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How To Boost Productivity Using OLAP Tools
Copyright (c) 2007-2012 John Parsons
You might have heard about OLAP, but did you know why it could be so important for anyone who wants to know more about any data stored in a database or warehouse? This article shows why OLAP-tools are widely used by people of any profession, and why it is so important to have well-represented visual information instead of raw data to take reasonable and important decisions.
Why is OLAP so important?
The main reason why OLAP is so important is its flexibility. Once a user defines dimensions and measures of interest, the OLAP-tool gives an incredibly easy way to analyze data by moving these dimensions and measures into needed areas.
Anyone who ever tried to make a cross-tab report, will appreciate the ease of the approach, when you just place the dimension you want in the report position you want. And though it is quite important, the ability to create a cross-tab report is not the final goal OLAP would achieve. The ultimate result is representing data as information that a person could easily understand and use.
Let's take a manager of the marketing department working with a database of all transactions made for the last year. There might be thousands or millions of records in this database. So to answer a simple question like "What is the trend of sales of a given item in the given area?" he will most likely have to analyze all sales for the given timeframe and make a visual representation of the sales history, and then calculate possible future values for the same dimensions. And though databases allow one to know sales summary for any period in no time at all, visual representation may be a quite hard task. At the same time most of the OLAP-tools can solve this task with ease. And that gives the analyst a chance to concentrate on thinking rather than on mining the information to think about.
OLAP is useful because it allows one to find out the reason why data looks like it does, i.e. determine the main reason that affects the data. For example, if the sales of a trade company are way up or way down for a given quarter, it is quite easy to find out what product or what region makes the changes. Then expanding the dimension members of interest it is very easy to drill down into progressively more detail, but only to such extent that one may spot a trend or a problem.
OLAP Types
As any technology this one doesn't have a single solution. There are four major approaches to OLAP: MOLAP, ROLAP, HOLAP, and DOLAP.
Multidimensional Online Analytical Processing (MOLAP) is the most standard approach to OLAP solutions. It uses a multidimensional database, which directly stores the information contained in the various cubes.
Relational Online Analytical Processing (ROLAP) provides the same solution but uses a relational database for data storage. This approach translates native OLAP queries, written in a language called multidimensional expressions (MDX) into the appropriate SQL statements. This is primarily done to prevent the need for another copy of the data. The data created directly by the online transaction processing (OLTP) applications are used. The primary disadvantage to this solution is that it does not perform as well as a MOLAP, and has a little poorer ability because SQL expressions are not always able to reproduce multidimensional expressions adequately.
Hybrid Online Analytical Processing (HOLAP) is a hybrid approach to the solution where the aggregated totals are stored in a multidimensional database while the detail data is stored in the relational database. This is the balance between the data efficiency of the ROLAP model and the performance of the MOLAP model.
Desktop Online Analytical Processing (DOLAP) is a convenient way to get easy-to-use and comfortable access to your OLAP data from within your desktop applications. This approach assumes that the data is stored in a relational database, but it is loaded onto client computer to create a multidimensional representation for a user when or before a user requests his first slice. After that a user is free to close the database connection and work as if the data were stored on the client. The main advantage of this approach is that the client may perform data processing itself and so avoid OLAP servers' usage and installing. In case of web OLAP applications, data processing is usually performed on an application sever rather than on a client computer, however this doesn't boost the system performance. This approach is widely used and very popular in case of file databases like Access.
So OLAP solutions create new perspective of processing large amount of data and allow converting the data into information and useful answers to important questions.
Visual Analysis
While OLAP has been a kind of revolution in data analysis field, it often cannot make the users entirely happy because of being short of visual means of data representation. What would be the use of a tool that gives out hundreds of numbers and provides no means to make some conclusions of these numbers? And that's where visual analysis can help.
Visual analysis is defined as analytical reasoning provided by interactive visualization. The goal of visual analysis is to make large datasets understandable and easy to analyze that is a major intellectual challenge. The field of visual analysis is currently a subject to apply to various fields like human-computer interaction, scientific and information visualization, statistical graphics and mathematical visualization, and geographic information systems and cartography.
The idea is that it is quite difficult to answer questions by working with a set of numbers. And even if the application provides charting wizards or the like, it is not a good alternative, because it is not good at finding out the answers to deep questions, and often limited by a small amount of data. And here comes the technology called "visual analysis", that is a high performance software interface for visualizing questions to multidimensional data in a meaningful manner.
Visual analysis means that the data should be presented in a way that is easy to understand and think of visually. The right presentation allows one to organize and understand information you need. For example, you might want to see some emergency or abnormal situations in your business at once, without digging through a lot of numbers to identify an outlier. This is where visual analysis comes out with its graphical and easy-to-spot data representation.
Conclusion
There are a wide range of OLAP tools today giving the best chance to make your data more clear. So why waste your time on searching for regular occurrences in you data? OLAP-tools can make it for you. It doesn't really matter if you're a professional data analyst, or a marketing manager, or simply a person who collects stamps. The rule is that if you have a database you might want to use it to answer questions, and reveal some useful knowledge from there.
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John Parsons writes for Radar-Soft. Founded in 2004, Radar-Soft, LLC specializes on developing OLAP-tools for developers on different developing platforms. Since that time Radar-Soft team has made a lot of innovations implemented and working in its products. Today Radar-Soft offers complex solutions for different programming platforms including ASP.NET and .NET Windows Forms. The wide range of solutions allows developers of all kind to give their applications OLAP- functionality with ease. ASP.NET solution can be tested online at http://olaponline.radar-soft.com/default.aspx You can know more about the Radar-Soft products at http://www.radar-soft.com/
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