Data and Insights
Insights are the new gold not the data as data is worth very little unless this data is turned into critical actionable insights. These insights can be used to support decision making and can help in refining the design and manufacturing processes. Machine learning algorithms can be used to accomplish different applied data mining tasks. These tasks can be descriptive analytics, predictive analytics, diagnostic analytics, and prescriptive analytics. Descriptive data analytics provides insight into the past and the present while predictive analytics forecasts the future. Diagnostic analytics provides root-cause analysis and perspective analytics advises on possible outcomes and their anticipated impacts.
Data Analytics
Descriptive data analytics provides a better understanding of the data and its nature and identifies patterns or relationships in the data. These descriptive models answer the following questions:
- What has happened?
- What happens now?
- What is the trend of a certain variable?
- What is the relationship between variables?
- How an item is performing w.r.t other item or a benchmark item?
Predictive models make prediction about future values of data and forecasts new proprieties instead of just exploring data properties like in case of descriptive analytics. These models answer the following questions:
- What would happen?
- When would it happen?
- Where would it happen?
Diagnostic analytics provides root-cause analysis to answer questions like:
- Why did it happen?
- Why would it happen?
Last but not least, prescriptive models focus on decision support. This decision support or recommendation engine answers the following questions.
- How would the predictions obtained from predictive models impact everything else?
- What are the proactive decision/actions to be made?
- How we benefit from predictions/recommendations?
- What are the best actions to make?
- What is the best time to take this action?
- What would be the impact of this action?
#descriptive-analytics #deep learning
