Forecasting and predictive analysis are terms that are often used synonymously, but when seen in scientific perspective, these are rather different terms. A logical and defendable prediction that uses the data from the past and present, analyses the trends, and gives an estimation of the future trends and events is called forecasting. Whereas, on the other hand, predictive analytics or predictive modeling uses existing data sets, data mining, and forecasting probabilities to determine the more specific outcomes.
Take any social media platform (e.g., Twitter). Forecast and predictive analytics will use any predictive analytics platform to answer a wide range of questions. Forecasting alone will help us answer questions such as:
- How much annual revenue will be generated by the end of a certain year (if you have the data of the revenues from past years)?
- How many people will be active users of this platform three years from now on?
- What percentage of the total budget has to be spent on server hardware in the next six months?
Whereas predictive analytics help explains questions such as:
- Which advertisement should the predictive algorithm show to a certain user at a certain time?
- Should the advertisements be changed when the mode of signing into the platform is different?
- What are the percentage chances that the user will click open that certain ad?
Forecasting vs Predictive Analytics, the difference:
Forecast and predictive analytics may sound familiar as both derive information from the past and present data to extract future trends. But predictive analytics uses modern algorithms, artificial intelligence, and automated machine learning techniques to devise sufficient data that can help identify specific outcomes for the future. A model is prepared from the information that is collected from the past and present and the model is then applied to the current information for which the possible patterns and outcomes are to be found.
Role of AI in augmentation of predictive analytics:
Artificial intelligence is the type of computer science that has the ability to use human intelligence like speech recognition, decision making, and extracting conclusions and is applied in a wide range of fields to help extract information and outcomes from the data given.
It is no wonder that today, AI has revolutionized so many fields including healthcare and medicine, industry, business, economics, etc. Today, with the increased demand for AI, techniques like forecast and predictive analytics make good use of artificial intelligence to extract the outputs in different fields. AI helps strengthen predictive analytics through:
- Decision-making that involves human factors: More often, the collection of data is influenced by human factors such as culture, beliefs, religion, and intuition, hence the extraction of information becomes rather difficult. AI, using a modern predictive analytics platform incorporates these factors while withdrawing conclusions that help us shape specific patterns for the future.
- In-depth analysis: AI helps forecast and predictive analytics through detailed analyses because its modernized algorithms make use of neural networks and help leverage huge businesses and companies.
- Handling a large amount of data efficiently: AI can extract information more easily from a large amount of data. We can consider data to be a portion of food for AI. More the data, the more is the efficiency of AI.
- Improving data governance: Increased data demands more scrutiny, proofreading, integration, and security. With the increase in information and data, it becomes more and more difficult to meet such demands. Having AI as a mode of augmenting predictive analytics helps govern such factors and makes it easier for scientists to predict outcomes and increase productivity.
- Detection of anomalies: Banks often use a predictive analytics platform to detect anomalies in transactions and prevent money laundering. Similarly, threats in other systems can be easily detected through the augmentation of AI in the forecast and predictive analytics.
How do forecast and predictive analytics offer new possibilities?
Business planning:
Forecast and predictive analytics help companies map out the business plans for the whole year by constructing their outcomes on the basis of a model built on previous data. For example; a forecast will help a company predict the gross sales of the coming season on the basis of the revenue generated from last season. Predictive analytics, on the other hand, will help the company predict its customers for the season by comparing the demographics with the gross sales.
Improved decision making:
Predictive analytics, especially when augmented with AI and machine learning, aids the companies with improved decision making by using all kinds of modes for business purchases and increasing their leverage by customizing new strategies.
Understanding customer behavior:
Forecast and predictive analytics help understand customer behavior at both micro and macro levels, respectively. By understanding the preferences of your customer, you can form better strategies to optimize the outcomes and prevent the possible risk factors. Predictive analytics also helps to understand the customers on an individual level so that the satisfactory levels of the customer can be reached.
Reduction in labor turnover rate:
More often than none, a company will use a predictive analytics platform to identify the connections between factors such as:
- Salary of the employee
- Promotion
- Commute distance
- Work environment
- Relationship with other workers and boss
Forecast and predictive analytics will devise evidence from all these factors and extract the satisfactory levels of the employees through predictive analytics and statistical modeling and help the company come up with solutions that can help reduce labor turnover over a certain period of time.
Fraud detection:
The majority of the companies are gradually incorporating forecast and predictive analytics to help detect suspicious and fraudulent activities.
Implement Artificial Intelligence at work to meet the changing expectations of teams around the world
Prioritizing high-risk patients:
Forecast and predictive analytics, through the application, if AI helps the healthcare systems to prioritize patients on the basis of the severity of their illnesses. This prevents the overcrowding and readmissions of patients and helps reduce clinical burnout
Prioritizing high-risk patients:
Forecast and predictive analytics, through the application, if AI helps the healthcare systems to prioritize patients on the basis of the severity of their illnesses. This prevents the overcrowding and readmissions of patients and helps reduce clinical burnout
Insurance industry:
Several models of predictive analytics are used in the insurance industry:
- Forecast models
- Classification models
- Outlier models
- Clustering models
Forecast and predictive analytics help the insurance industry in the following ways:
- Risk assessment: It uses the past criminal record data, and social media handles of the concerned person and uses this information to help the insurance company form proper pricing for its customer.
- Simplifying the claim processes: Predictive analytics help reduce the costs and time of claim processes by helping the insurers to predicts the events beforehand and prioritize their claims so maximum customary satisfaction could be achieved,
- Increasing the efficiency of the system: Forecast and predictive analytics help increase the overall efficiency of the whole insurance system and help its survival in tough climates by utilizing newer technologies, machine learning, AI, and deep learning.
- Finding potential customers: In order for any business to survive and stay in the market, it is of fundamental value to find the target audience, study their demographics in detail and provide the offers that best suit their condition.
Improving the merit system:
Predictive analytics helps gather information about the past work performances of the candidates applying for a job. This information is then extrapolated to predict the future work performances of the same candidates, hence in this way, people with the most outstanding work ethics get the job.
Augmenting human capital:
Predictive analytics help improve the quality of a person’s economic worth and a worker’s experience and skills, hence carefully managing the human capital.
Reduction of workplace injuries:
Factories and manufacturing companies can use forecast and predictive analytics to reduce workplace injuries. Injuries of the past, their causes, and connecting factors are extracted from the data and maximum protective measures are taken in order to prevent the same injuries from happening in the future.
Replacement of outdated security systems with newer ones:
All the old safety procedures were built upon static data. But since forecast and predictive analytics give factories access to real-time data, it is now possible to ensure the safety of factory workers. This is achieved through sensors that are built throughout the factory. Information from these sensors is fed into predictive analytics models and the outcomes from the model are then devised to build new safety measures.
Improving healthcare:
Forecast and predictive analytics help improve patient outcomes and relieve the healthcare workers by augmenting decision making for them. Prioritizing patients on the basis of the severity of the illness is now done easily through predictive analytics platforms. The vulnerability of a specific patient or the whole region to a specific disease can be estimated by extrapolating the data from the history of that disease. Hence, preventive measures are taken beforehand.
Predictive maintenance:
Predictive maintenance helps estimate the time when equipment might require maintenance. Equipment breakdowns always happen suddenly, hence it is important to detect the early signs and order maintenance as early as possible. Predictive analytics help do this at low costs with the best possible outcomes. The small early changes are detected through the data that is continuously fed into sensors all over the working domain of that equipment. This continuous monitoring helps provide timely insights into the working of the equipment and gives an idea of when it needs maintenance.
Increased annual gross revenues:
It is estimated that by the end of 2022, the global predictive analytics market would be valued at 10.95 billion dollars. Such large revenues owing to the easy accessibility of these techniques
Recommendation of content in the entertainment industry:
Take Apple TV or Netflix. We often see the option of “Based on your likes” or “Recommended TV shows”. Now how does the server know what movies we would like to watch? The answer again lies in predictive analytics. The algorithms are designed in such a way that they track record the type of content we are recently taking interest in and so, it recommends the same genre the next time we log into such apps. This not only enhances the efficacy but helps achieve the maximum satisfaction level of the user.
Virtual Assistants:
Some of the very important virtual assistants like Alexa and Siri use the power of deep learning and statistical analysis in addition to predictive analytics. These virtual assistants have a self-learning behavior. They extract information from the user’s experiences and interactions and use this to give accurate responses in the future.
Ease for retailers:
Retail companies have to handle a large amount of data to compare and contrast past sales and plan out the purchases for the future. Maximum optimization of the operation takes place in the following ways:
- Sales data from customers help provide personalized recommendations for them through forecast and predictive analytics.
- Predictive analytics help time companies to take steps like raising prices. Customer sales data, when incorporated into a predictive analytics platform, help in the timing of events.
- It also helps in the planning of future marketing campaigns.
- predictive analytics make sure that a specific product is available for customer consumption at a specific time.
Oil and Gas companies:
Oil and gas companies have been making use of forecast and predictive analytics since the early 90s. In the light of the recent downhole conditions of these companies, most of them have recently made huge investments in predictive analytics software to maximize their resources, enhance their efficacy, optimize their production and reduce downtime.
Following are some of the important applications of predictive analytics in oil and gas companies:
- Equipment efficiency.
- Thermal gradients for a drill operation.
- Tracking the performance of oil wells.
- Determine downhole conditions before time.
- Analysis of the factors responsible for equipment degradation.
- To predict capacity bottlenecks in terms of human resources and the total equipment.
Optimization of marketing campaigns:
Marketing campaigns and promotional events require predictive analytics in order to plan their strategies and attract maximum customer attraction. Patterns are identified, recorded, and analyzed through AI software and put into use for publicizing.
Sports:
Almost all sports today, ranging from the famous American football to cricket and badminton, make use of forecast and predictive analytics to:
- Predict the performance of a team
- Forecast the figures such as strike rates of individual players.
- Predict the results of a match beforehand on the basis of past win loss ratio and overall team performances:
Predictive analytics platforms:
Data scientists, developers, and analysts use various predictive analytics platforms to analyze the data from the past, use statistical modeling and AI algorithms to build models, and implement these models for future predictions. Potential risks and outstanding opportunities for a company can be assessed by using any such predictive analytics platform.
Some of the top predictive analytics platforms that are most considered by the companies worldwide for the year 2022 include IBM Watson Studio, RapidMiner Studio, and SAS.
Challenges of forecast and predictive analytics:
No wonder, forecast, and predictive analytics have revolutionized many domains of life but at the same time due to the complexity of its nature and difficulty in its application in a traditional manner, the following challenges are often faced:
- Long checklist: Before applying this procedure through any predictive analytics platform, there is a long series of steps that need to be checked. This often gets burdening for the data scientist. The whole checklist consists of around 13-15 steps, some of which include:
- Preparation of data
- Understanding different algorithms
- Choosing the right algorithm
- Choosing the right data format
- Understanding the output of a certain algorithm
- Predicting in real-time
- Imbalanced data dealing
- Time-consuming process: Since this technology is rather new in many fields, hence practitioners of the respective fields find it very hard, and learning the technique is rather time-consuming for them. Hence, fewer and fewer people are inclined to adopt the new methods.
- Demand for data scientists: new techniques demand expertise and since forecast and predictive analytics demand large amounts of data input and checking, there is a high demand for data scientists for this purpose. Data scientists incorporate the predictive algorithms into systems and help construct predictive analytics models that can be used by the industry to extract further results.
Addressing these challenges:
- Incorporate predictive analytics in common applications where most of the user consumption takes place rather than using it as a separate tool.
- Advanced use of AI and machine modeling in addition to predictive analytics.
- Simplifying the technique so no expert data scientist is needed for its application.
Predictive analytics platforms:
Data scientists, developers, and analysts use various predictive analytics platforms to analyze the data from the past, use statistical modeling and AI algorithms to build models, and implement these models for future predictions. Potential risks and outstanding opportunities for a company can be assessed by using any such predictive analytics platform.
Some of the top predictive analytics platforms that are most considered by the companies worldwide for the year 2022 include IBM Watson Studio, RapidMiner Studio, and SAS.