Forecasting and predictive analysis aren't the same things: Here is why

Data analytics and Business intelligence have taken our modern business world by storm; the true value of data has now been viewed as an indispensable asset that can help enterprises attain their business goals. This data is used as a resource to predict trends and foresee opportunities ahead of time. The two words,?Forecast?and?Predict,?are synonymously used in our Standard English language, which according to dictionaries, is correct. However, in the analytical world, their meaning differs to a great extent.

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Forecasting and Predictive analysis have two very different activities behind them, so companies often buy the wrong analytical tool when they need the other. Hence, in this article, we will distinguish in extreme detail why these words aren't the same in an analytical context.

1. Macros vs. Micro-level

Forecasting analysis gets required within large populations on a longer timeline, month, or year. In contrast, the Predictive analysis focuses on individual behavior, which happens in real-time, and often deals with a shorter timeline. Depending on their need, enterprises might require both of these analyses, although Forecasting helps them plan strategically and provides guidance in investing. On the other hand, Predicting analysis watches over customer behavior, forming a clear picture of the user's anticipated needs.

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2. Advanced analytical tools

There are two very different tools used for Forecasting and Predictive analysis. Within Forecasting analysis, using DAX (Excel-style formulas) in Power BI and Power Pivot calculates the data and arrives at an intelligent guess. Other than that, with the help of the Power BI tool, one can decompose trends into sub-tends and calculates each sub-trend separately, only to combine them later to give a more accurate trend forecast.

?Whereas in Predictive analysis, DAX has little role to play, and it heavily depends on Machine learning, AI, and R technologies of today, to come up with automated predictions.

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3. Human involvement

Forecasting analysis does require human input and insights, and when combined with the computational power of calculations and Biz knowledge, the answers seem to weigh higher. Due to higher risk, it is not something we can hand over to machines to take those crucial business decisions; however, it helps a lot in strategic planning.

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On the other hand, there is absolutely no involvement of humans in Predictive analysis, and that's what machine learning is built for. If it gets wrong in its prediction, it automatically changes its course to produce a more accurate picture of each customer's behavior.

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4. Chances of Mistakes

We never know what the future will be like, and because of this uncertainty, the chances of mistakes are higher. In Predictive analysis, detailed information is required of each individual, and every time it makes mistakes in its predictions, it gets better each time in generating a clearer view of the end-user. Whereas in the Forecast, you only get to make a mistake once and that is it.

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5. Explainability

Forecast needs to be explainable despite calculations and smart formulas, as the chances of getting it wrong won't be commendable. That's why human involvement is a must. Within the Predictive analysis, predictions aren't explainable, and one of the reasons we humans need them is because it precisely gives real-time information on each customer's behavior.

Conclusion

?The two words Forecast and Predict might be synonyms in common layman language, but in the analytical world, their meaning and activities behind them differ immensely. Forecasting is at the macro level with a longer timeline, whereas Predictive deals with the micro-level, with a shorter timeline, often deal with real-time information. In the business context, companies might need both of them since Forecasting helps them with investment and strategic planning and Predictive analysis gives them an accurate picture of their customer behavior which can help detect trend lines.

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Our discussion ends here; we hope you have a precise picture of what these two words mean in analytics and help you decide which one you would need for your business planning.