Question

Explain the difference between online analytical processing and data mining.

Answer 1

Both data mining and OLAP are two of the common Business Intelligence (BI) technologies.

Online Analytical Processing:

OLAP is a design paradigm, a way to seek information out of the physical data store. OLAP is all about summation. It aggregates information from multiple systems, and stores it in a multi-dimensional format. These could be a star schema, snowflake schema or a hybrid kind of a schema.

OLAP is a class of systems, which provide answers to multi-dimensional queries. Typically OLAP is used for marketing, budgeting, forecasting and similar applications. It goes without saying that the databases used for OLAP are configured for complex and ad-hoc queries with a quick performance in mind. Typically a matrix is used to display the output of an OLAP.

The customers for OLAP and data mining vary. In a typical organization, OLAP is used by the regular front and back office employees. Predominantly, they would use it for an organization-wide reporting or a small time analysis

OLAP is a dimensional model, which can scale up and information can be diced and sliced for interrogation. It is a kind of a BI cube, which is refreshed based on the source data on a periodic basis. However, an OLAP solution lacks the capacity for predictive analysis.

OLAP and data mining are used to solve different kinds of analytical problems. OLAP summarizes data and makes forecasts. For example, it answers operational questions like “What are the average sales of cars, by region and by year?"

Data Mining:

Data mining is the field of computer science which, deals with extracting interesting patterns from large sets of data. It combines many methods from artificial intelligence, statistics and database management.

Due to the exponential growth of data, especially in areas such as business, data mining has become very important tool to convert this large wealth of data in to business intelligence, as manual extraction of patterns has become seemingly impossible in the past few decades.

Data mining is used by business strategists. The strategists base their business moves on the information thrown up by the data mine.

A data mine is built for eternity, which is a shortcoming, as a model cannot be valid forever. Some data mining tools also enable the retention of older models.

Data mining discovers hidden patterns in data and operates at a detailed level instead of a summary level. For instance, in a telecom industry where customer churn is a key factor, Data mining would answer questions like, “Who is likely to shift service providers and what are the reasons for that?”