Why should you perform periodic predictive analysis?
While periodic predictive analytics is a discussed topic much today, in itself, it is quite an old concept. Mostly different now is that we can count on exponentially greater computing power and data volumes compared to then.
To understand the subject better, in the article we explain what predictive analysis is, how it works and why it is important. We also show you advantages offered, its relation to other technologies and application examples so that you understand why you should perform this kind of analysis.
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What is predictive analytics?
Predictive analytics — or predictive technical analytics — is a way of performing advanced analysis to check data or content in order to answer the question: what might happen in the future?
To do this, statistical modeling techniques, Big Data and machine learning are used, which allow historical data to be extracted and predictions to be made.
This predictability is possible due to the capacity of Big Data, which obtains data through several interconnected systems. They can be interpreted to verify how a group or a person will behave in relation to a certain context.
How does it work?
This predictive model is nothing more than a mathematical function, which performs a complex statistical calculation to present possibilities to a manager.
In this sense, a retail company, for example, can use a wide variety of data as a basis to understand that demand for a product may increase at a certain time of the year.
Based on this prediction indicated by the algorithm, decision-makers can understand that it is necessary to reinforce stock to meet demand, avoiding being caught by surprise.
How important is predictive analytics?
In an economy with fierce competition, the use of this tool becomes an important differentiator. After all, who wouldn’t want to be more certain about the likely outcomes of a decision?
In this regard, the value of predictive analysis lies in the very prevention of the events themselves based on the trends, traceable from similar circumstances encountered in the past.
What are the benefits of predictive analytics?
What is the most significant benefit of predictive analysis? It is that it enables companies to learn from their experiences – from their data – and to take effective measures to apply what has been learned toward better futures.
Below, see other important advantages:
- eliminates the burden of manual data analysis and minimizes errors;
- generates competitive advantage in the market;
- improves customer satisfaction;
- increases the chances of successful product launches.
How does predictive analytics relate to other technologies?
To perform predictive analysis, the company needs to keep in mind that it is a process that requires several other enabling technologies. Below, learn about the main ones.
Predictive Analytics and Artificial Intelligence
Here, we have two terms that are similar and closely related. This is because Artificial Intelligence is the fuel of predictive analysis, since this method considers not only historical data, but also seeks to predict various future possibilities.
To do this, you need applications capable of feeding the algorithms with external data collected in real time, to find patterns, behaviors and design future scenarios.
Predictive Analytics and Big Data
Big Data is the backbone framework under which data-gathering applications are implemented. It will be the raw material from which algorithms and models will be built. Hence, good interfaces to Big Data must be important in effective predictive analytics.
Predictive Analytics and Business Intelligence
Business Intelligence is the process of gathering, storing, and analyzing business data to extract insights that help drive better decision-making.
Therefore, when a company performs a predictive analysis, it is applying a BI action. With this, it obtains proposals for executable actions that enable better solutions.
What are the 3 Vs of predictive analytics?
Predictive analytics relies on Big Data. This technology is based on five Vs, identified as volume, variety, veracity, velocity, and value. Three of them are fundamental to the success of predictive analytics — as we will show below.
Variety
It is very important to have a good diversity of data sources and formats, to obtain a deeper analysis. In addition, this aspect helps to obtain less “biased” results — often caused by a single database.
Truthfulness
Veracity is an essential aspect: there is no point in having a large volume of data if the information is not reliable. Therefore, before carrying out any type of analysis, it is important to question whether the source of such data is reliable.
Speed
Just as important as having reliable and diverse data is having the agility to process it. This is because many of the insights may no longer be useful if the timing is lost.
In this sense, a good platform needs to have the ability to cross-reference information collected in real time, to generate accurate predictions based on the analyses.
What are examples of the application of predictive analytics?
Predictive analysis is part of the routine of large companies in a wide range of segments. Below, see some examples of its application.
Churn prediction
Predictive analysis can predict when certain customers are no longer satisfied with the solutions offered. This way, the company can plan better , based on a review of its weaknesses.
This allows you to develop new retention strategies or, in difficult scenarios, at least better prepare for customer loss.
Upsell and cross-sell
In contrast to churn prediction, in this aspect, the company can perceive the customer’s willingness to be interested in a new product.
This way, it is possible to approach it more precisely to offer a more advantageous upgrade for the customer and more profitable for the company.
Agribusiness
One of the biggest challenges that this technology allows us to overcome is knowledge about the climate and the conditions that impact planting.
Based on historical data and the help of advanced algorithms, we are able to predict events and receive insights on measures to overcome them, making the production chain more flexible to survive climate change.
The information also increases business leaders’ visibility into the level of waste and losses, among other aspects.
The use of field data and the automation of agricultural equipment make it possible to carry out rural activities more effectively and on a broader scale. An example of this is an autonomous tractor, which can receive weather forecast data.
Using this information, the equipment identifies when it can carry out its work in better weather conditions. Therefore, if the weather worsens, it can stop automatically and continue the interrupted task as soon as the situation changes.
In this way, agricultural management can incorporate predictive analysis, with a focus on the evolution of cold chain chains, to avoid losses , increase productivity and get ahead of the competition in terms of traceability, quality and reliability.
