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Major Aspects of Business Analytics

Business analytics has become an important tool for project managers to develop detailed project plans and is a key to financial or investment planning. There are certain aspects of business analytics that every manager should understand before using various models of it.<br>

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Major Aspects of Business Analytics

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  1. Major Aspects of Business Analytics that Every Manager should understand Business analytics (BA) is an iterative methodological examination of the data of an organization with particular focus on statistical analysis. Business analytics are mainly used for data- driven decision- making by companies. Techniques of business analytics break down into two main areas.  1. Basic business intelligence: This involves examining historical data to understand how a business department, team or employee has performed over a certain period of time. 2. Deeper statistical analysis: It is the second area of business analysis. This can mean predictive analyses using statistical algorithms on historical data to predict the future performance of any product, service or website design. Or, other advanced analytical techniques, such as cluster analysis, could be used to group customers based on similarities across several data points. This can help in targeted campaigns for marketing. With the ever-increasing competition in the market, business procedures are becoming more and more complex each day. Therefore, organizations have to handle and manage a large amount of data for their every single sector and department. 

  2. Now, the burden of data increasing, the work of data analysis is not relegated to the statistical department only. Managers of each department separately have to maintain their activity-data as well in order to keep updated information.  Business analytics has become an important tool for project managers to develop detailed project plans and is a key to financial or investment planning. There are certain aspects of business analytics that every manager should understand before using various models of it. Data Mining: This term, generally, refers to Big Data, a large amount of data that analysts often use and utilize. Large amounts of data using sophisticated statistical analysis are researched to find patterns and trends that can be useful for a company. The key objective is to provide practical recommendations based on huge amount of information. Correlation Analysis: This technique allows two separate variables to be compared to determine whether they have a relationship. Analysts typically use this approach when management suspects that two variables work in some way together. Analysis of correlation offers a technique to test the assumption.

  3. Factor Analysis: This technique is generally used to compare a large number of variables instead of only two. The statistical approaches used in the factor analysis help in reducing the number of these variables and the amount of required data. Meta-Analysis:  Meta-analysis means research on previously conducted studies on an issue. Researchers look at the earlier work, which has often been carried out over decades, to identify trends, find out patterns and relationships between variables. This approach saves time as well as money in conducting original statistical investigations. Regression Analysis: This technique involves investigating whether a variable has a significant effect on another. By using regression analysis past data on the effect of one variable on another is checked, precise decisions can be taken by business leaders in different areas. Experimental Design: Analytics are used in this field to check the validity of a strategic business plan. This may include an assumption, new product packaging or a strategic marketing plan. This approach can determine if the change improved productivity, saved money or improved product or service quality.

  4. Linear Optimization This technique helps organizations to reduce information from big data sets by transforming them into a series of actionable steps. Analysts determine the best possible outcome on the basis of a number of constraints using a linear mathematical model. These constraints in businesses can include manpower, time, money and materials.

  5. Key Points 1. Data Mining: The key objective of data mining is to provide practical recommendations based on huge amount of information. 2. Correlation Analysis: This technique allows two separate variables to be compared to determine whether they have a relationship.  3. Factor Analysis: This technique is generally used to compare a large number of variables instead of only two.  4. Meta-Analysis:  Meta-analysis means research on previously conducted studies on an issue. This approach saves time as well as money in conducting original statistical investigations. 5. Regression Analysis: This technique involves investigating whether a variable has a significant effect on another.

  6. 6. Experimental Design: Analytics are used in this field to check the validity of a strategic business plan. This approach can determine if the change improved productivity, saved money or improved product or service quality. 7. Linear Optimization: This technique helps organizations to reduce information from big data sets by transforming them into a series of actionable steps. Published by Brainware University

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