Step by step description

1. Stages in Analytics
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JUDE JEEVA ROHITH J

2. 4 Stages in Analytics

3. Descriptive Analytics – What Happened?
• The most basic level of analytics
• This is the vital step in the world of analytics.
• It looks at the events of the past and tries to identify specific pattern within the data.
• It is important to get beyond the initial level of observation and help to get insights
• Visuals like Pie charts, bar charts, tables and line graphs can be used to get the clear data.

4. Diagnostic Analytics-why it Happened ?
• This is where the ability to ask questions about the data and tie those
questions back to objectives and business imperatives is most important.
• A form of advanced analytics that examines the data.
• It is characterized by techniques such as data cleaning, data mining, data
discovery and correlations.

5. Predictive Analytics – What Will Happen?
• Predictive Analytics is another type of advanced analytics that looks to use data and
information to answer the question “What is likely to happen?”.
• The step between Predictive Analytics and Diagnostics Analytics is a big one. Predictive
Analytics involves techniques such as regression analysis, forecasting, multivariate
statistics, pattern matching, predictive modeling, and forecasting.
• Additionally, these techniques require a deep understanding of statistics and programming
languages such as R and Python.

6. Prescriptive Analytics – What can be done?
• Prescriptive Analytics is a method of analytics that analyzes data to answer the
question “What should be done?”.
• To obtain an effective response from a prescriptive analysis, the techniques which
an organization has accomplished each level of analytics.

7. Role of Machine Learning in analytics

9. Applications of Analytics

10. • Also Data analytics become one
of the major booming field and
process in education, logistics,
supply chain, production and
operation management etc.…

11. Analytics in Business scenario
• The use of Analytics is taking incredible walks in practically all roads over
the globe. On the off chance that we can get information and examine it, it
can help in expanding our general occupation productivity to a ton.
• Improving productivity also increases the overall profitability of the
company as well as reducing the number of errors and uncertainty.
Whenever utilized correctly, data analytics can achieve a significant positive
effect on our general public and world everywhere and increment the general
efficiency of specific areas.

12. Conclusion
• All four levels create the puzzle of analytics: describe, diagnose, predict,
prescribe. When all four work together, you can truly succeed with a data
and analytical strategy. If the four aren’t working well together or one part is
completely missing, the organization’s data and analytical strategy isn’t
complete.
• Additionally, teams need to have better skills which allow them to tap into
each level as best they can. The ultimate hope is that those decisions tie back
to the most important business objectives and goals.
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