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MOLAP, ROLAP, HOLAPEdit

MOLAP: Multidimensional Online Analytical ProcessingEdit

This is the more traditional way of OLAP analysis. In MOLAP, data is stored in a multidimensional cube. The storage is not in the relational database, but in proprietary formats.

Advantages:

  • Excellent performance: MOLAP cubes are built for fast data retrieval, and is optimal for slicing and dicing operations.
  • Can perform complex calculations: All calculations have been pre-generated when the cube is created. Hence, complex calculations are not only doable, but they return quickly.

Disadvantages:

  • Limited in the amount of data it can handle: Because all calculations are performed when the cube is built, it is not possible to include a large amount of data in the cube itself. This is not to say that the data in the cube cannot be derived from a large amount of data. Indeed, this is possible. But in this case, only summary-level information will be included in the cube itself.
  • Requires additional investment: Cube technology are often proprietary and do not already exist in the organization. Therefore, to adopt MOLAP technology, chances are additional investments in human and capital resources are needed.



ROLAP: Relational Online Analytical Processing.Edit

This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality. In essence, each action of slicing and dicing is equivalent to adding a "WHERE" clause in the SQL statement.

Advantages:

  • Can handle large amounts of data: The data size limitation of ROLAP technology is the limitation on data size of the underlying relational database. In other words, ROLAP itself places no limitation on data amount.
  • Can leverage functionalities inherent in the relational database: Often, relational database already comes with a host of functionalities. ROLAP technologies, since they sit on top of the relational database, can therefore leverage these functionalities.

Disadvantages:

  • Performance can be slow: Because each ROLAP report is essentially a SQL query (or multiple SQL queries) in the relational database, the query time can be long if the underlying data size is large.
  • Limited by SQL functionalities: Because ROLAP technology mainly relies on generating SQL statements to query the relational database, and SQL statements do not fit all needs (for example, it is difficult to perform complex calculations using SQL), ROLAP technologies are therefore traditionally limited by what SQL can do. ROLAP vendors have mitigated this risk by building into the tool out-of-the-box complex functions as well as the ability to allow users to define their own functions.



HOLAP: Hybrid Online Analytical ProcessingEdit

  • HOLAP technologies attempt to combine the advantages of MOLAP and ROLAP. For summary-type information, HOLAP leverages cube technology for faster performance. When detail information is needed, HOLAP can "drill through" from the cube into the underlying relational data. HOLAP allows direct access to data from the data warehouse which is not worthwhile to be transformed into OLAP cubes.

Importance of BIEdit

BI – An Introduction

One of the great ironies of information technology is that companies spend a lot of time and money amassing terrific amounts of data, which they then largely under utilize for strategic decision-making purposes.

Importance of BI

> Has emerged as a powerful tool for business

> Allows capture, analysis, interpretation, and dissemination of information

> BI applications are closely related to CRM

BI Functions

> Data integration and organization, Data analysis, Performance analysis, Information dissemination, Collaboration

Data Integration & Organization

Data Analysis, Data Analysis and Reporting Methods

OLAP and Data Mining

OLAP –provides the ability for users to perform detailed, summary, or trend analysis on data and allows for drill-down into that data.

Data mining -is the analysis of data for relationships that may not have previously been known. Discovery of information from data mining can be useful within many areas of the business, from customer analysis to production planning and cost control.

Performance Analysis

KPI- A performance indicator or key performance indicator (KPI) is a measure of performance. Such measures are commonly used to help an organization define and evaluate how successful it is, typically in terms of making progress towards its long-term organizational goals. KPIs can be specified by answering the question, "What is really important to different stakeholders? KPIs may be monitored using Business Intelligence techniques to assess the present state of the business and to assist in prescribing a course of action.

Implementation

Basic structure of an implementation will follow:

Planning phase; The critical success factors for BI include ease of use, scalability, flexibility, performance, and security.

Architecture design; two critical issues include database design and system architecture

> whether to use a two-tier or three-tier access design for the data warehouse/data mart.

> Two-tiered structures are simpler and often less costly, but if the number of users is high,performance can easily degrade.

> A three—tier structure allows the servers within the system to balance the load of user requests and is also required for some specific vendor products.

Execution

Top management commitment needs to exist throughout the project to ensure adequate resources are dedicated, and to gain employee buy-in.

> In addition, a cross-functional team approach to the entire project is necessary to allow departmental input, and evaluation of the project planning and implementation

Performance Metrics

Performance Metrics focuses more on the customer than the visits to a site. They log each customers visit by following their click process, known as a clickstream. This allows for a far greater understanding of customers than a hit counter. If you do not measure it, you cannot manage it,

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