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Different Process In The Business Intelligence Strategy In Data Science
In this age of Big Data, the process of Business Intelligence has become a crucial step in the enterprises business development strategy. As most of the enterprises these days are having access to large volumes of Big Data & with Business Intelligence process, enterprises can explore the insights that are encrypted deep inside Big Data. This process will help the stakeholders to define the areas where they should drive changes, eliminate inefficiencies, and quickly adapt to market or supply changes. Apart from the several benefits, the application of Business Intelligence will also help the enterprises to stay competitive & helps them in taking several revolutionary leaps forward.
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Now, let’s look at the different processes that are included in the Business Intelligence process in Data Science.
Prominent Aspects in Business Intelligence Process In Data Science-
- Data Mining
It involves Databases, Statistics & algorithms in Machine Learning to interpret with large sets of Big Data. It helps in exploring the hidden insights & in uncovering the trends.
This process involves communicating the findings from the Big Data Analysis process to the business stakeholders or managers & help them make smart business decisions.
- Performance Metrics & Benchmarking
In this process, the present business data is compared against the historical data to track the performance of each & every process against goals.
- Descriptive Analytics
In this stage, we analysts will be performing preliminary data analysis to get clear insights to what exactly happened.
In this stage, data specific questions would be raised & Business Intelligence teams would work towards pulling the answers from the datasets.
- Statistical Analysis
The results obtained from the process of Descriptive Analytics are analyzed & the data would be subjected to Statistical Analysis to better understand the trends.
- Data visualization
This process involves turning data insights into attractive visuals in the form of charts, graphs, and histograms so that stakeholders who are mostly from the no-analytical background wouldn’t find any difficulty in consuming the insights from the data.
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