Collecting Big Data


Cluster Analysis

Grace has a number of modules with high-grade statistical algorhythms that look at available data in more intelligent ways than other tooling. For example the ‘Cluster Analysis’ can search for clusters or cohorts of data, i.e. the search for cohesion having to do with customers’ age. In the example below the relatively large cluster of ‘80+’ is striking. The cluster algorhythms will quickly find cohesion in large data collections, such as cost, completion time, age, risk, et cetera.

 
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Business Rules Engine

Grace’s Business Rules Engine is able to simulate elaborately compiled business rules with various conditions over different corporate data collections. Grace is capable to analyze business questions spread over different administrative corporate systems and to research on which parts of a business rule there is no control. Company rules are put together from rules conditions of the different parts of corporate data. Various stakeholders can check all conditions and check whether they are conformed to the norm. In the example below, customers with opposite expressions on a specific product are being searched for.

 
 
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Correlation Analysis

An organization has large quantities of rough data. We demonstrate Grace’s unique ‘Correlation Analysis’ module, with which patterns or exceptions can be identified quickly, looking at data properties from different domains. With the Correlation Analysis aspects can be compared (i.e. product and region in relation to complaints) and correlation or deviation can be found in data patterns.

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Every cross cut grants access to the data in the semantic warehouse in order to find explanations for deviation with various stakeholders.

 
 

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