designed, it can be presented daily / weekly / monthly / quarterly / semi-annually / annually and automatically sent to the pre-determined distribution list in the required form.

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