Data Mining explained

The life nowadays is characterised by an explosion in data volumes collected and stored in data stores.  Enterprises accumulate their data in databases. However, information and knowledge are necessary to be extracted from these databases. Analysts must obtain information to be able to predict trends. Their analyses allow competent managers to make dynamic decisions, which is the process known as knowledge management performed by knowledge workers.

The process of extracting knowledge from databases, often called data mining, is an important step in the knowledge management. Knowledge management and data mining techniques allow to augment the decision-making of domain experts with additional knowledge and provide them with a competitive advantage. Executives and managers are the final users of these systems at all levels of control

Data mining is an interdisciplinary field with the general goal of predicting outcomes and uncovering relationships in data, enabling use of automated tools and techniques, employing sophisticated algorithms, to discover hidden patterns, associations, anomalies and/or structures from large amounts of data stored in a data warehouse or other information repositories.

Prediction and description are, in the context of manufacturing, two primary goals of data mining.

While descriptive data mining focuses on discovering interesting patterns to describe the data, predictive data mining is aimed at predicting the behaviour of a model and determining future values of key variables based on existing information from available databases. The boundaries between descriptive and predictive data mining are not sharp.


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KPI's - Hoe ziet succes er uit?

In een organisatie of een project is het van vitaal belang te bepalen hoe succes er uit ziet, onafhankelijk van hoe je van plan bent het werk uit te voeren.  Het bepalen van specifieke, meetbare en a


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