Data mining and data analytics go in a sync where the collected and stored data’s from various sources such as big data, transactional database, data warehouse, and other internal and external sources are mined by using analytic efforts to identify the patterns and support decision making.
"Identifying irrelevant data from databases is a significant task” (Deepti Mishra, 2014). Thus, Data mining is a systematic process of arranging the raw data and recognizing their different hidden patterns in the large data sets by using mathematical and computational algorithms. Data analytics, on the other hand, is the process of extracting facts from the information to answer some specific questions. For example, ML-Flex, WEKA, Text Analysis, Orange, etc. are some of the open source data mining that helps in structuring the data and find their hidden patterns so that it can be used for some purpose. Walmart uses data mining to find the sales trends, improve marketing campaigns, and find a pattern that can be used to recommend the product to the customers by observing their current and past buying behaviour.
Some of the approaches to Data mining and Analytics are explained below.
1. Cluster analysis
Cluster analysis divides data into groups. Data mining helps in identifying the cluster groups with similar age, gender, geography, sex, etc. and segment the database. After segmenting the database, the marketing company can use it to set their targets for marketing gains. For example, Johnson is promoting and targeting their products to small babies, while Pantene is targeting the young woman who needs smooth and healthy hair.
2. Regression analysis
According to this approach, the changes in one factor affects the other. The change in the buying pattern of the buyer can affect the marketing of that product. Hence, his analysis helps to forecast the changes in buying behaviour, habits and satisfaction level of customers so that they can modify their advertising campaigns costs.
3. Association mining
This is a traditional approach used to know to discover the links between large volumes of product sales activities. According to Bansal (2017), "It’s a rule-based ML method for discovering interesting relations between variables in large databases.” This approach identifies the relationship between different unrelated data in a relational database. For example, if a customer is buying bread, then he is likely to also purchase bread or chicken with it.
4. Decision Tree
In this approach, the data are formed in a tree structure and a parent-child formation. Here each parent represents a class and their child represent the data that comes inside the child. The decision tree analysis is the right computer tool to organize the various decision choices and present in detail with its costs and benefits so that the management can choose the best decisions that are favourable to their company.
5. Markov Model
This approach is known as the best tool to identify a pattern in prediction based applications. It gives results with higher accuracy and strength than the other approaches as it works with both structured and unstructured data. This model is useful in identifying the patterns over the series of data that is used in decision-making.
6. Data warehouse
Most of the data warehouse depends on the relational database, therefore organizations are adopting Hadoop and NoSQL to handle less structured data’s. For example, the application called Sears are used to save time to consign terabytes of data into Hadoop ignoring the ETL process used for the data warehouse.
7. Online analytical processing (OLAP)
This approach allows users to extract and recover useful information from data after observing and analyzing it from various perspectives and penetrating into specific groups. "The software allows users to "slice and dice” massive amounts of data stored in data warehouses to reveal significant patterns and trends” (Wallace, 2013).
In this way, the Business tool is becoming an extremely useful tree to help the organization in the decision-making process by providing all the correct information at the right time and innovative tools to envision data from graphs, pie charts, tables through the use of colour, shapes, 3D views, etc.
References
Bansal, M. (2017, March 28). Association Rule Mining. Deciphered . Retrieved from A Medium Corporation(US): medium.com/data-science-group-iitr...
Deepti Mishra, D. S. (2014). A Comprehensive Overview of Data Mining: Approaches and Applications. International Journal of Computer Science and Information Technologies, Volume 5, Issue 6 , 7814-7816.
Wallace, P. (2013). Introduction to Information System, Second Edition. New Jersey: Pearson Eductaion Inc
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Data mining and data analytics go in a sync where the collected and stored data’s from various sources such as big data, transactional database, data warehouse, and other internal and external sources are mined by using analytic efforts to identify the patterns and support decision making.
"Identifying irrelevant data from databases is a significant task” (Deepti Mishra, 2014). Thus, Data mining is a systematic process of arranging the raw data and recognizing their different hidden patterns in the large data sets by using mathematical and computational algorithms. Data analytics, on the other hand, is the process of extracting facts from the information to answer some specific questions. For example, ML-Flex, WEKA, Text Analysis, Orange, etc. are some of the open source data mining that helps in structuring the data and find their hidden patterns so that it can be used for some purpose. Walmart uses data mining to find the sales trends, improve marketing campaigns, and find a pattern that can be used to recommend the product to the customers by observing their current and past buying behaviour.
Some of the approaches to Data mining and Analytics are explained below.
1. Cluster analysis
Cluster analysis divides data into groups. Data mining helps in identifying the cluster groups with similar age, gender, geography, sex, etc. and segment the database. After segmenting the database, the marketing company can use it to set their targets for marketing gains. For example, Johnson is promoting and targeting their products to small babies, while Pantene is targeting the young woman who needs smooth and healthy hair.
2. Regression analysis
According to this approach, the changes in one factor affects the other. The change in the buying pattern of the buyer can affect the marketing of that product. Hence, his analysis helps to forecast the changes in buying behaviour, habits and satisfaction level of customers so that they can modify their advertising campaigns costs.
3. Association mining
This is a traditional approach used to know to discover the links between large volumes of product sales activities. According to Bansal (2017), "It’s a rule-based ML method for discovering interesting relations between variables in large databases.” This approach identifies the relationship between different unrelated data in a relational database. For example, if a customer is buying bread, then he is likely to also purchase bread or chicken with it.
4. Decision Tree
In this approach, the data are formed in a tree structure and a parent-child formation. Here each parent represents a class and their child represent the data that comes inside the child. The decision tree analysis is the right computer tool to organize the various decision choices and present in detail with its costs and benefits so that the management can choose the best decisions that are favourable to their company.
5. Markov Model
This approach is known as the best tool to identify a pattern in prediction based applications. It gives results with higher accuracy and strength than the other approaches as it works with both structured and unstructured data. This model is useful in identifying the patterns over the series of data that is used in decision-making.
6. Data warehouse
Most of the data warehouse depends on the relational database, therefore organizations are adopting Hadoop and NoSQL to handle less structured data’s. For example, the application called Sears are used to save time to consign terabytes of data into Hadoop ignoring the ETL process used for the data warehouse.
7. Online analytical processing (OLAP)
This approach allows users to extract and recover useful information from data after observing and analyzing it from various perspectives and penetrating into specific groups. "The software allows users to "slice and dice” massive amounts of data stored in data warehouses to reveal significant patterns and trends” (Wallace, 2013).
In this way, the Business tool is becoming an extremely useful tree to help the organization in the decision-making process by providing all the correct information at the right time and innovative tools to envision data from graphs, pie charts, tables through the use of colour, shapes, 3D views, etc.
References
Bansal, M. (2017, March 28). Association Rule Mining. Deciphered . Retrieved from A Medium Corporation(US): medium.com/data-science-group-iitr...
Deepti Mishra, D. S. (2014). A Comprehensive Overview of Data Mining: Approaches and Applications. International Journal of Computer Science and Information Technologies, Volume 5, Issue 6 , 7814-7816.
Wallace, P. (2013). Introduction to Information System, Second Edition. New Jersey: Pearson Eductaion Inc