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OLAP
OLAP is an acronym for Online Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is the foundation for many kinds of business applications for Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.>>Advantages of OLAP>>Knowledge is the foundation of all successful decisions. Successful businesses continuously plan, analyze and report on sales and operational activities in order to maximize efficiency, reduce expenditures and gain greater market share. Statisticians will tell you that the more sample data you have, the more likely the resulting statistic will be true. Naturally, the more data a company can access about a specific activity, the more likely that the plan to improve that activity will be effective. All businesses collect data using many different systems, and the challenge remains: how to get all the data together to create accurate, reliable, fast information about the business. A company that can take advantage and turn it into shared knowledge, accurately and quickly, will surely be better positioned to make successful business decisions and rise above the competition.>>Implementing an OLAP Solution
OLAP technology implementations depend not only on the type of software, but also on underlying data sources and the intended business objective(s). Each industry or business area is specific and requires some degree of customized modeling to create multidimensional “cubes” for data loading and reporting building, at minimum. An OLAP solution might be intended for dynamic reporting for finance professionals, with source data originating in an ERP system. Or a solution might address a medical institution’s activities as concerns patient analysis. All of which is to say that customers need to have clear objectives in mind for an intended solution, and start to consider product selection on that basis. Another factor to consider in an OLAP implementation is the delivery to end users: does the initial user base want to adopt a new front end, or is there a preference for utilizing a dashboard? Or perhaps users are better served by a dynamic spreadsheet “delivery” system to achieve, for example, a collaborative budgeting and forecasting solution.OLAP technology has been defined as the ability to achieve “fast access to shared multidimensional information.” Given OLAP technology’s ability to create very fast aggregations and calculations of underlying data sets, one can understand its usefulness in helping business leaders make better, quicker “informed” decisions.
DATA MINING
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] It is an essential process where intelligent methods are applied to extract data patterns.[1][2] It is an interdisciplinary subfield of computer science.[1][3][4] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD.[5] >>The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[6] It also is a buzzword[7] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[9] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.>>The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.