shiwaneeg

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

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I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

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Predictive Analytics technology Data

Why is there so much buzz around Predictive Analytics?

By shiwaneeg | May 11, 2018 | 6627 Views

Predictive Analytics is a buzzword today. Wondering what is it? It's Applications? So, first of all, let's get started on the meaning of Predictive Analytics. 
According to Gartner, Predictive Analytics helps to connect data to effective action by drawing reliable conclusions about current conditions and future events.

Particularly it encompasses a variety of techniques such as simulation statistics, game theory and data mining to do this analysis and make these predictions. These predictions enable organizations to use predictive models to exploit patterns found in a historical data to address a business goal such as customer attrition or customer demand. 

In other words, Predictive analytics can be defined as a form of data mining that uses machine learning and statistical modeling to predict the future based on historical data. 

Got a basic idea about what it is? 

Applications of predictive analytics are all around us already. It is used for weather forecasting, recommendation engine, spam filtering and fraud detection. 

Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This allows to focus on the team's efforts on leads with the highest ROI. This also helps in growing from quantity metrics to quality metrics, which leads to focus more time on. The implications of predictive analytics are much beyond. 

To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 

1. Ask the right questions:

First of all, you need to ask the right questions. Questions like:

  • Which questions am I trying to ask with my predictive analytics?
  • Which metrics am I trying to forecast?
  • Which future behavior am I trying to predict?

You need a sound hypothesis to actually test.

2. Need the right data:

The second element to consider is having the right data. We have come a long way in terms of data availability. It is assumed that 90% of all the world data has been generated in the last three years. But, we still need complete and clean data sets to arrive to plausible conclusions. 

It is important to figure out what data is available to you & whether it will be sufficient to answer the questions convincingly. 

3. Need the right technology:

The third element to consider is the right technology, whether or not a particular software is right for the problem you are trying to solve.  

4. Need the right people:

Without the right people, it is impossible to pose the right questions. 

Predictive Analytics has helped making wonders to the world of technology. Leverage this into your daily operations can transform the way we work.       

Source: HOB
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