The database of an organization contains enormous data but the data is of no use if we fail to use it effectively for the benefit of organization. In order to take business decisions, we need to look carefully through the available data, identify the trends, discover the patterns, establish meaningful relationships and find hidden correlation between different variables. Data mining facilitates the same. It is the process of transforming data into meaningful insights after properly analyzing it (Wallace, 2015).
Some approaches to data mining are;
Market Basket Analysis: Market basket analysis is used to identify association between different items people buy. It tries ti find out the combination of items that occur frequently in a transaction based on the assumption that people are likely to buy certain set of items following the set of previous items they purchase. For example: People who buy toothpaste are more likely to buy tooth brush. Similarly, people buying copies and pencils will also buy erasers and sharpeners.
Decision Tree: In this approach, we try to analyze all the possible alternatives related with a decision . Decision tree starts with a question/problem that has multiple answers/solutions and each answer/solution leads to new set of questions or conditions that affect our decision (Zentut). For example: If the profit margin of a company is decreasing, it might be due to decrease in sales or increase in production costs. If the reason is decrease in sale, it might be due to change in weather, introduction of substitute product or decrease in outlets. Similarly, if profit is decreased due to increase in production cost, it may be because of increase in cost of raw material or increased wages of labor. Each of these conditions also has multiple reasons. We keep on building possible scenarios until we reach at the conclusion to make a decision.
Clustering: In clustering, we group data together based on their similarities. For example, we may produce different products for different age group as the need and preferences of the people in same group will be similar (Alton, 2017).
In addition to these, regression, classification, association, outlier detection, sequential patterns and prediction are also used for data mining.
Data Analytics
Data analytics is the set of skills, practices, technologies and applications used by the decision maker to examine the available data and draw conclusion out of it. In data analytics, we tend to look at the past performance to get the insight for future planning. Data mining helps to find the relationship between variables and data analytics tries to find the reason for certain happenings in order to take strategic decision. It is used for fact based decision making, quantitative analysis, exploratory modelling and prediction (O’Brien & Marakas, 2013).
The approaches for data analytics are:
What-if Analysis: In what if analysis, we tend to analyze the effect on other variables resulting from the change in one variable. For example, we are currently selling a product at Rs.20, if we decide to change its price to Rs.25, it will effect in our sales, income, tax and profit. The what-if analysis provokes the question what if the price is increased by Rs.5 and we analyze the data to find the effect of the price hike.
Sensitivity Analysis: Sensitivity analysis is similar to what-if analysis where we analyze the change in dependent variables due to the change in an independent variable. For example: The sensitivity of market price to change in interest rate.
Goal Seeking Analysis: In goal seeking analysis, we analyze whether the business activities are contributing to the attainment of the goals of the organization or not. It requires backward planning. For example: If the business has the target to increase the profit by 10 %, all the activities should be planned in such a way that they contribute to the target accomplishment his method aims to make this happen.
Optimization Analysis: It is the extension of goal seeking analysis where the goal of the organization is to obtain the optimal value possible instead of a pre-determined value.
To conclude, with the advancement of technology, the data in the organization is also increasing. So we need to use data mining and data analytics to extract the relevant information that will help us in taking the best possible decision in a given scenario.
References
Alton, L. (2017, December 22). The 7 Most Important Data Mining Techniques. Retrieved from Data Science Central: datasciencecentral.com/profiles/bl...
O’Brien, J. A., & Marakas, G. M. (2013). Introduction to Information Systems (16 ed.). Irvin: McGraw-Hill.
Wallace, P. (2015). Introduction to Information Systems (Second ed.). New Jersey: Pearson Education Inc.
Data Mining
The database of an organization contains enormous data but the data is of no use if we fail to use it effectively for the benefit of organization. In order to take business decisions, we need to look carefully through the available data, identify the trends, discover the patterns, establish meaningful relationships and find hidden correlation between different variables. Data mining facilitates the same. It is the process of transforming data into meaningful insights after properly analyzing it (Wallace, 2015).
Some approaches to data mining are;
Market Basket Analysis: Market basket analysis is used to identify association between different items people buy. It tries ti find out the combination of items that occur frequently in a transaction based on the assumption that people are likely to buy certain set of items following the set of previous items they purchase. For example: People who buy toothpaste are more likely to buy tooth brush. Similarly, people buying copies and pencils will also buy erasers and sharpeners.
Decision Tree: In this approach, we try to analyze all the possible alternatives related with a decision . Decision tree starts with a question/problem that has multiple answers/solutions and each answer/solution leads to new set of questions or conditions that affect our decision (Zentut). For example: If the profit margin of a company is decreasing, it might be due to decrease in sales or increase in production costs. If the reason is decrease in sale, it might be due to change in weather, introduction of substitute product or decrease in outlets. Similarly, if profit is decreased due to increase in production cost, it may be because of increase in cost of raw material or increased wages of labor. Each of these conditions also has multiple reasons. We keep on building possible scenarios until we reach at the conclusion to make a decision.
Clustering: In clustering, we group data together based on their similarities. For example, we may produce different products for different age group as the need and preferences of the people in same group will be similar (Alton, 2017).
In addition to these, regression, classification, association, outlier detection, sequential patterns and prediction are also used for data mining.
Data Analytics
Data analytics is the set of skills, practices, technologies and applications used by the decision maker to examine the available data and draw conclusion out of it. In data analytics, we tend to look at the past performance to get the insight for future planning. Data mining helps to find the relationship between variables and data analytics tries to find the reason for certain happenings in order to take strategic decision. It is used for fact based decision making, quantitative analysis, exploratory modelling and prediction (O’Brien & Marakas, 2013).
The approaches for data analytics are:
What-if Analysis: In what if analysis, we tend to analyze the effect on other variables resulting from the change in one variable. For example, we are currently selling a product at Rs.20, if we decide to change its price to Rs.25, it will effect in our sales, income, tax and profit. The what-if analysis provokes the question what if the price is increased by Rs.5 and we analyze the data to find the effect of the price hike.
Sensitivity Analysis: Sensitivity analysis is similar to what-if analysis where we analyze the change in dependent variables due to the change in an independent variable. For example: The sensitivity of market price to change in interest rate.
Goal Seeking Analysis: In goal seeking analysis, we analyze whether the business activities are contributing to the attainment of the goals of the organization or not. It requires backward planning. For example: If the business has the target to increase the profit by 10 %, all the activities should be planned in such a way that they contribute to the target accomplishment his method aims to make this happen.
Optimization Analysis: It is the extension of goal seeking analysis where the goal of the organization is to obtain the optimal value possible instead of a pre-determined value.
To conclude, with the advancement of technology, the data in the organization is also increasing. So we need to use data mining and data analytics to extract the relevant information that will help us in taking the best possible decision in a given scenario.
References
Alton, L. (2017, December 22). The 7 Most Important Data Mining Techniques. Retrieved from Data Science Central: datasciencecentral.com/profiles/bl...
O’Brien, J. A., & Marakas, G. M. (2013). Introduction to Information Systems (16 ed.). Irvin: McGraw-Hill.
Wallace, P. (2015). Introduction to Information Systems (Second ed.). New Jersey: Pearson Education Inc.
Zentut. (n.d.). Data Mining Techniques. Retrieved from Zentut: zentut.com/data-mining/data-mining...