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ADM – analytic driven management

Getting the right knowledge at the right time is a key success factor in today’s world. Companies with analytic driven management (ADM) strategies gain increasing com-petitive advantages. i – OLAP opens a new level of ADM. It combines predictive analytics directly with the traditional OLAP – technology. This opens a new dimension of data based decision support and analytic driven management to reach competitive advantages. i – OLAP brings deep data insight to the desktop of the business user. The base is a  powerful predictive analytics solution that analyses data in a self – learning process and makes the optimized results directly available in OLAP – cubes for further analysis. The process is simple:

The user specifies the analysis question, e.g. The system finds out the relevant dimensions (attributes, factors) and their importance for answering the question with the highest accuracy and calculates the scores of probabilities in a self – learning process. The benefit of this process is on the one hand the most precise answers to the questions and on the other hand the business user can directly apply these functionalities without requirements for analytical expertise. Then the system itself proposes the configurations of OLAP dimensions that give the best answers. The i – OLAP splits the numeric and nominal dimensions into classes that maximize the quality of the scores. This is not a one direction process – rather the user interacts with the system to get insights from the data. He learns about the influence of the attributes and can make further analysis in the OLAP, e.g. what are the trends across segments or the im-pacts on aggregated levels. He can at any time store the interesting patterns in a specialized repository to be used to generate classifications. The competitive advantages of the analytic driven management with i-OLAP are:
 * identifying the customers with the best cross selling potential
 * valuating the new budget figures, e.g. the reliability of sales forecasts
 * which customers will respond to a promotion
 * what product should be offered next to a customer
 * detecting the relevant attributes for high contribution margins
 * detecting the relevant attributes for large deviations from budgets
 * detecting default risks
 * etc.
 * Cut costs and improve efficiency with optimization techniques and predictive models for market opportunites, shifts and changes
 * Manage risk more effectively and in every transaction
 * Leverage investments in IT and get value of information