An examination of the analytics model and transformation from traditional analytics to AI analytics

The field of analytics has witnessed a paradigm shift and transformation in the last few years. Credit goes to the techniques of artificial intelligence and machine learning that have brought about this change and transformation. What used to be data analytics in the past has now become AI Analytics. But AI Analytics is not a mere integration of data analytics with artificial intelligence. It is much more than that. In this article, we analyze the various aspects of analytics and examine its transformation over the years. We also examine the role of analytics training institutions that have helped in taking the field of analytics to a different level.

The art of analytics

Analytics is the process that helps in the interpretation of large data sets and deriving meaningful insights from them. Analytics finds its applications in a large number of sectors as well as different business operations. With the help of analytics, business decisions can be supported with meaningful information and quantitative facts. In addition to this, analytics also helps us to establish relationships between various variables that affect business operations. There are three main disciplines used in the process of analytics. The first one has applied mathematics, the second one is statistical analysis and the third one is artificial intelligence. It needs to be noted at this point in time that artificial intelligence helps in the process of analytics with the help of machine learning algorithms.

The analytical model: Analyzing the four important stages

There are four important stages in the process of analytics. When data is processed through all these stages, it is said to have been optimized with the help of the analytical model. The first important step is called descriptive analytics. This step is related to the process of hindsight and answers the questions based on previous facts. The second and the third stage are used for deriving insights. The second stage is called the diagnostic analysis stage and answers the question by explaining the reason behind it. The third stage is called the stage of predictive analytics and is used to forecast future happenings. The fourth stage is called prescriptive analytics and is dependent upon the information derived from the stage of predictive analytics. The stage of prescriptive analytics gives us a roadmap that we need to follow to lead the operations in a particular direction. The stage of prescriptive analytics is used for foresight and course correction.

Analytics training institutions have transformed traditional analytics into AI analytics

There are numerous ways of deriving analytics. Most of the ways depend upon data mining and data processing tools. However, when we use machine learning techniques to derive insights and uncover the relationship between various data sets, we call this analytics AI analytics. Analytics not only automates the process of deriving insights from voluminous amounts of data but also decreases the time required for processing and visualization. With the help of artificial intelligence, analytics increases the processing capabilities in terms of both speeds as well as the scale of the information that we operate with.

Let us now understand how AI Analytics improves upon the traditional analytical capabilities.

Traditional analytics operates in a diagnostic manner to analyze the causes of the slowdown of a business in the recent past. The hypotheses that are formed using traditional analytics take input from a smaller sample and the results are then generalized to support a particular course of action.

However, AI analytics collects information from a large number of sources and establishes its validity before further processing. Since analytics uses a wide data set, the findings are more authentic as well as concrete in nature. The probability of error while suggesting a particular course of action is also minimum.

Moreover, the techniques of statistical modeling and probability that played a great role in analytics methodology in the past are now getting automated. Advanced AI Analytics is paving the way for a scale and speed of analytics that was not possible before. In addition to this, the accuracy of AI Analytics has proved that it is one of the best techniques to derive insights and guide our business operations.

The way ahead

In the coming times, AI Analytics would help businesses in three primary modes. Firstly, it would help in demand forecasting. Secondly, it would help in predictive maintenance with the help of machine learning techniques. Thirdly, it would help in monitoring business operations with the help of diagnostic analytics. This would pave the way for sustained business growth amid changing market dynamics in the future.