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Diagnostic analytics is the ‘How’ of an event. It parses one or more of your systems for data and examines it to understand the cause of events, responses, and outcomes. It uses several tools and techniques to identify connections and trends that explain why certain events happened in the past. If the events gave good results, you need to know the cause so that you can replicate them. However, if the events cause problems, you must know the root cause to stop them from recurring. Diagnostic analytics answers these questions for you –
Descriptive analytics deals with the question - ‘What.’ To resolve a problem, you must first know what happened. Descriptive analytics scrubs raw data to present the ‘What’ that humans can easily understand. A quick example of this is the question – ‘Have the sales numbers gone up or down this quarter?’
Diagnostic analysis, on the other hand, turns data into analytics, business intelligence (BI), forecasts, reporting, and statistics.
Diagnostic analytics uses a combination of techniques to get a big picture of the data and draw a conclusion. Examples of some of the techniques are –
The right set of quality diagnostic tools and techniques for diagnostic analytics gives you clarity by translating data into actionable insights. They highlight user behaviour and let you optimise your product or service offerings. Data quality diagnostics form the basis for understanding data maturity, monitoring, and cleansing, which turns insights into actionable items.
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