Understanding the Difference among Detection and Forecasting Models, and Predictive Analytics and Leveraging Them in Business.

We do not need a soothsayer to realize how Artificial Intelligence (AI) has transformed our lives. From using machine learning for drug discovery to facial unlock ID using facial recognition, its' application is everywhere. While AI may not say what the next reading on a dice (or magic 8) ball can be, it surely can predict the probability of getting 6 in the next roll of dice. The predictive aspect of AI has become more refined and accurate with time, thanks to deep learning and data analytics. However, the question is, can Artificial Intelligence do more than just prediction like forecasting or detection of a trend?

Understanding the differences

While detection and forecasting may sound similar to predictive analytics or simply prediction, they are different. Detection refers to mining insights or information in a data pool when it is being processed. This can be the detection of objects, fraudulent behaviors, and practices, anomalies, etc. Whereas, forecasting is a process of predicting or estimating future events based on past and present data and most commonly by analysis of trends or data patterns. Unlike predictions, it is not vague and is defined by logic. Prediction or predictive analysis employs probability based on the data analyses and processing. Out of the three, it is the more uncertain, complicated, and expensive process.

How can they help Business?

Detection Vs. Prediction

A paper published by MIT states how detection can help businesses via a smoke detector-crystal ball analogy. Here, smoke detector and crystal ball are metaphorically examples of how detection and prediction work. Smoke detectors issue warning signals of an impending fire hazard. They don't predict the possibility of a fire accident. Based on early warning, we are presented options: whether to extinguish the fire/smoke source or escape the scene.

Similarly, businesses can benefit from detecting issues quickly, even if they are unpredicted. By leveraging detection algorithms of AI, companies always have the chance to act and manage outcomes and other functions even when they might have missed the opportunity to prevent any shortcomings or bottlenecks. Detection always encourages action using multiple solutions. Further, it is always definite as they offer some value, unlike the uncertainty offset of predictive analytics. This can help to boost ROI at minimal costs. One use case is, instead of trying to predict which customers will churn, managers, can shift to detect better which customers are dissatisfied. The implications may be similar, but changes in satisfaction are measurable while customers who were going to leave but didn't are.

Also, detection models can be used in every stage of the business pipeline, just like smoke detectors in every flat in an apartment. They help us to make sense of the activities and business insights. These can be identifying where data signals are currently missing. Where data signals have poor quality? Where are data signals giving false alarms causing system fatigue? All these go in the long run in enlightening ways to augment and enhance the productivity channels.

Forecasting vs. Prediction

Coming to forecasting, Business leveraging Artificial Intelligence-based forecasting models, can figure out trends that shall dominate the market in the coming days. Forecasting relies on the input of base data to arrive at an outcome. The quality of this data affects the results, unlike prediction or predictive models that have no separate input or output variable. Typically, forecasting is all about the numbers and using level and trend and seasonality observations to predict outcomes; predictive analytics is more about understanding consumer behavior. Even though forecasting is considered as projective of predictive models, the former is based on temporal information. It is scientific and free from intuition and personal bias, whereas prediction is subjective, arbitrary, and fatalistic by nature. This is why we have weather forecast instead of weather prediction. We need to strike a balance when employing these algorithms in Business. For, e.g., forecasting can help in marketing and promotional planning, but predictions can help estimate sales for targeting customers.

Outlook:

The bottom line is that businesses need to understand the key differences and use cases of predictive analytics, detection algorithms, and forecasting models of Artificial Intelligence. Then they can employ them as per their requirement to achieve brand goals.