Business Intelligence
Data mining and OLAP are the most common Business Intelligence technologies. The term Business Intelligence refers to computer based methods to identify and extract useful information from business data. Online Analytical Processing commonly known as OLAP provides summary data and generates rich calculations. OLAP is a class of systems that provide answers to multidimensional queries. OLAP is typically used in business reporting for sales, marketing and various such domains. OLAP enables the users to view the data interactively from multiple perspectives.
On the other hand, Data mining helps discover hidden pattern or trend in data to support a conclusion. As the name suggests, unlike OLAP that operates at a summary view, Data mining operates at detail level. For instance, if walmart would like to identify the trend of products sold during a holiday season, data mining would help them answer that question based on historic data.
Although, OLAP and data mining operate on data to gain intelligence, the main difference lies on how they operate on the data. OLAP tools provide multidimensional data analysis and summaries of the data. On the contrary data mining focuses on ratios, patterns and influences in the set of data. OLAP and data mining can complement each other. OLAP might point out problems with sales of a specific product for walmart for this month in particular region. Data mining can be used to gain an insight about the behavior of customers in the region. Data mining can predict such as 5% increase in sale.
Data mining can be used to identify the most important attributes concerning sales and those attributes could be used to design the data model in OLAP.
We can divide IT systems into transactional (OLTP) and analytical (OLAP).
OLTP
OLAP
OLTP stands for On-line Transaction Processing
OLAP stands for On-line Analytical Processing
OLTP tables are highly normalized
OLAP tables are generally de-normalized with fewer tables
OLTP comprises of Operational
On the other hand, Data mining helps discover hidden pattern or trend in data to support a conclusion. As the name suggests, unlike OLAP that operates at a summary view, Data mining operates at detail level. For instance, if walmart would like to identify the trend of products sold during a holiday season, data mining would help them answer that question based on historic data.
Although, OLAP and data mining operate on data to gain intelligence, the main difference lies on how they operate on the data. OLAP tools provide multidimensional data analysis and summaries of the data. On the contrary data mining focuses on ratios, patterns and influences in the set of data. OLAP and data mining can complement each other. OLAP might point out problems with sales of a specific product for walmart for this month in particular region. Data mining can be used to gain an insight about the behavior of customers in the region. Data mining can predict such as 5% increase in sale.
Data mining can be used to identify the most important attributes concerning sales and those attributes could be used to design the data model in OLAP.
We can divide IT systems into transactional (OLTP) and analytical (OLAP).
OLTP
OLAP
OLTP stands for On-line Transaction Processing
OLAP stands for On-line Analytical Processing
OLTP tables are highly normalized
OLAP tables are generally de-normalized with fewer tables
OLTP comprises of Operational