Dimensional data modeling is used for offline analysis of data and is associated with data warehousing. There are two elements to real world modeling.

The level of abstraction in case of real time transactions which is also called on-line transaction processing is very high and granularity low. On the other hand, data warehousing demands a higher level of granularity which is required for extracting intelligence from a database. This is called dimensional data modeling. In this model, the information is extracted from database offline. For example, a sales transaction may have many dimensions. Sales data, price, quantity and discount are different aspects which may be captured by a dimensional data model.

The same data is stored differently for on-line transaction processing and for data warehousing purposes. Why should the same data be represented in different forms? It should be appreciated that in any information processing system we are dealing with different levels of abstraction. Data extraction and business intelligence require a higher level of information procession. This would generally not be possible in real time.

As regards the actual implementation of dimensional data model, it consists of fact tables and lookup tables. Fact tables connect to lookup tables. There may be many fact tables which are not related to each other. Each of these fact tables would have their respective lookup or attribute tables.

There are two ways in which a dimensional data model is represented. The star schema has a fact table at the center of the star with the attribute or lookup tables forming the different arms of the star. There can be many fact tables and associated lookup tables in a single dimensional data model.

The other implementation is called the snow flakes schema. One can imagine this to be a snow flake with each star point breaking up into numerous stars. This results in lower memory requirements and also improves granularity.

Dimensional data models are important for extracting business intelligence from databases. It forms an important concept in data warehousing. Its applications are many and particularly in sales and marketing. Tracking consumer preferences and providing useful insights into their behavior is the ultimate aim of creating dimensional data model. Implementation can vary depending upon the quality of granularity or detail which may be required from a particular business transaction. Essentially it is an offline model which compliments the on-line transaction processing model which is used for real time or on-line transactions.

By Odalys E. Anton, VP Web/Application Development

GreenCode Technologies, Inc.

(954) 840-8068

OLAP or On-line Analytical Processing technology has been around awhile.Lately, with the advent of social networking and Web 2.0 applications, raw data by itself has lost meaning. Conversion of raw data into useful information is taking up much of the effort in combination with collection, collation and gathering of data. OLAP by its very definition is a tool to present cogent and reliable information for management.

Most of the OLAP vendors are essentially operating in the database or data handling space. It is seen that the bigger players are consolidating on their strength by buying off niche OLAP players. The vendor scenario underwent a drastic overhaul with Oracle and SAP leading the way with the purchase of Hyperion Solutions and Business Objects respectively. What can be clearly observed is that major enterprise resource processing or ERP firms are eagerly lapping up the OLAP offering. Also relevant is the purchase of Cognos by IBM. Those in the know are aware that Cognos has been on the OLAP radar for quite a while and considered as a thought leader in this field.

OLAP has been used, with mixed success, in the consumer goods or FMCG and financial sectors. The availability of data is crucial to the implementation of OLAP. Data manipulation being the essence of OLAP tools, it must have multiple presentation options, which can be used by different groups by extracting relevant information from a single data set. This flexibility is important because groups within a single organization have different decision making roles, but essentially depend on the same dataset.

The action in the OLTP space has almost become frenetic, mostly because of the need to make sense out of zillions of bytes of data captured by modern day organizations. Customer relationship management (CRM) is dependent on data extraction capabilities of software. Information discovery and resultant customer retention and conversion strategy are closely interlinked. OLAP has therefore become critical to the entire marketing and sales effort. In fact, OLAP is important for business operations from the stand point of data analysis and the process of discovery.

It has been argued that OLAP is a data extraction process and acts as a middleware rather than a data collection tool. By far this assumption seems to be fair , though it is predicted that the entire data environment including data collection(database) , business intelligence(BI) , Enterprise resource planning(ERP) and data presentation ( front ends and web-sites) would all come together as one monolithic tool.

By Odalys Anton, VP Web/Application Development

GreenCode Technologies, Inc.

(954) 840-8068

Extracting relevant data from huge databases and making intelligent use of it ,is the domain of data mining.

Data by itself has no meaning. When data is collated and analyzed it leads to knowledge discovery. It is this process of converting raw data into information or knowledge which is called data mining. Usually we associate data mining with data warehousing and generally with enterprise resource planning.

Data mining in business is big business. Software specifically designed to facilitate data mining form a part of business intelligence. Customer relationship management is another area which has found extensive application of data mining. What is the trend in customer preferences? What is the purchase pattern? Is it possible to find patterns in customer behavior? These are the questions which data mining attempts to offer. Instead of treating the entire customer base as a single entity, with similar tastes, data mining can help in distinguishing and classifying them under several groups. For example, banks use data mining to discover high value clients and provide facilities to them accordingly. Data mining has found significant application in unearthing credit card frauds. For example, a purchase made through credit card which is not following a definite pattern associated with that card holder can be marked suspect. A high value jewelry purchase on a card not having a similar credit history can be identified and fraud prevented. These are some areas in which data mining has been extensively utilized. Data mining is associated with expert systems and knowledge based systems. As the branch of artificial intelligence matures, data mining would assume greater importance.

Data mining is also deployed in many other fields. Genetic engineering is an area which requires handling of tremendous amount of data. Sifting through this enormous data and extracting relevant and cogent information is an enormous task. Data mining is playing an important role in genetics and bio-engineering fields which require data analysis in a big way. Data mining is being used in medicine for cancer research and molecular biology. In science and technology it has found a place in nanotechnology.

Data mining is an exciting field which is finding a place in almost every human activity. Knowledge discovery, expert systems and artificial intelligence have together extracted useful information from enormous amount of data which would not have been possible without using data mining techniques. Marketing, sales, human resources and customer relationship management have deployed data mining in some form or another. In fact, it has crept into all forms of business.

By Odalys E. Anton, VP Web/Application Development

GreenCode Technologies, Inc.

(954) 840-8068

After 9/11 the focus has turned on disaster recovery. If you consider the fact that many companies lost their entire data, the importance given to DR is understandable. Disaster may occur not only due to terrorist attacks but also from natural calamities.

Fire is a major source which can disrupt data center activities. Data center disaster recovery plan is therefore a necessity to ensure business continuity. What should be the scope of disaster recovery plan of a data center? Ideally the entire organization including the top management should be involved actively in developing a suitable plan. But in practice, it is only the IT department which is involved. There is a misconception that is prevalent among the managerial staff that data backup is the sole domain of technology department and the focus is on developing data backup and recovery plan. But this is itself a recipe for disaster. Data backup may be one of the elements in disaster recovery but not the only one by any means. To get a better idea of disaster recovery let’s examine its various aspects.

Data centers come in many flavors and sizes. If we consider data center of a bank, any time lapse between a disaster striking and recovery of operations would be nil. There obviously cannot be any tolerance limit. What should be the elements of a data center disaster recovery plan in these circumstances? The presence of two geographically distributed data centers with independent communication backbone with instant switch over is the only option. This has high cost implication which is unavoidable. Maintaining two independent infrastructure setups with identical hardware and communication backbone becomes imperative.

What about a data center which is catering to the requirements of an archival system. For example, records of an insurance company. In this case the data itself may be invaluable but the time taken for recovery from a disaster may not be a critical factor. The data can be presented after a few minutes, without drastically affecting the business process. In this case, data backup may be the only factor to be considered. Here again, what should be the ideal time for recovery? This is not solely a management or technology decision. The cost of implementing data center disaster recovery plans depends on the time factor. Sometimes it may be sufficient to backup data on a daily basis. If a disaster occurs, say at the twelfth hour, the entire data till that time would be unavailable. This may be acceptable for some applications. But the cost of implementing such a disaster recovery plan may be far less than more rigorous ones.

Lately there has been a move towards maintaining two data centers at geographically separated locations. But the cost implications of such a disaster recovery plan are prohibitive and considered unnecessary except for very few applications. Some disaster recovery plans consist of backing up data on tapes and physical media at regular intervals and transporting them to another location. This can mitigate the losses faced during any disaster at reasonable cost to the company.

By Odalys E. Anton, VP Web/Application Development

GreenCode Technologies, Inc.

(954) 840-8068