Looking into the future is difficult. But with predictive analysis based on historical data combined with analytical tools we can predict the future. The analytical tools are based on statistical models and machine learning. So that, We can make better predictions than before.
At the same time, we risk going crazy if the facts we build are incorrect or if we use bad strategies. – This process of future prediction may take weeks, months or even years before we discover it.
Correctly utilized, predictive analysis has potential to revolutionize a variety of industries such as retail, logistics, manufacturing, financial services and health care. Following are the best step-by-step principles & advices to succeed:
Predictive analysis by computer is based on data and requires feedback to be continually improved.
-It is important to understand what type or types of data flowing in the model.
Are data gathered every day like on Facebook or Google, or is it hard-working data in hard-to-access records, says Eric Feigl-Ding, economics economist who is visiting researcher at Harvard Chan’s School of Public Health.
In order for the predictive analysis to be correct, the model must be designed to work with just the specific types of data it is being fed.
-Just throwing in a large amount of data is judged to fail.
Because there is an abundance of data, most things are not relevant to a given problem, even if it seems relevant in a given selection, says Henri Waelbroeck, Research Manager for Trade Solutions at Factset.
– A model trained on unilateral data can be totally wrong.
-According to Richard Mooney (Product Manager for Advanced Data Analysis on SAP)
Everyone is obsessed with algorithms but the algorithms are no better than the data they feed with. If there is no pattern to find, they will not find one. Most datasets have hidden patterns.
-Patterns can be hidden essentially in two ways. It may be that the pattern lies in the relationship between two different columns. For example, a pattern may appear when comparing the closing date of an upcoming business with opening data for the emails associated with the deal.
The opening rate should increase sharply if a deal is about to be locked because there are a lot of people on the merchant side who go through the contract, said Richard Mooney.
-A pattern can also be revealed if you look at how a variable changes over time.
“If you link to the example above, the knowledge that a customer opened an email 200 times not half as useful as they opened it 175 times the past week.
Good news – there are almost an unlimited number of methods to use to get good predictive analyzes. Unfortunately, it is also bad news.
There is a new hot approach every day and it’s easy to get involved and get enthusiastic about a new method, said Angela Fontes, Head of Economic Analysis and Decision Making at the Norc Unit at the University of Chicago.
However, she notes that her experience is that the most successful projects are those who really think about what the desired outcome of the analysis is and allows them to control them in their choice of method – “even if it’s not the sexiest, newest method”.
It may seem obvious, but many projects with predictive analysis start with the goal of building a great model without a clear plan for how to use it.
There are a lot of amazing models that never got any because nobody knew how the information would be used to get any value, said Jason Verlen, Head of Product Management at CCC Information Services.
And Angela Fontes agrees by giving below statement
If we are not clear about our goals with the analysis, we can try to solve a problem just about anything and never really understand what we are looking for.
It is very important to establish a stable partnership between the business and the technical organization.
You must be able to understand how a new technology meets a business challenge or improve the existing business, says Paul Lasserre, Head of Product Management at AI at Genesys.
– Once the goal has been set, test the model in an application to a limited extent to determine whether the solution really gives value.
Models are designed by people so they are often not perfect. An incorrect model or an incorrectly or poorly selected data used will give misleading or completely incorrect predictions.
A narrow selection, for example, that is not sufficiently random, can stumble to the result. Imagine a hypothetical weight loss study, for example, where half of the participants do not follow the follow-up weight, but it is also true that those who do not come have completely different weight curves than those remaining. It complicates the analysis. Those who stay in the study are probably those who actually lose weight while those who leave it tend to be those who do not. If the study was conducted in a wide population, weight loss would be possible to predict – but not in a limited 50 percent drop-down database.
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