Automation is far beyond than a buzz-word. It has become the centre of our lives. Many of us subconsciously do not notice however we are looking for automation in many aspects in the things we do. Our vital daily life needs simplicity, thankfully complex business processes are being handled by automation and automated predictive engine.
Digitalisation changed the whole process of means operations & functions. Especially with the evolution of artificial intelligence (AI) and machine learning (ML) which has contributed an enormous amount to this process. When it comes to analytics and predictions, the importance of automated predictive engine is essential. They provide deep analysis of real-time and prediction about what might possibly happen in the future. In this blog article we will explain the basics of automated predictive engines
What is automated predictive engine?
It represents a complex software application that predicts, suggests, discovers and adapts according to the modelling. It can also be considered as the brain of prediction analytics. It collects data from multiple sources and organise the data in the optimum way for future analyses. The main goal is to start the analysis process and reach to a conclusion that will allow the possibility to take actions in advance
How do automated predictive engines work?
The working principal of the automated predictive engines is based on using models (can also be referred to as algorithms) to analyse the relationship between defined variables. This can be a relationship focusing on a performance, attributes or features. When the process is complete, the model estimates the likelihood of the relationship in comparison to a sample that has been designated earlier.
How are automated predictive engines used?
With the rise of automation, the means of processes has changed forever. From day-to-day tasks to intense and complex business processes, automation is widely accepted and benefited from.
From software applications and other platforms such as (Netflix, Amazon and CRM’s) to mobile phones, from less data-oriented business functions to data-swarmed processes, automated predictive engines are used heavily. Health, tourism, finance, logistics, heavy industries, renewable energy industries and many more industries are investing profoundly on it. It is fair to say that we are now experiencing a shift which is making businesses and industries dependent on it.
Benefits of automated predictive engines
As you can imagine from the heading, the main benefit is the prediction. The ability to understand and prevent things before events occur. Through this, users who use the software will be able to make critical decisions in critical moments. Moreover, it allows businesses and industries the ability to analyse their processes and organisation. With data coming from deep and precise analysis, it is possible to improve efficiency, forecast management, new product/process development which can possibly lead to innovation.
Automated ≠ automatic
The term ‘automated’ refers to a process that is being conducted by large automatic equipment. For the predictive engine it means a combination of automated processes and modelling. Periodic check-ups and improvements are needed, which means human interaction. Experts spend a huge amount of time modelling the predictive engine as it can be modelled and tailor-made for many occasions. As it is a complex and sensitive process and usually experienced data scientists are involved in the process by monitoring the algorithms.
What is artificial intelligence (AI)?
Artificial intelligence is referred to machines that are able to perform “cognitive” functions that humans and animals are naturally capable of. In other words, enabling machines to have a brain and a mind. Machines that are equipped with artificial intelligence are capable of functioning like humans such as learning, problem solving, analysis, adaptation, self-learning etc.
What makes artificial intelligence more valuable is the ability to analyse deeper data with accuracy allowing the users to have a better understanding of intense data being processed, also known as big data.
What is machine learning (ML)?
The process that studies computer algorithms which aims to improve automation through experience and history of data usage. It is being accepted as a component of artificial intelligence. The working mentality is based on having a sample data that is being compared to the results which will be acquired in the future in order to make assumptions and predictions.
It is important to highlight that artificial intelligence and machine learning do not represent the same thing. They are two different functions that benefit and complete each other.
Approaches to predictive engine modelling
There are various modelling versions available for predictive engines. We outline the important and wide accepted ones. It is mainly divided into regression techniques and machine learning techniques.
Regression models are the backbone of predictive engines. The main goal is to model and represent the correlation(s) between at least two different variables that are in consideration. The variety of regression techniques is wide. However, not all the techniques are applied at once. Depending on the situation the number can change. Linear regression model, discrete choice model, profit regression, logistic regression, time series models, multinomial logistic regression, multivariate adaptive regression, survival or duration analyses are among the most used ones.
Machine learning techniques are is a processes in which that various statistical methods for regression and classification is are applied to find/define a wide variety of data. Thus, specific practises of machine learning such as deep learning, reinforced learning and forecasting are important functions. The purpose of those specific practises is focused on reinforcing the learning throughout the historical collected data and maximising the cumulative result that supports the precision of the models.
ANNEA & automated predictive engine
At ANNEA, we start the whole process by acquiring and analysing the data through IoT, digital twin, edge computing and automated predictions. At the end of the process, we visualise the data in the ANNEA dashboard.
Automated predictive engines help us to further analyse the data, optimise and adapt to various situations and most importantly understand the current and predict future events. The automated predictive engines at ANNEA are equipped with cutting-edge technology and are consistently being checked for possible improvements to provide a seamless process.
Our team consists of data scientists engineers and machine learning specialists, who have expertise in predictive models/algorithms, artificial intelligence and machine learning, fully focusing on the extraction and representation of optimum and valuable data. Get in touch with ANNEA experts to learn more!
What is next?
We are close to bringing you to the end of a holistic process. Next topic will focus on how to deliver and visualise the final version of the data being analysed and process through our next generation platform: The ANNEA Dashboard.
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