Diagnostic Analytics

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big

data

diagnostic

Âge recommandé: 16 ans

8 fois fait

Ray Larkin

United Kingdom

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Compléter Diagnostic Analytics Version en ligne Diagnostic Analytics par Ray Larkin 1 Diagnostic AI expertise Why KPIs anomalies Hidden identify machine three analytics : did it happen ? In descriptive analytics , the importance of Key Performance Indicators were highlighted . These establish common data and identify trends , thresholds , anomalies , and other metrics that lead to the question of why something happened . The functions of diagnostic analytics fall broadly into categories : Identify : Based on the results of descriptive analysis , analysts must identify areas that require further study . Drill into the analytics ( discovery ) : Analysts must the data sources that will help them explain anomalies . Determine the cause of relationships : relationships are uncovered by looking at events that might have resulted in the identified data anomalies . Probability theory , regression analysis , filtering , and time - series data analytics can be useful . Modern solutions for diagnostic analytics employ learning techniques to help data analysts . Machines are more capable at recognising patterns , detecting anomalies , surfacing ? unusual' events , and identifying drivers of KPIs . Diagnostic analytics are generally based on the combination of software and the of people to help answer the question Why did it happen ?