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Developing Insights with solid base

We received raw data of every type to fit our analysis demands, we analyze and finally we’ve come with something interesting. Insights are called useful only if they can be put into practicality. If I’m a business owner and I’ve ready to use analytics software in front of me, then it’s not a big deal to analyze up and down with figures and charts.

With this post, I would like to mention few important points to improvise the insight development process. Please note that, following steps can be applied to every common operation you perform over data from initialization to the end of analysis.

When you first collect the data, you first perform data cleaning operation to remain only with those data which can guide you through the right direction of analysis.

First of all try to understand the pattern of data you already have.
Normal patterns

  • Constant
  • Linear
  • Cyclic
  • Seasonal pattern with growth

Abnormal patterns (Triggered by external sources)

  • Transient impulse
  • Sudden rise
  • Sudden fall

Once you’re able to judge the data pattern, you can make decision on if there is any data modeling technique applicable to those data or not.

For abnormal patterns, modeling is not possible as it is triggered by the external sources, and forecasting on their triggering behavior is not possible. But yes we can make some prediction on triggering but can’t forecast. When we use two almost similar words forecasting and prediction then we must clearly understand the difference.

 

As we can see in the comparison, when some formulation and processing on facts and figures comes into an operation, the outcomes become more relevant and precise.

This is first part of a depth oriented insight development post. With my next post, in continuation to this post, I’ll take few psychological rules to understand user behavior against their positive or negative action.

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